This list of references in automatically generated from a collection of BibTeX files organized in a way that tries to avoid redundancy, minimise mistakes and facilitate customization.
You only need to fork (or link) the git repository in your papers and sync with the main copy to send/receive updates.
Most customisations, such as shorter journal or conference names, do not require changing the existing .bib files.
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@comment{{This file has been generated by bib2bib 1.99}}
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@article{AbdGad2012dynamic,
author = {Abdelkhalik, Ossama and Gad, Ahmed},
title = {Dynamic-Size Multiple Populations Genetic Algorithm for Multigravity-Assist Trajectory Optimization},
journal = {Journal of Guidance, Control, and Dynamics},
year = 2012,
volume = 35,
number = 2,
pages = {520--529},
doi = {10.2514/1.54330}
}
@article{AbrAmoDan1999,
title = {Simulated annealing cooling schedules for the school timetabling problem},
author = { David Abramson and Amoorthy, Mohan Krishna and Dang, Henry},
journal = {Asia-Pacific Journal of Operational Research},
volume = 16,
number = 1,
pages = {1--22},
year = 1999
}
@article{Abramson1991,
title = {Constructing School Timetables Using Simulated Annealing: Sequential and Parallel Algorithms},
author = { David Abramson },
journal = {Management Science},
volume = 37,
number = 1,
pages = {98--113},
year = 1991,
publisher = {{INFORMS}}
}
@article{Ach2009mpc,
author = {Tobias Achterberg},
title = {{SCIP}: {Solving} constraint integer programs},
journal = {Mathematical Programming Computation},
year = 2009,
volume = 1,
number = 1,
month = jul,
pages = {1--41},
epub = {http://mpc.zib.de/archive/2009/1/Achterberg2009_Article_SCIPSolvingConstraintIntegerPr.pdf}
}
@article{AchBer2007,
title = {Improving the feasibility pump},
author = {Achterberg, Tobias and Berthold, Timo},
journal = {Discrete Optimization},
volume = 4,
number = 1,
pages = {77--86},
year = 2007,
publisher = {Elsevier}
}
@article{AcoMes2014jbi,
author = {H{\'e}ctor-Gabriel Acosta-Mesa and Fernando Rechy-Ram{\'i}rez and Efr{\'e}n Mezura-Montes and Nicandro Cruz-Ram{\'i}rez and
Hern{\'a}ndez Jim{\'e}nez, Rodolfo},
title = {Application of time series discretization using evolutionary programming for classification of precancerous cervical lesions},
journal = {Journal of Biomedical Informatics},
volume = 49,
pages = {73--83},
year = 2014,
doi = {10.1016/j.jbi.2014.03.004},
keywords = {irace}
}
@article{AddLocSch2008,
title = {Disk Packing in a Square: A New Global Optimization Approach},
author = {Addis, Bernardetta and Locatelli, Marco and Schoen, Fabio},
journal = {INFORMS Journal on Computing},
year = 2008,
number = 4,
pages = {516--524},
volume = 20,
doi = {10.1287/ijoc.1080.0263}
}
@article{Ade92,
author = {B. Adenso-D{\'i}az},
title = {Restricted Neighborhood in the Tabu Search for the
Flowshop Problem},
journal = {European Journal of Operational Research},
year = 1992,
volume = 62,
number = 1,
pages = {27--37}
}
@article{AdeLag06tuning,
author = {B. Adenso-D{\'i}az and Manuel Laguna },
title = {Fine-Tuning of Algorithms Using Fractional
Experimental Design and Local Search},
journal = {Operations Research},
year = 2006,
volume = 54,
number = 1,
pages = {99--114},
keywords = {Calibra}
}
@article{AdrBieSha2022jair,
title = {Automated dynamic algorithm configuration},
author = { Steven Adriaensen and Biedenkapp, Andr{\'e} and Shala, Gresa and Awad, Noor and Eimer, Theresa and Marius Thomas Lindauer and Frank Hutter },
journal = {Journal of Artificial Intelligence Research},
volume = 75,
pages = {1633--1699},
year = 2022,
doi = {10.1613/jair.1.13922}
}
@article{AfsMieRui2021survey,
author = {Afsar, Bekir and Kaisa Miettinen and Francisco Ruiz },
title = {Assessing the Performance of Interactive Multiobjective
Optimization Methods: A Survey},
year = 2021,
volume = 54,
number = 4,
doi = {10.1145/3448301},
abstract = {Interactive methods are useful decision-making tools for
multiobjective optimization problems, because they allow a
decision-maker to provide her/his preference information
iteratively in a comfortable way at the same time as (s)he
learns about all different aspects of the problem. A wide
variety of interactive methods is nowadays available, and
they differ from each other in both technical aspects and
type of preference information employed. Therefore, assessing
the performance of interactive methods can help users to
choose the most appropriate one for a given problem. This is
a challenging task, which has been tackled from different
perspectives in the published literature. We present a
bibliographic survey of papers where interactive
multiobjective optimization methods have been assessed
(either individually or compared to other methods). Besides
other features, we collect information about the type of
decision-maker involved (utility or value functions,
artificial or human decision-maker), the type of preference
information provided, and aspects of interactive methods that
were somehow measured. Based on the survey and on our own
experiences, we identify a series of desirable properties of
interactive methods that we believe should be assessed.},
journal = {{ACM} Computing Surveys},
numpages = 27,
keywords = {decision-makers, Interactive methods, performance assessment,
preference information, multiobjective optimization problems}
}
@article{AfsSilMis2022design,
author = {Afsar, Bekir and Silvennoinen, Johanna and Misitano,
Giovanni and Francisco Ruiz and Ruiz, Ana B. and Kaisa Miettinen },
title = {Designing empirical experiments to compare interactive
multiobjective optimization methods},
journal = {Journal of the Operational Research Society},
year = 2022,
volume = 74,
number = 11,
pages = {2327--2338},
month = nov,
doi = {10.1080/01605682.2022.2141145}
}
@article{AgoPea1973normality,
doi = {10.2307/2335012},
year = 1973,
month = dec,
publisher = {{JSTOR}},
volume = 60,
number = 3,
pages = {613--622},
author = {Ralph {D'Agostino} and E. S. Pearson},
title = {Tests for Departure from Normality. Empirical Results for the
Distributions of $b_2$ and $\surd b_1$},
journal = {Biometrika}
}
@article{Agrell1997ejor,
title = {On redundancy in multi criteria decision making},
journal = {European Journal of Operational Research},
volume = 98,
number = 3,
pages = {571--586},
year = 1997,
doi = {10.1016/0377-2217(95)00340-1},
author = {Per J. Agrell},
keywords = {Multi criteria decision making, Redundancy, objective
reduction, Vector optimisation},
abstract = {The concept of redundancy is accepted in Operations Research
and Information Theory. In Linear Programming, a constraint
is said to be redundant if the feasible decision space is
identical with or without the constraint. In Information
Theory, redundancy is used as a measure of the stability
against noise in transmission. Analogies with Multi Criteria
Decision Making (MCDM) are indicated and it is argued that
the redundancy concept should be used as a regular feature in
conditioning and analysis of Multi Criteria
Programs. Properties of a proposed conflict-based
characterisation are stated and some existence results are
derived. Redundancy is here intended for interactive methods,
when the efficient set is progressively explored. A new
redundancy test for the linear case is formulated from the
framework. A probabilistic method based on correlation is
proposed and tested for the non-linear case. Finally, some
general guidelines are given concerning the redundancy
problem.}
}
@article{AguTan2007ejor,
title = {Working principles, behavior, and performance of {MOEAs} on
{MNK}-landscapes},
author = { Aguirre, Hern\'{a}n E. and Tanaka, Kiyoshi },
journal = {European Journal of Operational Research},
volume = 181,
number = 3,
year = 2007,
pages = {1670--1690},
doi = {10.1016/j.ejor.2006.08.004}
}
@article{AhmOsm2004:aor,
author = {Samad Ahmadi and Ibrahim H. Osman },
title = {Density Based Problem Space Search for the Capacitated Clustering $p$-Median Problem},
journal = {Annals of Operations Research},
year = 2004,
volume = 131,
pages = {21--43}
}
@article{AhrElsSarEss2021weighted,
title = {Weighted pointwise prediction method for dynamic
multiobjective optimization},
journal = {Information Sciences},
volume = 546,
pages = {349--367},
year = 2021,
author = {Ali Ahrari and Saber Elsayed and Ruhul Sarker and Daryl
Essam and Carlos A. {Coello Coello} }
}
@article{AhuErgOrlPun2002:dam,
author = { R. K. Ahuja and O. Ergun and A. P. Punnen},
title = {A Survey of Very Large-scale Neighborhood Search
Techniques},
journal = {Discrete Applied Mathematics},
year = 2002,
volume = 123,
number = {1--3},
pages = {75--102}
}
@article{AinKumCha2009asc,
author = { Sandip Aine and Rajeev Kumar and P. P. Chakrabarti },
title = {Adaptive parameter control of evolutionary
algorithms to improve quality-time trade-off},
journal = {Applied Soft Computing},
volume = 9,
number = 2,
year = 2009,
pages = {527--540},
doi = {10.1016/j.asoc.2008.07.001},
keywords = {anytime}
}
@article{AlbLanSte2010,
author = {Albrecht, A. A. and Lane, P. C. R. and Steinh{\"o}fel, K.},
title = {Analysis of Local Search Landscapes for k-{SAT} Instances},
journal = {Mathematics in Computer Science},
number = 4,
pages = {465--488},
volume = 3,
year = 2010,
doi = {10.1007/s11786-010-0040-7}
}
@article{Albers2003online,
title = {Online Algorithms: A Survey},
author = {Albers, Susanne},
journal = {Mathematical Programming},
year = 2003,
number = 1,
pages = {3--26},
volume = 97
}
@article{AleMos2016slr,
author = {Aldeida Aleti and Irene Moser},
year = 2016,
title = {A systematic literature review of adaptive parameter control
methods for evolutionary algorithms},
journal = {{ACM} Computing Surveys},
volume = 49,
number = {3, Article 56},
month = oct,
pages = 35,
doi = {10.1145/2996355}
}
@article{AlfRuiPagStu2020hybrid,
title = {Automatic Algorithm Design for Hybrid Flowshop Scheduling
Problems},
author = { Pedro Alfaro-Fern{\'a}ndez and Rub{\'e}n Ruiz and Federico Pagnozzi and Thomas St{\"u}tzle },
journal = {European Journal of Operational Research},
volume = 282,
number = 3,
pages = {835--845},
year = 2020,
doi = {10.1016/j.ejor.2019.10.004},
keywords = {Scheduling, Hybrid flowshop, Automatic algorithm
configuration, Automatic Algorithm Design},
abstract = {Industrial production scheduling problems are challenges that
researchers have been trying to solve for decades. Many
practical scheduling problems such as the hybrid flowshop are
NP-hard. As a result, researchers resort to metaheuristics to
obtain effective and efficient solutions. The traditional
design process of metaheuristics is mainly manual, often
metaphor-based, biased by previous experience and prone to
producing overly tailored methods that only work well on the
tested problems and objectives. In this paper, we use an
Automatic Algorithm Design (AAD) methodology to eliminate
these limitations. AAD is capable of composing algorithms
from components with minimal human intervention. We test the
proposed AAD for three different optimization objectives in
the hybrid flowshop. Comprehensive computational and
statistical testing demonstrates that automatically designed
algorithms outperform specifically tailored state-of-the-art
methods for the tested objectives in most cases.}
}
@article{AliMei2011kemeny,
annote = {Computational Foundations of Social Choice},
year = 2012,
month = jul,
publisher = {Elsevier {BV}},
volume = 64,
number = 1,
pages = {28--40},
author = {Alnur Ali and Marina Meil{\u{a}}},
doi = {10.1016/j.mathsocsci.2011.08.008},
journal = { Mathematical Social Science },
title = {Experiments with {Kemeny} ranking: What Works When?},
keywords = {Borda ranking, Kemeny ranking}
}
@article{AllAyd2013,
title = {Algorithms for no-wait flowshops with total
completion time subject to makespan},
author = {Allahverdi, Ali and Aydilek, Harun},
journal = {International Journal of Advanced Manufacturing Technology},
pages = {1--15},
year = 2013
}
@article{AllJasLieTam2022cor,
title = {What if we increase the number of objectives? {Theoretical}
and empirical implications for many-objective combinatorial
optimization},
author = { Allmendinger, Richard and Andrzej Jaszkiewicz and Arnaud Liefooghe and Tammer,
Christiane},
doi = {10.1016/j.cor.2022.105857},
journal = {Computers \& Operations Research},
volume = 145,
pages = 105857,
year = 2022,
publisher = {Elsevier}
}
@article{AllKno2013ephemeral,
title = {On Handling Ephemeral Resource Constraints in Evolutionary
Search},
author = { Allmendinger, Richard and Joshua D. Knowles },
year = 2013,
month = sep,
journal = {Evolutionary Computation},
volume = 21,
number = 3,
pages = {497--531},
issn = {1063-6560, 1530-9304},
doi = {10.1162/EVCO_a_00097},
abstract = {We consider optimization problems where the set of solutions
available for evaluation at any given time t during
optimization is some subset of the feasible space. This model
is appropriate to describe many closed-loop optimization
settings (i.e. where physical processes or experiments are
used to evaluate solutions) where, due to resource
limitations, it may be impossible to evaluate particular
solutions at particular times (despite the solutions being
part of the feasible space). We call the constraints
determining which solutions are non-evaluable ephemeral
resource constraints (ERCs). In this paper, we investigate
two specific types of ERC: one encodes periodic resource
availabilities, the other models `commitment' constraints
that make the evaluable part of the space a function of
earlier evaluations conducted. In an experimental study, both
types of constraint are seen to impact the performance of an
evolutionary algorithm significantly. To deal with the
effects of the ERCs, we propose and test five different
constrainthandling policies (adapted from those used to
handle `standard' constraints), using a number of different
test functions including a fitness landscape from a real
closed-loop problem. We show that knowing information about
the type of resource constraint in advance may be sufficient
to select an effective policy for dealing with it, even when
advance knowledge of the fitness landscape is limited.},
langid = {english}
}
@article{Alm10,
author = { Christian Almeder },
title = {A hybrid optimization approach for multi-level
capacitated lot-sizing problems},
number = 2,
journal = {European Journal of Operational Research},
year = 2010,
keywords = {Ant colony optimization, Manufacturing, Material
requirements planning, Mixed-integer programming},
pages = {599--606},
volume = 200,
doi = {10.1016/j.ejor.2009.01.019},
abstract = {Solving multi-level capacitated lot-sizing problems
is still a challenging task, in spite of increasing
computational power and faster algorithms. In this
paper a new approach combining an ant-based
algorithm with an exact solver for (mixed-integer)
linear programs is presented. A {MAX-MIN} ant system
is developed to determine the principal production
decisions, a {LP/MIP} solver is used to calculate
the corresponding production quantities and
inventory levels. Two different local search methods
and an improvement strategy based on reduced
mixed-integer problems are developed and integrated
into the ant algorithm. This hybrid approach
provides superior results for small and medium-sized
problems in comparison to the existing approaches in
the literature. For large-scale problems the
performance of this method is among the best}
}
@article{AluKat2004:ieee,
author = {S. Alupoaei and S. Katkoori},
title = {Ant Colony System Application to Marcocell Overlap Removal},
journal = {IEEE Transactions on Very Large Scale Integration (VLSI) Systems},
year = 2004,
volume = 12,
number = 10,
pages = {1118--1122}
}
@article{Amaral2012corridor,
title = {The Corridor Allocation Problem},
journal = {Computers \& Operations Research},
volume = 39,
number = 12,
pages = {3325--3330},
year = 2012,
author = {Amaral, Andr{\'e} R. S.},
doi = {10.1016/j.cor.2012.04.016},
keywords = {Facility layout, Double row layout, Integer programming},
abstract = {The corridor allocation problem (CAP) seeks an arrangement of
facilities along a central corridor defined by two horizontal
lines parallel to the x-axis of a Cartesian coordinate
system. The objective is to minimize the total communication
cost among facilities, while respecting two main conditions:
(i) no space is allowed between two adjacent facilities; (ii)
the left-most point of the arrangement on either line should
have zero abscissa. The conditions (i) and (ii) are required
in many applications such as the arrangement of rooms at
office buildings or hospitals. The CAP is a NP-Hard
problem. In this paper, a mixed-integer programming
formulation of the CAP is proposed, which allows us to
compute optimal layouts in reasonable time for problem
instances of moderate sizes. Moreover, heuristic procedures
are presented that can handle larger instances.}
}
@article{AmiBadFar2007:cis,
author = {Amir, C. and Badr, A. and Farag, I},
title = {A Fuzzy Logic Controller for Ant Algorithms},
journal = {Computing and Information Systems},
year = 2007,
volume = 11,
number = 2,
pages = {26--34}
}
@article{AndDefDouJor2003,
title = {An Introduction to {MCMC} for Machine Learning},
author = { Christophe Andrieu and Nando de Freitas and Arnaud Doucet and Michael I. Jordan },
journal = {Machine Learning},
volume = 50,
number = {1-2},
pages = {5--43},
year = 2003,
publisher = {Springer}
}
@article{AndFagHob2015maritime,
author = {Andersson, Henrik and Fagerholt, Kjetil and Hobbesland,
Kirsti},
title = {Integrated maritime fleet deployment and speed optimization:
Case study from {RoRo} shipping},
journal = {Computers \& Operations Research},
year = 2015,
volume = 55,
pages = {233--240},
month = mar,
doi = {10.1016/j.cor.2014.03.017}
}
@article{AndJorLin1996,
author = {Andersen, K. A. and J{\"o}rnsten, K. and Lind, M.},
title = {On bicriterion minimal spanning trees: An approximation},
journal = {Computers \& Operations Research},
volume = 23,
number = 12,
pages = {1171--1182},
year = 1996
}
@article{AnejaNair79,
author = {Aneja, Y. P. and Nair, K. P. K.},
title = {Bicriteria Transportation Problem},
journal = {Management Science},
volume = 25,
number = 1,
pages = {73--78},
year = 1979
}
@article{AngBamGou2004tcs,
author = {Eric Angel and Evripidis Bampis and Laurent
Gourv{\'e}s},
title = {Approximating the {Pareto} curve with local search
for the bicriteria {TSP}(1,2) problem},
journal = {Theoretical Computer Science},
number = {1-3},
pages = {135--146},
volume = 310,
year = 2004,
doi = {10.1016/S0304-3975(03)00376-1},
keywords = {Archiving, Local search, Multicriteria TSP,
Approximation algorithms}
}
@article{AngWoo09,
author = { Daniel Angus and Clinton Woodward},
title = {Multiple Objective Ant Colony Optimisation},
journal = {Swarm Intelligence},
year = 2009,
volume = 3,
number = 1,
pages = {69--85},
doi = {10.1007/s11721-008-0022-4}
}
@article{AnjVie2017flp,
title = {Mathematical optimization approaches for facility layout
problems: The state-of-the-art and future research
directions},
author = {Anjos, Miguel F. and Vieira, Manuel V. C.},
journal = {European Journal of Operational Research},
volume = 261,
number = 1,
pages = {1--16},
year = 2017,
publisher = {Elsevier}
}
@article{AnsBriGou2002qap,
author = {Kurt Anstreicher and Nathan Brixius and Jean-Pierre Goux and
Jeff Linderoth},
title = {Solving large quadratic assignment problems on computational
grids},
doi = {10.1007/s101070100255},
year = 2002,
month = feb,
volume = 91,
number = 3,
pages = {563--588},
journal = {Mathematical Programming Series B},
abstract = {The quadratic assignment problem (QAP) is among the hardest
combinatorial optimization problems. Some instances of size
$n = 30$ have remained unsolved for decades. The solution of
these problems requires both improvements in mathematical
programming algorithms and the utilization of powerful
computational platforms. In this article we describe a novel
approach to solve QAPs using a state-of-the-art
branch-and-bound algorithm running on a federation of
geographically distributed resources known as a computational
grid. Solution of QAPs of unprecedented complexity, including
the nug30, kra30b, and tho30 instances, is reported.}
}
@article{AppBixChvCoo03:mp,
author = { David Applegate and Robert E. Bixby and Va{\v{s}}ek Chv{\'a}tal and William J. Cook },
title = {Implementing the {Dantzig}-{Fulkerson}-{Johnson} Algorithm for Large Traveling Salesman Problems},
journal = {Mathematical Programming Series B},
year = 2003,
volume = 97,
number = {1--2},
pages = {91--153},
doi = {10.1007/s10107-003-0440-4}
}
@article{AppBixChvCoo98,
author = { David Applegate and Robert E. Bixby and Va{\v{s}}ek Chv{\'a}tal and William J. Cook },
title = {On the Solution of Traveling Salesman Problems},
journal = {Documenta Mathematica},
year = 1998,
volume = {Extra Volume ICM III},
pages = {645--656}
}
@article{AppBlaNew1961,
author = {Appleby, J. S. and Blake, D. V. and Newman, E. A.},
title = {Techniques for producing school timetables on a computer and
their application to other scheduling problems},
journal = {The Computer Journal},
year = 1961,
volume = 3,
number = 4,
pages = {237--245},
doi = {10.1093/comjnl/3.4.237}
}
@article{AppCoo91,
author = { David Applegate and William J. Cook },
title = {A Computational Study of the Job-Shop Scheduling
Problem},
journal = {ORSA Journal on Computing},
year = 1991,
volume = 3,
number = 2,
pages = {149--156}
}
@article{AppCooRoh2003,
title = {Chained {Lin}-{Kernighan} for Large Traveling Salesman
Problems},
author = { David Applegate and William J. Cook and Andr{\'e} Rohe},
journal = {INFORMS Journal on Computing},
volume = 15,
number = 1,
pages = {82--92},
year = 2003,
doi = {10.1287/ijoc.15.1.82.15157}
}
@article{AppEtAl09,
author = { David Applegate and Robert E. Bixby and Va{\v{s}}ek Chv{\'a}tal and William J. Cook and
D. Espinoza and M. Goycoolea and Keld Helsgaun },
title = {Certification of an Optimal {TSP} Tour Through 85,900 Cities},
journal = {Operations Research Letters},
volume = 37,
number = 1,
year = 2009,
pages = {11--15}
}
@article{AraCamCam2022openletter,
author = {Claus Aranha and Camacho-Villal\'{o}n, Christian Leonardo and Felipe Campelo and Marco Dorigo and Rub{\'e}n Ruiz and Marc Sevaux and Kenneth S{\"o}rensen and Thomas St{\"u}tzle },
title = {Metaphor-based Metaheuristics, a Call for Action: the Elephant in the Room},
journal = {Swarm Intelligence},
pages = {1--6},
volume = 16,
number = 1,
doi = {10.1007/s11721-021-00202-9},
year = 2022
}
@article{ArcSavSpe2016vehicle,
title = {The Vehicle Routing Problem with Occasional Drivers},
author = {Archetti, Claudia and Martin Savelsbergh and Speranza, M. Grazia },
journal = {European Journal of Operational Research},
volume = 254,
number = 2,
pages = {472--480},
year = 2016,
doi = {10.1016/j.ejor.2016.03.049},
publisher = {Elsevier}
}
@article{ArnSanSorVid2019,
author = {Florian Arnold and Santana, \'{I}talo and Kenneth S{\"o}rensen and Thibaut Vidal },
title = {{PILS}: Exploring high-order neighborhoods by pattern mining
and injection},
journal = {Arxiv preprint arXiv:1912.11462 [cs.AI]},
year = 2019,
doi = {10.48550/arXiv.1912.11462}
}
@article{ArnSor2019knowledge,
author = {Florian Arnold and Kenneth S{\"o}rensen },
title = {Knowledge-guided local search for the vehicle routing
problem},
journal = {Computers \& Operations Research},
year = 2019,
volume = 105,
pages = {32--46},
doi = {10.1016/j.cor.2019.01.002}
}
@article{ArnSor2019vrp,
author = {Florian Arnold and Kenneth S{\"o}rensen },
title = {What makes a {VRP} solution good? The generation of
problem-specific knowledge for heuristics},
journal = {Computers \& Operations Research},
year = 2019,
volume = 106,
pages = {280--288},
doi = {10.1016/j.cor.2018.02.007}
}
@article{AroKadKhu2006,
title = {An empirical comparison of tabu search, simulated annealing,
and genetic algorithms for facilities location problems},
author = {Arostegui Jr, Marvin A. and Kadipasaoglu, Sukran N. and Khumawala, Basheer M.},
journal = {International Journal of Production Economics},
volume = 103,
number = 2,
pages = {742--754},
year = 2006,
publisher = {Elsevier}
}
@article{Arr04,
title = {A partial enumeration heuristic for multi-objective
flowshop scheduling problems},
author = { Jos{\'e} Elias C. Arroyo and V. A. Armentano},
journal = {Journal of the Operational Research Society},
volume = 55,
number = 9,
pages = {1000--1007},
year = 2004
}
@article{ArrArm05,
author = { Jos{\'e} Elias C. Arroyo and V. A. Armentano},
title = {Genetic local search for multi-objective flowshop
scheduling problems},
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year = 2014,
eprinttype = {arXiv},
eprint = {1410.7172},
keywords = {Treed-GP}
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title = {Finding Optimal Algorithmic Parameters Using Derivative-Free
Optimization},
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volume = 17,
number = 3,
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title = {Using Confidence Bounds for Exploitation-Exploration
Trade-offs},
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year = 2002,
abstract = {We show how a standard tool from statistics --- namely
confidence bounds --- can be used to elegantly deal with
situations which exhibit an exploitation-exploration
trade-off. Our technique for designing and analyzing
algorithms for such situations is general and can be applied
when an algorithm has to make exploitation-versus-exploration
decisions based on uncertain information provided by a random
process. We apply our technique to two models with such an
exploitation-exploration trade-off. For the adversarial
bandit problem with shifting our new algorithm suffers only
$O((ST)^{1/2})$ regret with high probability over $T$ trials
with $S$ shifts. Such a regret bound was previously known
only in expectation. The second model we consider is
associative reinforcement learning with linear value
functions. For this model our technique improves the regret
from $O(T^{3/4})$ to $O(T^{1/2})$.}
}
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{QUBO} Solving},
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title = {Gaussian process optimization with failures: Classification
and convergence proof},
author = {Bachoc, Fran{\c c}ois and Helbert, C{\'e}line and Picheny,
Victor},
journal = {Journal of Global Optimization},
year = 2020,
epub = {https://hal.archives-ouvertes.fr/hal-02100819/file/optimwithfailurerevised_hal.pdf},
keywords = {crashed simulation; latent gaussian process; automotive fan
design; industrial application; GP classification; Expected
Feasible Improvement with Gaussian Process Classification
with signs; EFI GPC sign},
doi = {10.1007/s10898-020-00920-0},
abstract = {We consider the optimization of a computer model where each
simulation either fails or returns a valid output
performance. We first propose a new joint Gaussian process
model for classification of the inputs (computation failure
or success) and for regression of the performance
function. We provide results that allow for a computationally
efficient maximum likelihood estimation of the covariance
parameters, with a stochastic approximation of the likelihood
gradient. We then extend the classical improvement criterion
to our setting of joint classification and regression. We
provide an efficient computation procedure for the extended
criterion and its gradient. We prove the almost sure
convergence of the global optimization algorithm following
from this extended criterion. We also study the practical
performances of this algorithm, both on simulated data and on
a real computer model in the context of automotive fan
design.}
}
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author = { Johannes Bader and Eckart Zitzler },
title = {{HypE}: An Algorithm for Fast Hypervolume-Based
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title = {Bi-objective multi-layer location--\hspace{0pt}allocation model for the
immediate aftermath of sudden-onset disasters},
author = {Baharmand, Hossein and Comes, Tina and Lauras, Matthieu},
journal = {Transportation Research Part E: Logistics and Transportation Review},
volume = 127,
pages = {86--110},
year = 2019,
doi = {10.1016/j.tre.2019.05.002},
abstract = {Locating distribution centers is critical for humanitarians
in the immediate aftermath of a sudden-onset disaster. A
major challenge lies in balancing the complexity and
uncertainty of the problem with time and resource
constraints. To address this problem, we propose a
location-allocation model that divides the topography of
affected areas into multiple layers; considers constrained
number and capacity of facilities and fleets; and allows
decision-makers to explore trade-offs between response time
and logistics costs. To illustrate our theoretical work, we
apply the model to a real dataset from the 2015 Nepal
earthquake response. For this case, our method results in a
considerable reduction of logistics costs.}
}
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title = {Is there a reproducibility crisis?},
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year = 2016
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author = {Edward K. Baker},
title = {An Exact Algorithm for the Time-Constrained
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year = 1983,
pages = {938--945},
anote = {makespan optimization}
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title = {Facility location in humanitarian relief},
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publisher = {Taylor \& Francis}
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author = { Prasanna Balaprakash and Mauro Birattari and Thomas St{\"u}tzle and Marco Dorigo },
title = {Adaptive Sampling Size and Importance Sampling in Estimation-based
Local Search for the Probabilistic Traveling Salesman Problem},
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number = 1,
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@article{BalBirStuDor2010cor,
author = { Prasanna Balaprakash and Mauro Birattari and Thomas St{\"u}tzle and Marco Dorigo },
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author = { Prasanna Balaprakash and Mauro Birattari and Thomas St{\"u}tzle and Marco Dorigo },
title = {Estimation-based Metaheuristics for the Single Vehicle Routing Problem with Stochastic Demands and Customers},
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author = { Egon Balas and Neil Simonetti },
title = {Linear Time Dynamic-Programming Algorithms for New
Classes of Restricted {TSP}s: {A} Computational Study},
volume = 13,
doi = {10.1287/ijoc.13.1.56.9748},
abstract = {Consider the following restricted (symmetric or
asymmetric) traveling-salesman problem {(TSP):}
given an initial ordering of the n cities and an
integer $k > 0$, find a minimum-cost
feasible tour, where a feasible tour is one in which
city $i$ precedes city $j$ whenever $j >= i + k$ in the
initial ordering. Balas (1996) has proposed a
dynamic-programming algorithm that solves this
problem in time linear in n, though exponential in
k. Some important real-world problems are amenable
to this model or some of its close relatives. The
algorithm of Balas (1996) constructs a layered
network with a layer of nodes for each position in
the tour, such that source-sink paths in this
network are in one-to-one correspondence with tours
that satisfy the postulated precedence
constraints. In this paper we discuss an
implementation of the dynamic-programming algorithm
for the general case when the integer k is replaced
with city-specific integers k(j), j = 1, . . .,
n. We discuss applications to, and computational
experience with, {TSPs} with time windows, a model
frequently used in vehicle routing as well as in
scheduling with setup, release and delivery
times. We also introduce a new model, the {TSP} with
target times, applicable to {Just-in-Time}
scheduling problems. Finally for {TSPs} that have no
precedence restrictions, we use the algorithm as a
heuristic that finds in linear time a local optimum
over an exponential-size neighborhood. For this
case, we implement an iterated version of our
procedure, based on contracting some arcs of the
tour produced by a first application of the
algorithm, then reapplying the algorithm to the
shrunken graph with the same k.},
number = 1,
journal = {INFORMS Journal on Computing},
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keywords = {tsptw},
pages = {56--75}
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abstract = {Agent-based models (ABM) are examples of complex adaptive
systems, which can be characterized as those systems for
which no model less complex than the system itself can
accurately predict in detail how the system will behave at
future times. Consequently, the standard tools of policy
analysis, based as they are on devising policies that perform
well on some best estimate model of the system, cannot be
reliably used for ABM. This paper argues that policy analysis
by using ABM requires an alternative approach to decision
theory. The general characteristics of such an approach are
described, and examples are provided of its application to
policy analysis.ABM, agent-based model},
journal = {Proceedings of the National Academy of Sciences},
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Combining Response Surface Methodology and Racing Algorithms},
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Bennetot and Siham Tabik and Alberto Barbado and Salvador
Garcia and Sergio Gil-Lopez and Daniel Molina and Richard
Benjamins and Raja Chatila and Francisco Herrera},
title = {Explainable Artificial Intelligence ({XAI}): Concepts,
taxonomies, opportunities and challenges toward responsible
{AI}},
journal = {Information Fusion}
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title = {Benchmarking in Optimization: Best Practice and Open Issues},
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Cava} and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Katherine M. Malan and Jason H. Moore and Boris Naujoks and Patryk Orzechowski and Vanessa Volz and Markus Wagner and Thomas Weise},
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author = { Roberto Battiti and Tecchiolli, Giampietro },
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year = 2019,
title = {Peer Reviews for Making Cities Resilient: A Systematic
Literature Review},
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number = 4,
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doi = {10.1504/IJEM.2019.104201},
abstract = {Peer reviews are a unique governance tool that use expertise
from one city or country to assess and strengthen the
capabilities of another. Peer review tools are gaining
momentum in disaster management and remain an important but
understudied topic in risk governance. Methodologies to
conduct a peer review are still in their infancy. To enhance
these, a systematic literature review (SLR) of academic and
non-academic literature was conducted on city resilience peer
reviews. Thirty-three attributes of resilience are
identified, which provides useful insights into how research
and practice can inform risk governance, and utilise peer
reviews, to drive meaningful change. Moreover, it situates
the challenges associated with resilience building tools
within risk governance to support the development of
interdisciplinary perspectives for integrated city resilience
frameworks. Results of this research have been used to
develop a peer review methodology and an international
standard on conducting peer reviews for disaster risk
reduction.},
keywords = {city resilience, city peer review, disaster risk governance}
}
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title = {Fast Algorithms for Geometric Traveling Salesman
Problems},
journal = {ORSA Journal on Computing},
year = 1992,
volume = 4,
number = 4,
pages = {387--411}
}
@article{BenKao2013,
author = {Una Benlic and Jin-Kao Hao },
title = {Breakout Local Search for the Quadratic Assignment Problem},
journal = {Applied Mathematics and Computation},
year = 2013,
volume = 219,
number = 9,
pages = {4800--4815}
}
@article{BenLiuAuIst2014transient,
title = {Transient protein-protein interface prediction: datasets,
features, algorithms, and the {RAD-T} predictor},
author = {Bendell, Calem J. and Liu, Shalon and Aumentado-Armstrong,
Tristan and Istrate, Bogdan and Cernek, Paul T. and Khan,
Samuel and Picioreanu, Sergiu and Zhao, Michael and Murgita,
Robert A.},
journal = {BMC Bioinformatics},
volume = 15,
pages = 82,
year = 2014
}
@article{BenLodPro2021ml,
author = { Bengio, Yoshua and Andrea Lodi and Antoine Prouvost},
title = {Machine learning for combinatorial optimization: A
methodological tour d'horizon},
journal = {European Journal of Operational Research},
year = 2021,
volume = 290,
number = 2,
pages = {405--421},
doi = {10.1016/j.ejor.2020.07.063},
keywords = {Combinatorial optimization, Machine learning, Branch and
bound, Mixed-integer programming solvers},
abstract = {This paper surveys the recent attempts, both from the machine
learning and operations research communities, at leveraging
machine learning to solve combinatorial optimization
problems. Given the hard nature of these problems,
state-of-the-art algorithms rely on handcrafted heuristics
for making decisions that are otherwise too expensive to
compute or mathematically not well defined. Thus, machine
learning looks like a natural candidate to make such
decisions in a more principled and optimized way. We advocate
for pushing further the integration of machine learning and
combinatorial optimization and detail a methodology to do
so. A main point of the paper is seeing generic optimization
problems as data points and inquiring what is the relevant
distribution of problems to use for learning on a given
task.}
}
@article{BenRit2016:cor,
author = {Alexander Javier Benavides and Marcus Ritt},
title = {Two Simple and Effective Heuristics for Minimizing the
Makespan in Non-permutation Flow Shops},
journal = {Computers \& Operations Research},
year = 2016,
volume = 66,
pages = {160--169},
doi = {10.1016/j.cor.2015.08.001}
}
@article{Benders1962,
author = {Benders, J. F.},
title = {Partitioning Procedures for Solving Mixed-variables Programming Problems},
journal = {Numerische Mathematik},
year = 1962,
volume = 4,
number = 3,
pages = {238--252}
}
@article{Bentley1980,
author = { Jon Louis Bentley },
title = {Multidimensional Divide-and-conquer},
journal = {Communications of the ACM},
year = 1980,
volume = 23,
number = 4,
doi = {10.1145/358841.358850},
pages = {214--229},
abstract = {Most results in the field of algorithm design are single
algorithms that solve single problems. In this paper we
discuss multidimensional divide-and-conquer, an algorithmic
paradigm that can be instantiated in many different ways to
yield a number of algorithms and data structures for
multidimensional problems. We use this paradigm to give
best-known solutions to such problems as the ECDF, maxima,
range searching, closest pair, and all nearest neighbor
problems. The contributions of the paper are on two
levels. On the first level are the particular algorithms and
data structures given by applying the paradigm. On the
second level is the more novel contribution of this paper: a
detailed study of an algorithmic paradigm that is specific
enough to be described precisely yet general enough to solve
a wide variety of problems.}
}
@article{BerBen2012jmlr,
author = { James S. Bergstra and Bengio, Yoshua },
title = {Random Search for Hyper-Parameter Optimization},
journal = {Journal of Machine Learning Research},
year = 2012,
volume = 13,
pages = {281--305},
abstract = {Grid search and manual search are the most widely
used strategies for hyper-parameter
optimization. This paper shows empirically and
theoretically that randomly chosen trials are more
efficient for hyper-parameter optimization than
trials on a grid. Empirical evidence comes from a
comparison with a large previous study that used
grid search and manual search to configure neural
networks and deep belief networks. Compared with
neural networks configured by a pure grid search, we
find that random search over the same domain is able
to find models that are as good or better within a
small fraction of the computation time. Granting
random search the same computational budget, random
search finds better models by effectively searching
a larger, less promising configuration
space. Compared with deep belief networks configured
by a thoughtful combination of manual search and
grid search, purely random search over the same
32-dimensional configuration space found
statistically equal performance on four of seven
data sets, and superior performance on one of
seven. A Gaussian process analysis of the function
from hyper-parameters to validation set performance
reveals that for most data sets only a few of the
hyper-parameters really matter, but that different
hyper-parameters are important on different data
sets. This phenomenon makes grid search a poor
choice for configuring algorithms for new data
sets. Our analysis casts some light on why recent
"High Throughput" methods achieve surprising
success: they appear to search through a large number
of hyper-parameters because most hyper-parameters do
not matter much. We anticipate that growing interest
in large hierarchical models will place an
increasing burden on techniques for hyper-parameter
optimization; this work shows that random search is
a natural baseline against which to judge progress
in the development of adaptive (sequential)
hyper-parameter optimization algorithms.},
epub = {http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf}
}
@article{BerEmmTav2017managing,
title = {Managing catastrophic climate risks under model uncertainty
aversion},
author = {Berger, Lo{\"i}c and Emmerling, Johannes and Tavoni,
Massimo},
journal = {Management Science},
volume = 63,
number = 3,
pages = {749--765},
year = 2017,
publisher = {{INFORMS}}
}
@article{BerFisLod2007,
title = {A feasibility pump heuristic for general mixed-integer problems},
author = {Bertacco, Livio and Matteo Fischetti and Andrea Lodi },
journal = {Discrete Optimization},
volume = 4,
number = 1,
pages = {63--76},
year = 2007,
publisher = {Elsevier}
}
@article{BerKal2020,
title = {From predictive to prescriptive analytics},
author = {Bertsimas, Dimitris and Kallus, Nathan},
journal = {Management Science},
volume = 66,
number = 3,
pages = {1025--1044},
year = 2020,
publisher = {{INFORMS}}
}
@article{BerKraSch2016bayesian,
author = {Felix Berkenkamp and Andreas Krause and Angela P. Schoellig},
title = {Bayesian Optimization with Safety Constraints: Safe and
Automatic Parameter Tuning in Robotics},
journal = {Arxiv preprint arXiv:1602.04450},
year = 2016,
url = {http://arxiv.org/abs/1602.04450},
keywords = {Safe Optimization, SafeOpt}
}
@article{BerKraSch2021bayesian,
author = {Berkenkamp, Felix and Krause, Andreas and Schoellig, Angela
P.},
title = {Bayesian optimization with safety constraints: safe and
automatic parameter tuning in robotics},
journal = {Machine Learning},
year = 2021,
month = jun,
annote = {Preprint: \url{http://arxiv.org/abs/1602.04450}},
doi = {10.1007/s10994-021-06019-1},
abstract = {Selecting the right tuning parameters for algorithms is a
pravelent problem in machine learning that can significantly
affect the performance of algorithms. Data-efficient
optimization algorithms, such as Bayesian optimization, have
been used to automate this process. During experiments on
real-world systems such as robotic platforms these methods
can evaluate unsafe parameters that lead to safety-critical
system failures and can destroy the system. Recently, a safe
Bayesian optimization algorithm, called SafeOpt, has been
developed, which guarantees that the performance of the
system never falls below a critical value; that is, safety is
defined based on the performance function. However, coupling
performance and safety is often not desirable in practice,
since they are often opposing objectives. In this paper, we
present a generalized algorithm that allows for multiple
safety constraints separate from the objective. Given an
initial set of safe parameters, the algorithm maximizes
performance but only evaluates parameters that satisfy safety
for all constraints with high probability. To this end, it
carefully explores the parameter space by exploiting
regularity assumptions in terms of a Gaussian process
prior. Moreover, we show how context variables can be used to
safely transfer knowledge to new situations and tasks. We
provide a theoretical analysis and demonstrate that the
proposed algorithm enables fast, automatic, and safe
optimization of tuning parameters in experiments on a
quadrotor vehicle.}
}
@article{BerTsiWu1997:joh,
author = {Dimitri P. Bertsekas and John N. Tsitsiklis and Cynara Wu},
title = {Rollout Algorithms for Combinatorial Optimization},
journal = {Journal of Heuristics},
year = 1997,
volume = 3,
number = 3,
pages = {245--262}
}
@article{BerWan1987binpack,
title = {Two-dimensional finite bin-packing algorithms},
author = {Berkey, Judith O. and Wang, Pearl Y.},
journal = {Journal of the Operational Research Society},
volume = 38,
number = 5,
pages = {423--429},
year = 1987,
doi = {10.2307/2582731}
}
@article{BeuFonLopPaqVah09:tec,
author = { Nicola Beume and Carlos M. Fonseca and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Jan Vahrenhold },
title = {On the complexity of computing the hypervolume
indicator},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2009,
volume = 13,
number = 5,
pages = {1075--1082},
doi = {10.1109/TEVC.2009.2015575},
abstract = {The goal of multi-objective optimization is to find
a set of best compromise solutions for typically
conflicting objectives. Due to the complex nature of
most real-life problems, only an approximation to
such an optimal set can be obtained within
reasonable (computing) time. To compare such
approximations, and thereby the performance of
multi-objective optimizers providing them, unary
quality measures are usually applied. Among these,
the \emph{hypervolume indicator} (or
\emph{S-metric}) is of particular relevance due to
its favorable properties. Moreover, this indicator
has been successfully integrated into stochastic
optimizers, such as evolutionary algorithms, where
it serves as a guidance criterion for finding good
approximations to the Pareto front. Recent results
show that computing the hypervolume indicator can be
seen as solving a specialized version of Klee's
Measure Problem. In general, Klee's Measure Problem
can be solved with $\mathcal{O}(n \log n +
n^{d/2}\log n)$ comparisons for an input instance of
size $n$ in $d$ dimensions; as of this writing, it
is unknown whether a lower bound higher than
$\Omega(n \log n)$ can be proven. In this article,
we derive a lower bound of $\Omega(n\log n)$ for the
complexity of computing the hypervolume indicator in
any number of dimensions $d>1$ by reducing the
so-called \textsc{UniformGap} problem to it. For
the three dimensional case, we also present a
matching upper bound of $\mathcal{O}(n\log n)$
comparisons that is obtained by extending an
algorithm for finding the maxima of a point set.}
}
@article{BeuNauEmm2007ejor,
author = { Nicola Beume and Boris Naujoks and Emmerich, Michael T. M. },
title = {{SMS-EMOA}: Multiobjective selection based on
dominated hypervolume},
journal = {European Journal of Operational Research},
year = 2007,
volume = 181,
number = 3,
pages = {1653--1669},
doi = {10.1016/j.ejor.2006.08.008}
}
@article{BeySch2002:es,
author = { Hans-Georg Beyer and Hans-Paul Schwefel },
title = {Evolution Strategies: A Comprehensive Introduction},
journal = {Natural Computing},
volume = 1,
pages = {3--52},
year = 2002
}
@article{BeySchWeg2002,
title = {How to analyse evolutionary algorithms},
author = { Hans-Georg Beyer and Hans-Paul Schwefel and Ingo Wegener },
journal = {Theoretical Computer Science},
volume = 287,
number = 1,
pages = {101--130},
year = 2002,
publisher = {Elsevier}
}
@article{BezLopStu2015tec,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Component-Wise Design of Multi-Objective
Evolutionary Algorithms},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2016,
volume = 20,
number = 3,
pages = {403--417},
doi = {10.1109/TEVC.2015.2474158},
supplement = {https://github.com/iridia-ulb/automoea-tevc-2016}
}
@article{BezLopStu2017assessment,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {A Large-Scale Experimental Evaluation of High-Performing
Multi- and Many-Objective Evolutionary Algorithms},
year = 2018,
journal = {Evolutionary Computation},
doi = {10.1162/evco_a_00217},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2015-007/},
volume = 26,
number = 4,
pages = {621--656},
abstract = {Research on multi-objective evolutionary algorithms (MOEAs)
has produced over the past decades a large number of
algorithms and a rich literature on performance assessment
tools to evaluate and compare them. Yet, newly proposed MOEAs
are typically compared against very few, often a decade older
MOEAs. One reason for this apparent contradiction is the lack
of a common baseline for comparison, with each subsequent
study often devising its own experimental scenario, slightly
different from other studies. As a result, the state of the
art in MOEAs is a disputed topic. This article reports a
systematic, comprehensive evaluation of a large number of
MOEAs that covers a wide range of experimental scenarios. A
novelty of this study is the separation between the
higher-level algorithmic components related to
multi-objective optimization (MO), which characterize each
particular MOEA, and the underlying parameters-such as
evolutionary operators, population size, etc.-whose
configuration may be tuned for each scenario. Instead of
relying on a common or "default" parameter configuration that
may be low-performing for particular MOEAs or scenarios and
unintentionally biased, we tune the parameters of each MOEA
for each scenario using automatic algorithm configuration
methods. Our results confirm some of the assumed knowledge in
the field, while at the same time they provide new insights
on the relative performance of MOEAs for many-objective
problems. For example, under certain conditions,
indicator-based MOEAs are more competitive for such problems
than previously assumed. We also analyze problem-specific
features affecting performance, the agreement between
performance metrics, and the improvement of tuned
configurations over the default configurations used in the
literature. Finally, the data produced is made publicly
available to motivate further analysis and a baseline for
future comparisons.}
}
@article{BezLopStu2019ec,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatically Designing State-of-the-Art Multi- and
Many-Objective Evolutionary Algorithms},
journal = {Evolutionary Computation},
year = 2020,
volume = 28,
number = 2,
pages = {195--226},
doi = {10.1162/evco_a_00263},
supplement = {https://github.com/iridia-ulb/automoea-ecj-2020},
abstract = {A recent comparison of well-established multiobjective
evolutionary algorithms (MOEAs) has helped better identify
the current state-of-the-art by considering (i) parameter
tuning through automatic configuration, (ii) a wide range of
different setups, and (iii) various performance
metrics. Here, we automatically devise MOEAs with verified
state-of-the-art performance for multi- and many-objective
continuous optimization. Our work is based on two main
considerations. The first is that high-performing algorithms
can be obtained from a configurable algorithmic framework in
an automated way. The second is that multiple performance
metrics may be required to guide this automatic design
process. In the first part of this work, we extend our
previously proposed algorithmic framework, increasing the
number of MOEAs, underlying evolutionary algorithms, and
search paradigms that it comprises. These components can be
combined following a general MOEA template, and an automatic
configuration method is used to instantiate high-performing
MOEA designs that optimize a given performance metric and
present state-of-the-art performance. In the second part, we
propose a multiobjective formulation for the automatic MOEA
design, which proves critical for the context of
many-objective optimization due to the disagreement of
established performance metrics. Our proposed formulation
leads to an automatically designed MOEA that presents
state-of-the-art performance according to a set of metrics,
rather than a single one.}
}
@article{BiaBirMan2006jmma,
author = { Leonora Bianchi and Mauro Birattari and M. Manfrin and
M. Mastrolilli and Lu{\'i}s Paquete and O. Rossi-Doria and Tommaso Schiavinotto },
title = {Hybrid Metaheuristics for the Vehicle Routing Problem with
Stochastic Demands},
journal = {Journal of Mathematical Modelling and Algorithms},
year = 2006,
volume = 5,
number = 1,
pages = {91--110}
}
@article{BiaDorGam2009survey,
title = {A survey on metaheuristics for stochastic combinatorial
optimization},
author = { Leonora Bianchi and Marco Dorigo and L. M. Gambardella and Gutjahr, Walter J. },
journal = {Natural Computing},
volume = 8,
number = 2,
pages = {239--287},
year = 2009
}
@article{BinGinRou2015gaupar,
title = {Quantifying uncertainty on {Pareto} fronts with {Gaussian}
process conditional simulations},
volume = 243,
doi = {10.1016/j.ejor.2014.07.032},
abstract = {Multi-objective optimization algorithms aim at finding
Pareto-optimal solutions. Recovering Pareto fronts or Pareto
sets from a limited number of function evaluations are
challenging problems. A popular approach in the case of
expensive-to-evaluate functions is to appeal to
metamodels. Kriging has been shown efficient as a base for
sequential multi-objective optimization, notably through
infill sampling criteria balancing exploitation and
exploration such as the Expected Hypervolume
Improvement. Here we consider Kriging metamodels not only for
selecting new points, but as a tool for estimating the whole
Pareto front and quantifying how much uncertainty remains on
it at any stage of Kriging-based multi-objective optimization
algorithms. Our approach relies on the Gaussian process
interpretation of Kriging, and bases upon conditional
simulations. Using concepts from random set theory, we
propose to adapt the Vorob'ev expectation and deviation to
capture the variability of the set of non-dominated
points. Numerical experiments illustrate the potential of the
proposed workflow, and it is shown on examples how Gaussian
process simulations and the estimated Vorob'ev deviation can
be used to monitor the ability of Kriging-based
multi-objective optimization algorithms to accurately learn
the Pareto front.},
number = 2,
journal = {European Journal of Operational Research},
author = {Binois, M. and Ginsbourger, D. and Roustant, O.},
year = 2015,
keywords = {Attainment function, Expected Hypervolume Improvement,
Kriging, Multi-objective optimization, Vorob'ev expectation},
pages = {386--394}
}
@article{BirBalStuDor07:informs,
author = { Mauro Birattari and Prasanna Balaprakash and Thomas St{\"u}tzle and Marco Dorigo },
title = {Estimation Based Local Search for Stochastic Combinatorial Optimization},
journal = {INFORMS Journal on Computing},
year = 2008,
volume = 20,
number = 4,
pages = {644--658}
}
@article{BirPelDor2007:tec,
author = { Mauro Birattari and Paola Pellegrini and Marco Dorigo },
title = {On the invariance of ant colony optimization},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2007,
volume = 11,
number = 6,
pages = {732--742},
doi = {10.1109/TEVC.2007.892762}
}
@article{BirZloDor06meta_design,
author = { Mauro Birattari and Zlochin, M. and Marco Dorigo },
title = {Towards a theory of practice in metaheuristics design: A machine learning perspective},
journal = {Theoretical Informatics and Applications},
year = 2006,
volume = 40,
number = 2,
pages = {353--369}
}
@article{BisIzzYam2010:pagmo-arxiv,
title = {A Global Optimisation Toolbox for Massively Parallel
Engineering Optimisation},
author = {Biscani, Francesco and Dario Izzo and Yam, Chit Hong},
journal = {Arxiv preprint arXiv:1004.3824},
year = 2010,
url = {http://arxiv.org/abs/1004.3824},
keywords = {PaGMO},
abstract = {A software platform for global optimisation, called PaGMO,
has been developed within the Advanced Concepts Team (ACT) at
the European Space Agency, and was recently released as an
open-source project. PaGMO is built to tackle
high-dimensional global optimisation problems, and it has
been successfully used to find solutions to real-life
engineering problems among which the preliminary design of
interplanetary spacecraft trajectories - both chemical
(including multiple flybys and deep-space maneuvers) and
low-thrust (limited, at the moment, to single phase
trajectories), the inverse design of nano-structured
radiators and the design of non-reactive controllers for
planetary rovers. Featuring an arsenal of global and local
optimisation algorithms (including genetic algorithms,
differential evolution, simulated annealing, particle swarm
optimisation, compass search, improved harmony search, and
various interfaces to libraries for local optimisation such
as SNOPT, IPOPT, GSL and NLopt), PaGMO is at its core a C++
library which employs an object-oriented architecture
providing a clean and easily-extensible optimisation
framework. Adoption of multi-threaded programming ensures the
efficient exploitation of modern multi-core architectures and
allows for a straightforward implementation of the island
model paradigm, in which multiple populations of candidate
solutions asynchronously exchange information in order to
speed-up and improve the optimisation process. In addition to
the C++ interface, PaGMO's capabilities are exposed to the
high-level language Python, so that it is possible to easily
use PaGMO in an interactive session and take advantage of the
numerous scientific Python libraries available.}
}
@article{BisBinLan2023wirdmkd,
title = {Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges},
author = { Bernd Bischl and Binder, Martin and Lang, Michel and Pielok, Tobias and Richter, Jakob and Coors, Stefan and Thomas, Janek and Ullmann, Theresa and Becker, Marc and Boulesteix, Anne-Laure and Deng, Difan and Marius Thomas Lindauer },
journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
volume = 13,
number = 2,
pages = {e1484},
year = 2023,
publisher = {Wiley Online Library}
}
@article{BisKerKot++16:ASlib,
author = { Bernd Bischl and Pascal Kerschke and Kotthoff, Lars and Marius Thomas Lindauer and Yuri Malitsky and Alexandre Fr{\'{e}}chette and Holger H. Hoos and Frank Hutter and Kevin Leyton-Brown and Kevin Tierney and Joaquin Vanschoren },
title = {{ASlib}: A Benchmark Library for Algorithm Selection},
journal = {Artificial Intelligence},
year = 2016,
volume = 237,
pages = {41--58}
}
@article{BisLanKot2016mlr,
title = {{\rpackage{mlr}}: Machine Learning in \proglang{R}},
author = { Bernd Bischl and Michel Lang and Kotthoff, Lars and Julia
Schiffner and Jakob Richter and Erich Studerus and Giuseppe
Casalicchio and Zachary M. Jones},
journal = {Journal of Machine Learning Research},
year = 2016,
volume = 17,
number = 170,
pages = {1--5},
epub = {http://jmlr.org/papers/v17/15-066.html}
}
@article{BlaHerSanMar2008vis,
title = {A new graphical visualization of n-dimensional {Pareto} front
for decision-making in multiobjective optimization},
author = {Blasco, Xavier and Herrero, Juan M. and Sanchis, Javier and
Mart{\'i}nez, Manuel},
journal = {Information Sciences},
volume = 178,
number = 20,
pages = {3908--3924},
year = 2008,
publisher = {Elsevier}
}
@article{BlaRayEde2017:corr,
author = {Craig Blackmore and Oliver Ray and Kerstin Eder},
title = {Automatically Tuning the {GCC} Compiler to Optimize the
Performance of Applications Running on Embedded Systems},
journal = {Arxiv preprint arXiv:1703.08228},
url = {https://arxiv.org/abs/1703.08228},
year = 2017
}
@article{BleBlu2007:jmma,
author = { Mar{\'i}a J. Blesa and Christian Blum },
title = {Finding edge-disjoint paths in networks by means of
artificial ant colonies},
journal = {Journal of Mathematical Modelling and Algorithms},
year = 2007,
volume = 6,
number = 3,
pages = {361--391}
}
@article{BliCosRefZha2023aitsp,
title = {The First {AI4TSP} Competition: Learning to Solve Stochastic
Routing Problems},
journal = {Artificial Intelligence},
pages = 103918,
volume = 319,
year = 2023,
issn = {0004-3702},
doi = {10.1016/j.artint.2023.103918},
author = {Laurens Bliek and Paulo {da Costa} and Reza {Refaei Afshar}
and Robbert Reijnen and Yingqian Zhang and Tom Catshoek and
Dani{\"e}l Vos and Sicco Verwer and Fynn Schmitt-Ulms and
Andr{\'e} Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf
and Federico Bobbio and Justine Pepin and Warley Almeida
Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Irurozki, Ekhine },
keywords = {AI for TSP competition, Travelling salesman problem, Routing
problem, Stochastic combinatorial optimization,
Surrogate-based optimization, Deep reinforcement learning},
abstract = {This paper reports on the first international competition on
AI for the traveling salesman problem (TSP) at the
International Joint Conference on Artificial Intelligence
2021 (IJCAI-21). The TSP is one of the classical
combinatorial optimization problems, with many variants
inspired by real-world applications. This first competition
asked the participants to develop algorithms to solve an
orienteering problem with stochastic weights and time windows
(OPSWTW). It focused on two learning approaches:
surrogate-based optimization and deep reinforcement
learning. In this paper, we describe the problem, the
competition setup, and the winning methods, and give an
overview of the results. The winning methods described in
this work have advanced the state-of-the-art in using AI for
stochastic routing problems. Overall, by organizing this
competition we have introduced routing problems as an
interesting problem setting for AI researchers. The simulator
of the problem has been made open-source and can be used by
other researchers as a benchmark for new learning-based
methods. The instances and code for the competition are
available at
\url{https://github.com/paulorocosta/ai-for-tsp-competition}.}
}
@article{Blu05:cor,
author = { Christian Blum },
title = {{Beam-ACO}---{Hybridizing} Ant Colony Optimization
with Beam Search: {An} Application to Open Shop
Scheduling},
journal = {Computers \& Operations Research},
year = 2005,
volume = 32,
number = 6,
pages = {1565--1591}
}
@article{Blu08:informs,
author = { Christian Blum },
title = {Beam-{ACO} for simple assembly line balancing},
journal = {INFORMS Journal on Computing},
year = 2008,
volume = 20,
number = 4,
pages = {618--627},
doi = {10.1287/ijoc.1080.0271}
}
@article{BluBleLop09-BeamSearch-LCS,
author = { Christian Blum and Mar{\'i}a J. Blesa and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Beam search for the longest common subsequence
problem},
number = 12,
journal = {Computers \& Operations Research},
year = 2009,
pages = {3178--3186},
volume = 36,
doi = {10.1016/j.cor.2009.02.005},
abstract = {The longest common subsequence problem is a classical string
problem that concerns finding the common part of a set of
strings. It has several important applications, for example,
pattern recognition or computational biology. Most research
efforts up to now have focused on solving this problem
optimally. In comparison, only few works exist dealing with
heuristic approaches. In this work we present a deterministic
beam search algorithm. The results show that our algorithm
outperforms the current state-of-the-art approaches not only
in solution quality but often also in computation time.}
}
@article{BluCaBle2015swarm,
author = { Christian Blum and Borja Calvo and Mar{\'i}a J. Blesa },
title = {{FrogCOL} and {FrogMIS}: New Decentralized Algorithms for Finding Large Independent Sets in Graphs},
journal = {Swarm Intelligence},
year = 2015,
volume = 9,
number = {2-3},
pages = {205--227},
doi = {10.1007/s11721-015-0110-1},
keywords = {irace}
}
@article{BluDor03:ieee_tsmcb,
author = { Christian Blum and Marco Dorigo },
title = {The hyper-cube framework for ant colony optimization},
journal = {IEEE Transactions on Systems, Man, and Cybernetics -- Part B},
year = 2004,
volume = 34,
number = 2,
pages = {1161--1172}
}
@article{BluDor2005:tec,
author = { Christian Blum and Marco Dorigo },
journal = {IEEE Transactions on Evolutionary Computation},
number = 2,
pages = {159--174},
title = {Search Bias in Ant Colony Optimization: On the Role
of Competition-Balanced Systems},
volume = 9,
year = 2005
}
@article{BluOch2021,
author = { Christian Blum and Gabriela Ochoa },
title = {A comparative analysis of two matheuristics by means of merged local optima networks},
journal = {European Journal of Operational Research},
volume = 290,
number = 1,
pages = {36--56},
year = 2021
}
@article{BluPinLopLoz2015cor,
author = { Christian Blum and Pedro Pinacho and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Jos{\'e} A. Lozano },
title = {Construct, Merge, Solve \& Adapt: A New General Algorithm for
Combinatorial Optimization},
journal = {Computers \& Operations Research},
year = 2016,
volume = 68,
pages = {75--88},
doi = {10.1016/j.cor.2015.10.014},
keywords = {irace, CMSA}
}
@article{BluPucRaiRol11:asc,
author = { Christian Blum and Jakob Puchinger and G{\"u}nther R. Raidl and Andrea Roli },
title = {Hybrid Metaheuristics in Combinatorial Optimization: A Survey},
journal = {Applied Soft Computing},
year = 2011,
volume = 11,
number = 6,
pages = {4135--4151}
}
@article{BluRol03:acm-cs,
author = { Christian Blum and Andrea Roli },
title = {Metaheuristics in Combinatorial Optimization:
Overview and Conceptual Comparison},
journal = {{ACM} Computing Surveys},
year = 2003,
volume = 35,
number = 3,
pages = {268--308}
}
@article{BluSam2004:jmma,
author = { Christian Blum and M. Sampels },
title = {An Ant Colony Optimization Algorithm for Shop
Scheduling Problems},
journal = {Journal of Mathematical Modelling and Algorithms},
year = 2004,
volume = 3,
number = 3,
pages = {285--308},
doi = {10.1023/B:JMMA.0000038614.39977.6f}
}
@article{BluYabBle08:cor,
author = { Christian Blum and M. {Y{\'a}bar Vall{\`e}s} and Mar{\'i}a J. Blesa },
title = {An ant colony optimization algorithm for {DNA} sequencing by hybridization},
journal = {Computers \& Operations Research},
year = 2008,
volume = 35,
number = 11,
pages = {3620--3635}
}
@article{BocFawVal2018performance,
author = {Bocchese, Andrea F. and Chris Fawcett and Vallati, Mauro and Gerevini, Alfonso E. and Holger H. Hoos },
title = {Performance robustness of {AI} planners in the 2014
International Planning Competition},
volume = 31,
doi = {10.3233/AIC-170537},
abstract = {Solver competitions have been used in many areas of AI to
assess the current state of the art and guide future research
and development. AI planning is no exception, and the
International Planning Competition (IPC) has been frequently
run for nearly two decades. Due to the organisational and
computational burden involved in running these competitions,
solvers are generally compared using a single homogeneous
hardware and software environment for all competitors. To
what extent does the specific choice of hardware and software
environment have an effect on solver performance, and is that
effect distributed equally across the competing solvers? In
this work, we use the competing planners and benchmark
instance sets from the 2014 IPC to investigate these two
questions. We recreate the 2014 IPC Optimal and Agile tracks
on two distinct hardware environments and eight distinct
software environments. We show that solver performance varies
significantly based on the hardware and software environment,
and that this variation is not equal for all
planners. Furthermore, the observed variation is sufficient
to change the competition rankings, including the top-ranked
planners for some tracks.},
number = 6,
journal = {AI Communications},
publisher = {IOS Press},
year = 2018,
month = dec,
pages = {445--463}
}
@article{BoeKahMud1994,
author = {Kenneth D. Boese and Andrew B. Kahng and Sudhakar Muddu},
title = {A New Adaptive Multi-Start Technique for Combinatorial Global
Optimization},
journal = {Operations Research Letters},
year = 1994,
volume = 16,
number = 2,
pages = {101--113},
keywords = {big-valley hypothesis, TSP, landscape analysis}
}
@article{Boh2009idcs,
author = {Marko Bohanec},
title = {Decision making: a computer-science and
information-technology viewpoint},
journal = {Interdisciplinary Description of Complex Systems},
year = 2009,
volume = 7,
number = 2,
pages = {22--37}
}
@article{BohJohSte1986,
title = {Generalized Simulated Annealing for Function Optimization},
author = { Ihor O. Bohachevsky and Mark E. Johnson and Myron L. Stein },
journal = {Technometrics},
volume = 28,
number = 3,
pages = {209--217},
year = 1986,
publisher = {Taylor \& Francis}
}
@article{Bor2000,
title = {{CHESS} - Changing Horizon Efficient Set Search: A
simple principle for multiobjective optimization},
author = {Borges, P. C.},
journal = {Journal of Heuristics},
volume = 6,
number = 3,
pages = {405--418},
year = 2000
}
@article{BorHamTav2007joh,
author = {Boros, Endre and Hammer, Peter L. and Tavares, Gabriel},
title = {Local search heuristics for Quadratic Unconstrained Binary
Optimization ({QUBO})},
journal = {Journal of Heuristics},
year = 2007,
volume = 13,
number = 2,
pages = {99--132}
}
@article{Borda1781,
author = {Jean-Charles de Borda},
journal = {Histoire de l'Acad{\'e}mie Royal des Sciences},
title = {M{\'e}moire sur les {\'E}lections au Scrutin},
year = 1781,
keywords = {ranking}
}
@article{BotBon98,
author = {Hozefa M. Botee and Eric Bonabeau},
title = {Evolving Ant Colony Optimization},
year = 1998,
journal = {Advances in Complex Systems},
volume = 1,
pages = {149--159}
}
@article{BotSch2019dominance,
title = {Dominance for multi-objective robust optimization concepts},
author = {Botte, Marco and Sch{\"o}bel, Anita },
journal = {European Journal of Operational Research},
volume = 273,
number = 2,
pages = {430--440},
year = 2019,
publisher = {Elsevier}
}
@article{BouBluBou2012,
author = {Salim Bouamama and Christian Blum and Abdellah Boukerram},
title = {A Population-based Iterated Greedy Algorithm for the Minimum Weight Vertex Cover Problem},
journal = {Applied Soft Computing},
year = 2012,
volume = 12,
number = 6,
pages = {1632--1639}
}
@article{BouForGliPir2010:ejor,
author = { G{\'e}raldine Bous and Philippe Fortemps and Fran\c{c}ois Glineur and Marc Pirlot },
title = {{ACUTA}: {A} novel method for eliciting additive value functions on the basis of holistic preference statements},
journal = {European Journal of Operational Research},
year = 2010,
volume = 206,
number = 2,
pages = {435--444}
}
@article{BouLec2003ejor,
author = {Bouleimen, K. and Lecocq, H.},
title = {A new efficient simulated annealing algorithm for
the resource-constrained project scheduling problem
and its multiple mode version},
volume = 149,
doi = {10.1016/S0377-2217(02)00761-0},
abstract = {This paper describes new simulated annealing ({SA)}
algorithms for the resource-constrained project
scheduling problem ({RCPSP)} and its multiple mode
version ({MRCPSP).} The objective function
considered is minimisation of the makespan. The
conventional {SA} search scheme is replaced by a new
design that takes into account the specificity of
the solution space of project scheduling
problems. For {RCPSP}, the search was based on an
alternated activity and time incrementing process,
and all parameters were set after preliminary
statistical experiments done on test instances. For
{MRCPSP}, we introduced an original approach using
two embedded search loops alternating activity and
mode neighbourhood exploration. The performance
evaluation done on the benchmark instances available
in the literature proved the efficiency of both
adaptations that are currently among the most
competitive algorithms for these problems.},
number = 2,
journal = {European Journal of Operational Research},
year = 2003,
keywords = {multi-mode resource-constrained project scheduling,
project scheduling, simulated annealing},
pages = {268--281}
}
@article{BozFowGelKim2010or,
title = {Quantitative comparison of approximate solution sets for
multicriteria optimization problems with weighted
{Tchebycheff} preference function},
author = {Bozkurt, B. and Fowler, J. W. and Gel, E. S. and Kim, B. and Murat K{\"o}ksalan and Wallenius, Jyrki },
journal = {Operations Research},
year = 2010,
number = 3,
pages = {650--659},
volume = 58,
publisher = {INFORMS},
annote = {Proposed IPF indicator}
}
@article{BraGreSlo2010bpas,
title = {Interactive evolutionary multiobjective optimization driven
by robust ordinal regression},
author = { J{\"u}rgen Branke and Salvatore Greco and Roman S{\l}owi{\'n}ski and Zielniewicz, P},
journal = {Bulletin of the Polish Academy of Sciences: Technical Sciences},
volume = 58,
number = 3,
pages = {347--358},
year = 2010,
doi = {10.2478/v10175-010-0033-3}
}
@article{BraGutRAu2006cms,
author = {S. C. Brailsford and Gutjahr, Walter J. and M. S. Rauner and
W. Zeppelzauer},
title = {Combined Discrete-event Simulation and Ant Colony
Optimisation Approach for Selecting Optimal Screening
Policies for Diabetic Retinopathy},
journal = {Computational Management Science},
year = 2006,
volume = 4,
number = 1,
pages = {59--83}
}
@article{BraKauSch2001aes,
author = { J{\"u}rgen Branke and Kaussler, T. and Schmeck, H.},
title = {Guidance in evolutionary multi-objective optimization},
journal = {Advances in Engineering Software},
year = 2001,
volume = 32,
pages = {499--507}
}
@article{BraNguPic2016tec,
author = { J{\"u}rgen Branke and S. Nguyen and C. W. Pickardt and M. Zhang},
journal = {IEEE Transactions on Evolutionary Computation},
title = {Automated Design of Production Scheduling Heuristics: A
Review},
year = 2016,
volume = 20,
number = 1,
pages = {110--124}
}
@article{BraSch2005faster,
title = {Faster Convergence by Means of Fitness Estimation},
author = { J{\"u}rgen Branke and Schmidt, C.},
year = 2005,
month = jan,
journal = {Soft Computing},
volume = 9,
number = 1,
pages = {13--20},
issn = {1432-7643, 1433-7479},
doi = {10.1007/s00500-003-0329-4},
langid = {english}
}
@article{BraZap2016:cor,
author = {Roland Braune and G. Z{\"a}pfel},
title = {Shifting Bottleneck Scheduling for Total Weighted Tardiness Minimization---A Computational Evaluation of Subproblem and Re-optimization Heuristics},
journal = {Computers \& Operations Research},
year = 2016,
volume = 66,
pages = {130--140}
}
@article{BranCorrGreSlow2016ejor,
author = { J{\"u}rgen Branke and Salvatore Corrente and Salvatore Greco and Roman S{\l}owi{\'n}ski and Zielniewicz, P.},
title = {Using {Choquet} integral as preference model in interactive
evolutionary multiobjective optimization},
journal = {European Journal of Operational Research},
volume = 250,
number = 3,
pages = {884--901},
year = 2016,
doi = {10.1016/j.ejor.2015.10.027}
}
@article{BranFarSha2016cgti,
author = { J{\"u}rgen Branke and Farid, S. S. and Shah, N.},
title = {Industry 4.0: a vision for personalized medicine supply
chains?},
journal = {Cell and Gene Therapy Insights},
year = 2016,
volume = 2,
number = 2,
pages = {263--270},
doi = {10.18609/cgti.2016.027}
}
@article{BranGreSlow2015,
author = { J{\"u}rgen Branke and Salvatore Greco and Roman S{\l}owi{\'n}ski and Piotr Zielniewicz},
title = {Learning Value Functions in Interactive Evolutionary
Multiobjective Optimization},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2015,
volume = 19,
pages = {88--102},
number = 1
}
@article{BranJin2005tec,
author = { Yaochu Jin and J{\"u}rgen Branke },
title = {Evolutionary Optimization in Uncertain Environments---A
Survey},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2005,
volume = 9,
number = 5,
pages = {303--317}
}
@article{Breiman2001,
author = {Leo Breiman},
title = {Random Forests},
journal = {Machine Learning},
year = 2001,
volume = 45,
number = 1,
pages = {5--32},
doi = {10.1023/A:1010933404324}
}
@article{BriCabEmm2018maximum,
title = {Maximum volume subset selection for anchored boxes},
author = { Karl Bringmann and Cabello, Sergio and Emmerich, Michael T. M. },
journal = {Arxiv preprint arXiv:1803.00849},
year = 2018,
doi = {10.48550/arXiv.1803.00849},
abstract = {Let $B$ be a set of $n$ axis-parallel boxes in $\mathbb{R}^d$
such that each box has a corner at the origin and the other
corner in the positive quadrant of $\mathbb{R}^d$, and let
$k$ be a positive integer. We study the problem of selecting
$k$ boxes in $B$ that maximize the volume of the union of the
selected boxes. This research is motivated by applications
in skyline queries for databases and in multicriteria
optimization, where the problem is known as the
\emph{hypervolume subset selection problem}. It is known
that the problem can be solved in polynomial time in the
plane, while the best known running time in any dimension $d
\ge 3$ is $\Omega\big(\binom{n}{k}\big)$. We show that: The
problem is NP-hard already in 3 dimensions. In 3 dimensions,
we break the bound $\Omega\big(\binom{n}{k}\big)$, by
providing an $n^{O(\sqrt{k})}$ algorithm. For any constant
dimension $d$, we present an efficient polynomial-time
approximation scheme.},
keywords = {hypervolume subset selection}
}
@article{BriFri2012tcs,
author = { Karl Bringmann and Tobias Friedrich },
title = {Approximating the Least Hypervolume Contributor: {NP}-Hard in
General, But Fast in Practice},
pages = {104--116},
year = 2012,
volume = 425,
journal = {Theoretical Computer Science},
doi = {10.1016/j.tcs.2010.09.026}
}
@article{BriFri2010eff,
author = { Karl Bringmann and Tobias Friedrich },
title = {An efficient algorithm for computing hypervolume
contributions},
journal = {Evolutionary Computation},
volume = 18,
number = 3,
pages = {383--402},
year = 2010
}
@article{BriFri2014convergence,
title = {Convergence of hypervolume-based archiving algorithms},
author = { Karl Bringmann and Tobias Friedrich },
journal = {IEEE Transactions on Evolutionary Computation},
year = 2014,
number = 5,
pages = {643--657},
volume = 18,
publisher = {IEEE},
keywords = {competitive ratio},
doi = {10.1109/TEVC.2014.2341711},
annote = {Proof that all nondecreasing $(\mu + \lambda)$ archiving algorithms with
$\lambda < \mu$ are ineffective.}
}
@article{Bro1970bfgs,
author = {Broyden, Charles G.},
title = {The Convergence of a Class of Double-rank Minimization
Algorithms: 2. The New Algorithm},
journal = {IMA Journal of Applied Mathematics},
year = 1970,
volume = 6,
number = 3,
pages = {222--231},
month = sep,
annote = {One of the four papers that proposed BFGS.},
doi = {10.1093/imamat/6.3.222},
eprint = {https://academic.oup.com/imamat/article-pdf/6/3/222/1848059/6-3-222.pdf},
keywords = {BFGS}
}
@article{BroBadThiZit2013directed,
title = {Directed Multiobjective Optimization Based on the Weighted
Hypervolume Indicator},
volume = 20,
doi = {10.1002/mcda.1502},
abstract = {Recently, there has been a large interest in set-based
evolutionary algorithms for multi objective
optimization. They are based on the definition of indicators
that characterize the quality of the current population while
being compliant with the concept of Pareto-optimality. It has
been shown that the hypervolume indicator, which measures the
dominated volume in the objective space, enables the design
of efficient search algorithms and, at the same time, opens
up opportunities to express user preferences in the search by
means of weight functions. The present paper contains the
necessary theoretical foundations and corresponding
algorithms to (i) select appropriate weight functions, to
(ii) transform user preferences into weight functions and to
(iii) efficiently evaluate the weighted hypervolume indicator
through Monte Carlo sampling. The algorithm W-HypE, which
implements the previous concepts, is introduced, and the
effectiveness of the search, directed towards the user's
preferred solutions, is shown using an extensive set of
experiments including the necessary statistical performance
assessment.},
number = {5-6},
journal = {Journal of Multi-Criteria Decision Analysis},
author = { Dimo Brockhoff and Johannes Bader and Lothar Thiele and Eckart Zitzler },
year = 2013,
keywords = {hypervolume, preference-based search, multi objective
optimization, evolutionary algorithm},
pages = {291--317}
}
@article{BroCorFre2010tutorial,
author = {Brochu, Eric and Cora, Vlad and Nando de Freitas },
year = 2010,
month = dec,
title = {A Tutorial on {Bayesian} Optimization of Expensive Cost
Functions, with Application to Active User Modeling and
Hierarchical Reinforcement Learning},
journal = {Arxiv preprint arXiv:1012.2599},
url = {https://arxiv.org/abs/1012.2599}
}
@article{BroTusTusWag2016biobj,
author = { Dimo Brockhoff and Tea Tu{\v s}ar and Dejan Tu{\v s}ar and Tobias Wagner and Nikolaus Hansen and Anne Auger },
title = {Biobjective performance assessment with the {COCO} platform},
journal = {Arxiv preprint arXiv:1605.01746},
year = 2016,
doi = {10.48550/arXiv.1605.01746}
}
@article{BroWagTrau2015r2,
title = {{R2} indicator-based multiobjective search},
author = { Dimo Brockhoff and Tobias Wagner and Heike Trautmann },
journal = {Evolutionary Computation},
year = 2015,
number = 3,
pages = {369--395},
volume = 23
}
@article{BroZit2009ec,
author = { Dimo Brockhoff and Eckart Zitzler },
title = {Objective Reduction in Evolutionary Multiobjective
Optimization: Theory and Applications},
journal = {Evolutionary Computation},
volume = 17,
number = 2,
pages = {135--166},
year = 2009,
abstract = {Many-objective problems represent a major challenge in the
field of evolutionary multiobjective optimization, in terms of
search efficiency, computational cost, decision making,
visualization, and so on. This leads to various research
questions, in particular whether certain objectives can be
omitted in order to overcome or at least diminish the
difficulties that arise when many, that is, more than three,
objective functions are involved. This study addresses this
question from different perspectives. First, we investigate
how adding or omitting objectives affects the problem
characteristics and propose a general notion of conflict
between objective sets as a theoretical foundation for
objective reduction. Second, we present both exact and
heuristic algorithms to systematically reduce the number of
objectives, while preserving as much as possible of the
dominance structure of the underlying optimization
problem. Third, we demonstrate the usefulness of the proposed
objective reduction method in the context of both decision
making and search for a radar waveform application as well as
for well-known test functions.},
doi = {10.1162/evco.2009.17.2.135}
}
@article{Broyden1970bfgs,
author = {Broyden, C. G.},
title = {The Convergence of a Class of Double-rank Minimization
Algorithms 1. General Considerations},
journal = {IMA Journal of Applied Mathematics},
year = 1970,
volume = 6,
number = 1,
pages = {76--90},
month = mar,
keywords = {Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm},
abstract = {This paper presents a more detailed analysis of a class of
minimization algorithms, which includes as a special case the
DFP (Davidon-Fletcher-Powell) method, than has previously
appeared. Only quadratic functions are considered but
particular attention is paid to the magnitude of successive
errors and their dependence upon the initial matrix. On the
basis of this a possible explanation of some of the observed
characteristics of the class is tentatively suggested.},
doi = {10.1093/imamat/6.1.76}
}
@article{BruHurWer1996,
author = {Peter Brucker and Johann Hurink and Frank Werner},
title = {Improving Local Search Heuristics for some Scheduling Problems --- {Part} {I}},
journal = {Discrete Applied Mathematics},
year = 1996,
volume = 65,
number = {1--3},
pages = {97--122}
}
@article{BruHurWer1997,
author = {Peter Brucker and Johann Hurink and Frank Werner},
title = {Improving Local Search Heuristics for some Scheduling Problems --- {Part} {II}},
journal = {Discrete Applied Mathematics},
year = 1997,
volume = 72,
number = {1--2},
pages = {47--69}
}
@article{BruJacTho1999:aor,
author = {M. J. Brusco and L. W. Jacobs and G. M. Thompson},
title = {A Morphing Procedure to Supplement a Simulated
Annealing Heuristic for Cost- and
Coverage-correlated Set Covering Problems},
journal = {Annals of Operations Research},
year = 1999,
volume = 86,
pages = {611--627}
}
@article{Buc1994jors,
title = {An experimental evaluation of interactive {MCDM}
methods and the decision making process},
author = { Buchanan, John T. },
journal = {Journal of the Operational Research Society},
pages = {1050--1059},
volume = 45,
number = 9,
year = 1994
}
@article{Buc1997jors,
author = { Buchanan, John T. },
title = {A naive approach for solving {MCDM} problems: the {GUESS}
method},
journal = {Journal of the Operational Research Society},
year = 1997,
volume = 48,
pages = {202--206}
}
@article{BucCor1997anchoring,
title = {The effects of anchoring in interactive {MCDM} solution
methods},
volume = 24,
doi = {10.1016/S0305-0548(97)00014-2},
number = 10,
journal = {Computers \& Operations Research},
author = { Buchanan, John T. and Corner, James L.},
month = oct,
year = 1997,
pages = {907--918}
}
@article{BucGoo2004maxima,
author = {A. L. Buchsbaum and M. T. Goodrich},
title = {Three-Dimensional Layers of Maxima},
journal = {Algorithmica},
year = 2004,
volume = 39,
pages = {275--289}
}
@article{BulHarStr99:aor,
author = { B. Bullnheimer and Richard F. Hartl and Christine Strauss },
title = {An Improved Ant System Algorithm for the Vehicle Routing
Problem},
journal = {Annals of Operations Research},
year = 1999,
volume = 89,
pages = {319--328}
}
@article{BulHarStr99:cejore,
author = { B. Bullnheimer and Richard F. Hartl and Christine Strauss },
title = {A new rank-based version of the {Ant} {System}: {A}
computational study},
journal = {Central European Journal for Operations Research and Economics},
year = 1999,
volume = 7,
number = 1,
pages = {25--38}
}
@article{BurByk2017,
title = {The Late Acceptance Hill-Climbing Heuristic},
author = { Edmund K. Burke and Yuri Bykov },
journal = {European Journal of Operational Research},
volume = 258,
number = 1,
pages = {70--78},
year = 2017
}
@article{BurFin1983,
title = {The asymptotic probabilistic behaviour of quadratic sum assignment problems},
author = { Burkard, Rainer E. and Fincke, Ulrich},
journal = {Zeitschrift f{\"u}r Operations Research},
volume = 27,
number = 1,
pages = {73--81},
year = 1983,
publisher = {Springer}
}
@article{BurFraMos2004:joh,
author = {Luciana Buriol and Paulo M. Fran{\c c}a and Pablo Moscato },
title = {A New Memetic Algorithm for the Asymmetric Traveling Salesman Problem},
journal = {Journal of Heuristics},
year = 2004,
volume = 10,
number = 5,
pages = {483--506}
}
@article{BurGenHyd2013,
author = { Edmund K. Burke and Michel Gendreau and Matthew R. Hyde and Graham Kendall and Gabriela Ochoa and Ender {\"O}zcan and Rong Qu },
title = {Hyper-heuristics: A Survey of the State of the Art},
journal = {Journal of the Operational Research Society},
year = 2013,
volume = 64,
number = 12,
pages = {1695--1724},
doi = {10.1057/jors.2013.71}
}
@article{BurHydKen2010tec,
author = { Edmund K. Burke and Matthew R. Hyde and Graham Kendall and John R. Woodward},
journal = {IEEE Transactions on Evolutionary Computation},
title = {A Genetic Programming Hyper-Heuristic Approach for Evolving
{2-D} Strip Packing Heuristics},
year = 2010,
volume = 14,
number = 6,
pages = {942--958},
doi = {10.1109/TEVC.2010.2041061}
}
@article{BurHydKen2012ec,
title = {Automating the Packing Heuristic Design Process with Genetic
Programming},
author = { Edmund K. Burke and Matthew R. Hyde and Graham Kendall and John R. Woodward},
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author = { Josu Ceberio and Irurozki, Ekhine and Alexander Mendiburu and Jos{\'e} A. Lozano },
title = {A distance-based ranking model estimation of distribution
algorithm for the flowshop scheduling problem},
abstract = {The aim of this paper is two-fold. First, we introduce a
novel general estimation of distribution algorithm to deal
with permutation-based optimization problems. The algorithm
is based on the use of a probabilistic model for permutations
called the generalized Mallows model. In order to prove the
potential of the proposed algorithm, our second aim is to
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approach consisting of the new estimation of distribution
algorithm and a variable neighborhood search is
proposed. Conducted experiments demonstrate that the proposed
algorithm is able to outperform the state-of-the-art
approaches. Moreover, from the 220 benchmark instances
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results in 152 cases. An in-depth study of the results
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estimation of distribution algorithm to discover promising
regions in the search space.},
doi = {10.1109/TEVC.2013.2260548},
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efficient frontier associated with the standard mean-variance
portfolio optimisation model. We extend the standard model to
include cardinality constraints that limit a portfolio to
have a specified number of assets, and to impose limits on
the proportion of the portfolio held in a given asset (if any
of the asset is held). We illustrate the differences that
arise in the shape of this efficient frontier when such
constraints are present. We present three heuristic
algorithms based upon genetic algorithms, tabu search and
simulated annealing for finding the cardinality constrained
efficient frontier. Computational results are presented for
five data sets involving up to 225 assets. Scope and purpose
The standard Markowitz mean-variance approach to portfolio
selection involves tracing out an efficient frontier, a
continuous curve illustrating the tradeoff between return and
risk (variance). This frontier can be easily found via
quadratic programming. This approach is well-known and widely
applied. However, for practical purposes, it may be desirable
to limit the number of assets in a portfolio, as well as
imposing limits on the proportion of the portfolio devoted to
any particular asset. If such constraints exist, the problem
of finding the efficient frontier becomes much harder. This
paper illustrates how, in the presence of such constraints,
the efficient frontier becomes discontinuous. Three heuristic
techniques are applied to the problem of finding this
efficient frontier and computational results presented for a
number of data sets which are made publicly available.}
}
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Real-world time and Space Assembly Line Balancing
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Jussi and Sindhya, Karthik},
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author = { Tinkle Chugh and Sindhya, Karthik and Hakanen, Jussi and Kaisa Miettinen },
title = {A survey on handling computationally expensive multiobjective
optimization problems with evolutionary algorithms},
journal = {Soft Computing},
pages = {3137--3166},
volume = 23,
number = 9,
year = 2019,
abstract = {Evolutionary algorithms are widely used for solving
multiobjective optimization problems but are often criticized
because of a large number of function evaluations
needed. Approximations, especially function approximations,
also referred to as surrogates or metamodels are commonly
used in the literature to reduce the computation time. This
paper presents a survey of 45 different recent algorithms
proposed in the literature between 2008 and 2016 to handle
computationally expensive multiobjective optimization
problems. Several algorithms are discussed based on what kind
of an approximation such as problem, function or fitness
approximation they use. Most emphasis is given to function
approximation-based algorithms. We also compare these
algorithms based on different criteria such as metamodeling
technique and evolutionary algorithm used, type and
dimensions of the problem solved, handling constraints,
training time and the type of evolution control. Furthermore,
we identify and discuss some promising elements and major
issues among algorithms in the literature related to using an
approximation and numerical settings used. In addition, we
discuss selecting an algorithm to solve a given
computationally expensive multiobjective optimization problem
based on the dimensions in both objective and decision spaces
and the computation budget available.},
doi = {10.1007/s00500-017-2965-0}
}
@article{CinFerLopAlb2022irace,
author = { Christian Cintrano and Javier Ferrer and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Alba, Enrique },
title = {Hybridization of Evolutionary Operators with Elitist Iterated
Racing for the Simulation Optimization of Traffic Lights
Programs},
journal = {Evolutionary Computation},
year = 2023,
volume = 31,
number = 1,
pages = {31--51},
doi = {10.1162/evco_a_00314},
abstract = {In the traffic light scheduling problem the evaluation of
candidate solutions requires the simulation of a process
under various (traffic) scenarios. Thus, good solutions
should not only achieve good objective function values, but
they must be robust (low variance) across all different
scenarios. Previous work has shown that combining IRACE with
evolutionary operators is effective for this task due to the
power of evolutionary operators in numerical optimization. In
this paper, we further explore the hybridization of
evolutionary operators and the elitist iterated racing of
IRACE for the simulation-optimization of traffic light
programs. We review previous works from the literature to
find the evolutionary operators performing the best when
facing this problem to propose new hybrid algorithms. We
evaluate our approach over a realistic case study derived
from the traffic network of Málaga (Spain) with 275 traffic
lights that should be scheduled optimally. The experimental
analysis reveals that the hybrid algorithm comprising IRACE
plus differential evolution offers statistically better
results than the other algorithms when the budget of
simulations is low. In contrast, IRACE performs better than
the hybrids for high simulations budget, although the
optimization time is much longer.},
keywords = {irace, Simulation optimization, Uncertainty, Traffic light
planning}
}
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here as scenarios. Therefore, a meaningful and resilient
optimal solution must be such that it remains feasible under
all scenarios and performs close to an individual optimal
solution corresponding to each scenario. Despite its
practical importance, multi-scenario consideration has
received a lukewarm attention, particularly in the context of
multi-objective optimization. The usual practice is to
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goal to suggest a new and potential population-based method
for handling multiple scenarios by defining scenario-wise
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operators. To evaluate, the proposed method is applied to a
number of numerical test problems and engineering design
problems with a detail explanation of the obtained results
and compared with an existing method. This first systematic
evolutionary based multi-scenario, multiobjective,
optimization study on numerical problems indicates that
multiple scenarios can be handled in an integrated manner
using an EMO framework to find a well-balanced compromise set
of solutions to multiple scenarios and maintain a tradeoff
among multiple objectives. In comparison to an existing
serial multiple optimization approach, the proposed approach
finds a set of compromised trade-off solutions
simultaneously. An achievement of multi-objective trade-off
and multi-scenario trade-off is algorithmically challenging,
but due to its practical appeal, further research and
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year = 2021,
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keywords = {BOPS, CEGO}
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author = { Marcelo {De Souza} and Marcus Ritt and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and P{\'e}rez C{\'a}ceres, Leslie },
title = {{\softwarepackage{ACVIZ}}: A Tool for the Visual Analysis of
the Configuration of Algorithms with {\rpackage{irace}}},
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year = 2021,
doi = {10.1016/j.orp.2021.100186},
supplement = {https://zenodo.org/record/4714582},
abstract = {This paper introduces acviz, a tool that helps to analyze the
automatic configuration of algorithms with irace. It provides
a visual representation of the configuration process,
allowing users to extract useful information, e.g. how the
configurations evolve over time. When test data is available,
acviz also shows the performance of each configuration on the
test instances. Using this visualization, users can analyze
and compare the quality of the resulting configurations and
observe the performance differences on training and test
instances.},
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pages = 100186
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planning, Robust optimization, Simulation-based optimization,
Uncertainty modelling}
}
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title = {Integrating meta-heuristics, simulation and exact techniques
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planning, Simulation-based optimization, Uncertainty
modelling}
}
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year = 2021,
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abstract = {Automatic configuration (AC) methods are increasingly used to
tune and design optimisation algorithms for problems with
multiple objectives. Most AC methods use unary quality
indicators, which assign a single scalar value to an
approximation to the Pareto front, to compare the performance
of different optimisers. These quality indicators, however,
imply preferences beyond Pareto-optimality that may differ
from those of the decision maker (DM). Although it is
possible to incorporate DM's preferences into quality
indicators, e.g., by means of the weighted hypervolume
indicator (HV$^w$), expressing preferences in terms of weight
function is not always intuitive nor an easy task for a DM,
in particular, when comparing the stochastic outcomes of
several algorithm configurations. A more visual approach to
compare such outcomes is the visualisation of their empirical
attainment functions (EAFs) differences. This paper proposes
using such visualisations as a way of eliciting information
about regions of the objective space that are preferred by
the DM. We present a method to convert the information about
EAF differences into a HV$^w$ that will assign higher quality
values to approximation fronts that result in EAF differences
preferred by the DM. We show that the resulting HV$^w$ may be
used by an AC method to guide the configuration of
multi-objective optimisers according to the preferences of
the DM. We evaluate the proposed approach on a well-known
benchmark problem. Finally, we apply our approach to
re-configuring, according to different DM's preferences, a
multi-objective optimiser tackling a real-world production
planning problem arising in the manufacturing industry.},
supplement = {https://doi.org/10.5281/zenodo.3749288}
}
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Carion and Andreas Krause and Angela P. Schoellig},
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Gaussian Process, Particle Swarm Optimization, Policy Search,
Reinforcement learning},
abstract = {Tuning controller parameters is a recurring and
time-consuming problem in control. This is especially true in
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it has been shown that constrained Bayesian optimization is a
promising approach to automate the tuning process without
risking system failures during the optimization
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expensive for tuning more than a couple of parameters. In
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perform constrained Bayesian optimization in high-dimensional
parameter spaces by using an adaptive discretization based on
particle swarms. We apply the method to the tuning problem of
an L1 adaptive controller on a quadrotor vehicle and show
that we can reliably and automatically tune parameters in
experiments.}
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}
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title = {Interactive evolutionary multi-objective optimization for
quasi-concave preference functions},
journal = {European Journal of Operational Research},
volume = 206,
number = 2,
pages = {417--425},
year = 2010,
doi = {10.1016/j.ejor.2010.02.027},
author = {John W. Fowler and Esma S. Gel and Murat K{\"o}ksalan and Pekka Korhonen and Jon L. Marquis and Wallenius, Jyrki },
keywords = {Interactive optimization, Multi-objective optimization,
Evolutionary optimization, Knapsack problem},
abstract = {We present a new hybrid approach to interactive evolutionary
multi-objective optimization that uses a partial preference
order to act as the fitness function in a customized genetic
algorithm. We periodically send solutions to the decision
maker (DM) for her evaluation and use the resulting
preference information to form preference cones consisting of
inferior solutions. The cones allow us to implicitly rank
solutions that the DM has not considered. This technique
avoids assuming an exact form for the preference function,
but does assume that the preference function is
quasi-concave. This paper describes the genetic algorithm and
demonstrates its performance on the multi-objective knapsack
problem.}
}
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volume = 41,
number = 2,
pages = {47--67},
year = 1993,
publisher = {Springer}
}
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author = {Peter I. Frazier},
title = {A Tutorial on {Bayesian} Optimization},
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doi = {10.48550/arXiv.1807.02811}
}
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title = {Empirical Analysis of Stochastic Local Search Behaviour: Connecting Structure, Components and Landscape},
author = { Alberto Franzin },
journal = {{4OR}: A Quarterly Journal of Operations Research},
year = 2022,
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}
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author = {G. Francesca and M. Brambilla and
A. Brutschy and Vito Trianni and Mauro Birattari },
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of Control Software for Robot Swarms},
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author = {Francesca, Gianpiero and Brambilla, Manuele and Brutschy,
Arne and Garattoni, Lorenzo and Miletitch, Roman and
Podevijn, Gaetan and Reina, Andreagiovanni and Soleymani,
Touraj and Salvaro, Mattia and Pinciroli, Carlo and Mascia,
Franco and Vito Trianni and Mauro Birattari },
title = {{AutoMoDe-Chocolate}: Automatic Design of Control Software
for Robot Swarms},
year = 2015,
journal = {Swarm Intelligence},
doi = {10.1007/s11721-015-0107-9},
keywords = {Swarm robotics; Automatic design; AutoMoDe},
language = {English}
}
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title = {A Review and Classification of Heuristics for Permutation Flow-shop Scheduling with Makespan Objective},
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year = 2004,
number = 12,
pages = {1243--1255},
volume = 55
}
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title = {Effect of Transformations of Numerical Parameters
in Automatic Algorithm Configuration},
journal = {Optimization Letters},
year = 2018,
volume = 12,
number = 8,
pages = {1741--1753},
doi = {10.1007/s11590-018-1240-3}
}
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author = { Alberto Franzin and Sambo, Francesco and Di Camillo, Barbara},
title = {\rpackage{bnstruct}: an {R} package for {Bayesian} Network structure learning
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author = { Alberto Franzin and Thomas St{\"u}tzle },
title = {Revisiting Simulated Annealing: A Component-Based Analysis},
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pages = {191--206},
year = 2019,
doi = {10.1016/j.cor.2018.12.015}
}
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author = { Alberto Franzin and Thomas St{\"u}tzle },
title = {A Landscape-based Analysis of Fixed Temperature and Simulated Annealing},
year = 2023,
journal = {European Journal of Operational Research},
volume = 304,
number = 2,
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doi = {10.1016/j.ejor.2022.04.014}
}
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title = {Automated Discovery of Local Search Heuristics for
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year = 2008,
pages = {31--61},
volume = 16,
doi = {10.1162/evco.2008.16.1.31},
abstract = {The development of successful metaheuristic
algorithms such as local search for a difficult
problem such as satisfiability testing ({SAT)} is a
challenging task. We investigate an evolutionary
approach to automating the discovery of new local
search heuristics for {SAT}. We show that several
well-known {SAT} local search algorithms such as
Walksat and Novelty are composite heuristics that
are derived from novel combinations of a set of
building blocks. Based on this observation, we
developed {CLASS}, a genetic programming system that
uses a simple composition operator to automatically
discover {SAT} local search heuristics. New
heuristics discovered by {CLASS} are shown to be
competitive with the best Walksat variants,
including Novelty+. Evolutionary algorithms have
previously been applied to directly evolve a
solution for a particular {SAT} instance. We show
that the heuristics discovered by {CLASS} are also
competitive with these previous, direct evolutionary
approaches for {SAT}. We also analyze the local
search behavior of the learned heuristics using the
depth, mobility, and coverage metrics proposed by
Schuurmans and Southey.}
}
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in parallel, while a model incrementally learns their runtime
distributions.}
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among the given objective functions there are some which can
be deleted without influencing the set E of all efficient
solutions. Such objectives are said to be
redundant. Introducing systems of objective functions which
realize their individual optimum in a single vertex of the
polyhedron generated by the restriction set, the notion of
relative or absolute redundant objectives is defined. A
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redundant objectives is developed. A method for determining
all the relative and absolute redundant objectives, based on
this theory, is given. Illustrative examples demonstrate the
procedure.}
}
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optimization,Realistic traffic instances,SUMO microscopic
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second half of the 20th century, the fields of artificial
intelligence (AI), cognitive science, and neuroscience are
reconverging on a shared view of the computational
foundations of intelligence that promotes valuable
cross-disciplinary exchanges on questions, methods, and
results. We chart advances over the past several decades that
address challenges of perception and action under uncertainty
through the lens of computation. Advances include the
development of representations and inferential procedures for
large-scale probabilistic inference and machinery for
enabling reflection and decisions about tradeoffs in effort,
precision, and timeliness of computations. These tools are
deployed toward the goal of computational rationality:
identifying decisions with highest expected utility, while
taking into consideration the costs of computation in complex
real-world problems in which most relevant calculations can
only be approximated. We highlight key concepts with examples
that show the potential for interchange between computer
science, cognitive science, and neuroscience.},
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journal = {Science}
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quadratic problems},
author = { Fred Glover and L{\"u}, Zhipeng and Jin-Kao Hao },
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}
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title = {Recovery-to-optimality: A new two-stage approach to
robustness with an application to aperiodic timetabling},
author = {Goerigk, Marc and Sch{\"o}bel, Anita },
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}
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author = {Donald Goldfarb},
title = {A Family of Variable-Metric Methods Derived by Variational
Means},
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year = 1970,
volume = 24,
number = 109,
pages = {23--26},
annote = {One of the four papers that proposed BFGS.},
publisher = {American Mathematical Society},
eprint = {http://www.jstor.org/stable/2004873},
keywords = {BFGS}
}
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title = {Probability matching, the magnitude of reinforcement, and
classifier system bidding},
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title = {The optimization ordering model for intuitionistic fuzzy
preference relations with utility functions},
journal = {Knowledge-Based Systems},
volume = 162,
pages = {174--184},
year = 2018,
annote = {Special Issue on intelligent decision-making and consensus
under uncertainty in inconsistent and dynamic environments},
issn = {0950-7051},
doi = {10.1016/j.knosys.2018.07.012},
author = {Zaiwu Gong and Ning Zhang and Francisco Chiclana},
keywords = {Intuitionistic fuzzy preference relation, Utility function,
Ranking, Multiplicative consistency, Non-archimedean
infinitesimal},
abstract = {Intuitionistic fuzzy sets describe information from the three
aspects of membership degree, non-membership degree and
hesitation degree, which has more practical significance when
uncertainty pervades qualitative decision problems. In this
paper, we investigate the problem of ranking intuitionistic
fuzzy preference relations (IFPRs) based on various
non-linear utility functions. First, we transform IFPRs into
their isomorphic interval-value fuzzy preference relations
(IVFPRs), and utilise non-linear utility functions, such as
parabolic, S-shaped, and hyperbolic absolute risk aversion,
to fit the true value of a decision-maker's
judgement. Ultimately, the optimization ordering models for
the membership and non-membership of IVFPRs based on utility
function are constructed, with objective function aiming at
minimizing the distance deviation between the multiplicative
consistency ideal judgment and the actual judgment,
represented by utility function, subject to the
decision-maker's utility constraints. The proposed models
ensure that more factual and optimal ranking of alternative
is acquired, avoiding information distortion caused by the
operations of intervals. Second, by introducing a
non-Archimedean infinitesimal, we establish the optimization
ordering model for IFPRs with the priority of utility or
deviation, which realises the goal of prioritising solutions
under multi-objective programming. Subsequently, we verify
that a close connection exists between the ranking for
membership and non-membership degree IVFPRs. Comparison
analyses with existing approaches are summarized to
demonstrate that the proposed models have advantage in
dealing with group decision making problems with IFPRs.}
}
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author = {Gorski, Jochen and Kathrin Klamroth and Ruzika, Stefan},
journal = {Journal of Optimization Theory and Applications},
number = 3,
pages = {475--497},
publisher = {Springer},
title = {Connectedness of Efficient Solutions in Multiple Objective
Combinatorial Optimization},
volume = 150,
year = 2011,
doi = {10.1007/s10957-011-9849-8}
}
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}
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author = {N. I. M. Gould and D. Orban and P. L. Toint},
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}
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A Case Study},
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volume = 4,
pages = {365--396},
annote = {Earliest hyper-heuristic?}
}
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author = {Robert B. Gramacy and Lee, Herbert K. H.},
title = {Bayesian Treed {Gaussian} Process Models With an Application
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keywords = {Treed-GP}
}
@article{GraJuaLou2016,
author = {Alex Grasas and Angel A. Juan and Helena {Ramalhinho Louren{\c c}o} },
title = {{SimILS}: A Simulation-based Extension of the Iterated Local Search Metaheuristic for Stochastic Combinatorial Optimization},
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volume = 10,
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}
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author = {M. Gravel and W. L. Price and Caroline Gagn{\'e} },
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multiple objective ant colony optimization
metaheuristic},
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number = 1,
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}
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author = {John J. Grefenstette},
title = {Optimization of Control Parameters for Genetic Algorithms},
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}
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author = { Salvatore Greco and Kadzi{\'n}ski, Mi{\l}osz and Vincent Mousseau and Roman S{\l}owi{\'n}ski },
title = {{ELECTRE}$^\mathrm{{GKMS}}$: Robust ordinal regression for outranking methods},
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number = 1,
pages = {118--135},
year = 2011
}
@article{GreMouSlo2014ejor,
author = { Salvatore Greco and Vincent Mousseau and Roman S{\l}owi{\'n}ski },
title = {Robust ordinal regression for value functions handling interacting
criteria},
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}
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title = {Modelling science trustworthiness under publish or perish pressure},
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volume = 5,
pages = {171511},
year = 2018
}
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author = { Andrea Grosso and Federico {Della Croce} and R. Tadei},
title = {An Enhanced Dynasearch Neighborhood for the
Single-Machine Total Weighted Tardiness Scheduling
Problem},
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number = 1,
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}
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author = { Andrea Grosso and A. R. M. J. U. Jamali and Marco Locatelli},
title = {Finding Maximin Latin Hypercube Designs by Iterated Local Search Heuristics},
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year = 2009,
volume = 197,
number = 2,
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}
@article{GroKayKnoVan2013,
title = {The "big data" revolution in healthcare},
author = {Groves, Peter and Kayyali, Basel and Knott, David and Van
Kuiken, Steve},
journal = {McKinsey Quarterly},
volume = 2,
year = 2013
}
@article{GroMan2019hvsubset,
title = {Hypervolume subset selection with small subsets},
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year = 2019,
number = 4,
pages = {611--637},
volume = 27
}
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author = { Viviane {Grunert da Fonseca} and Carlos M. Fonseca },
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function and the hitting function for random closed sets},
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year = 2002,
volume = 57,
number = 2,
pages = {179--182},
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}
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title = {The Hypervolume Indicator: Computational Problems and Algorithms},
author = { Andreia P. Guerreiro and Carlos M. Fonseca and Lu{\'i}s Paquete },
journal = {{ACM} Computing Surveys},
year = 2021,
number = 6,
pages = {1--42},
volume = 54
}
@article{GueManFig2021exacthv,
author = { Andreia P. Guerreiro and Vasco Manquinho and Jos{\'e} Rui Figueira },
title = {Exact hypervolume subset selection through incremental
computations},
doi = {10.1016/j.cor.2021.105471},
year = 2021,
month = dec,
volume = 136,
pages = {105--471},
journal = {Computers \& Operations Research}
}
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author = {Gonzalo Guill{\'e}n-Gos{\'a}lbez},
title = {A novel {MILP}-based objective reduction method for
multi-objective optimization: Application to environmental
problems},
journal = {Computers \& Chemical Engineering},
volume = 35,
number = 8,
pages = {1469--1477},
year = 2011,
issn = {0098-1354},
doi = {10.1016/j.compchemeng.2011.02.001},
keywords = {Environmental engineering, Life cycle assessment,
Multi-objective optimization, Objective reduction},
abstract = {Multi-objective optimization has recently emerged as a useful
technique in sustainability analysis, as it can assist in the
study of optimal trade-off solutions that balance several
criteria. The main limitation of multi-objective optimization
is that its computational burden grows in size with the
number of objectives. This computational barrier is critical
in environmental applications in which decision-makers seek
to minimize simultaneously several environmental indicators
of concern. With the aim to overcome this limitation, this
paper introduces a systematic method for reducing the number
of objectives in multi-objective optimization with emphasis
on environmental problems. The approach presented relies on a
novel mixed-integer linear programming formulation that
minimizes the error of omitting objectives. We test the
capabilities of this technique through two environmental
problems of different nature in which we attempt to minimize
a set of life cycle assessment impacts. Numerical examples
demonstrate that certain environmental metrics tend to behave
in a non-conflicting manner, which makes it possible to
reduce the dimension of the problem without losing
information.}
}
@article{GunGilAha2018repro,
author = {Odd Erik Gundersen and Yolanda Gil and David W. Aha},
title = {On Reproducible {AI}: Towards Reproducible Research, Open
Science, and Digital Scholarship in {AI} Publications},
doi = {10.1609/aimag.v39i3.2816},
year = 2018,
month = sep,
publisher = {Association for the Advancement of Artificial Intelligence
({AAAI})},
volume = 39,
number = 3,
pages = {56--68},
journal = {{AI} Magazine},
annote = {The reproducibility guidelines can be found here:
\url{https://folk.idi.ntnu.no/odderik/reproducibility_guidelines.pdf}
and a short how-to can be found here:
\url{https://folk.idi.ntnu.no/odderik/reproducibility_guidelines_how_to.html}}
}
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title = {A Hybridized {Lagrangian} Relaxation and Simulated Annealing
Method for the Course Timetabling Problem},
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year = 2012,
publisher = {Elsevier}
}
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}
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author = { Gutjahr, Walter J. },
title = {A {Graph}-based {Ant} {System} and its Convergence},
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year = 2000,
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number = 8,
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}
@article{Gut2002:ipl,
author = { Gutjahr, Walter J. },
title = {{ACO} Algorithms with Guaranteed Convergence to the
Optimal Solution},
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year = 2002,
volume = 82,
number = 3,
pages = {145--153}
}
@article{Gut2006:mcap,
author = { Gutjahr, Walter J. },
title = {On the finite-time dynamics of ant colony
optimization},
journal = {Methodology and Computing in Applied Probability},
year = 2006,
volume = 8,
number = 1,
pages = {105--133}
}
@article{Gut2007:swarm,
author = { Gutjahr, Walter J. },
title = {Mathematical runtime analysis of {ACO} algorithms:
survey on an emerging issue},
journal = {Swarm Intelligence},
volume = 1,
number = 1,
year = 2007,
pages = {59--79}
}
@article{Gut2007cor,
title = {An {ACO} algorithm for a dynamic regional nurse-scheduling
problem in {Austria} },
journal = {Computers \& Operations Research},
volume = 34,
number = 3,
pages = {642--666},
year = 2007,
anote = {Logistics of Health Care Management Part Special Issue:
Logistics of Health Care Management },
doi = {10.1016/j.cor.2005.03.018},
author = { Gutjahr, Walter J. and Marion S. Rauner},
abstract = {To the best of our knowledge, this paper describes the first
ant colony optimization (ACO) approach applied to nurse
scheduling, analyzing a dynamic regional problem which is
currently under discussion at the Vienna hospital
compound. Each day, pool nurses have to be assigned for the
following days to public hospitals while taking into account
a variety of soft and hard constraints regarding working date
and time, working patterns, nurses qualifications, nurses
and hospitals preferences, as well as costs. Extensive
computational experiments based on a four week simulation
period were used to evaluate three different scenarios
varying the number of nurses and hospitals for six different
hospitals demand intensities. The results of our simulations
and optimizations reveal that the proposed {ACO} algorithm
achieves highly significant improvements compared to a greedy
assignment algorithm.}
}
@article{Gut2008:cor,
author = { Gutjahr, Walter J. },
title = {First steps to the runtime complexity analysis of ant colony
optimization},
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volume = 35,
number = 9,
year = 2008,
pages = {2711--2727}
}
@article{GutSeb2008,
author = { Gutjahr, Walter J. and G. Sebastiani},
title = {Runtime analysis of ant colony optimization with best-so-far
reinforcement},
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year = 2008,
volume = 10,
number = 3,
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}
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author = {Gutin, Gregory and Yeo, Anders and Zverovich, Alexey},
title = {Traveling salesman should not be greedy: domination analysis
of greedy-type heuristics for the {TSP}},
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volume = 117,
number = {1--3},
year = 2002
}
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title = {Gene selection for cancer classification using support vector
machines},
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Vapnik, Vladimir},
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number = 1,
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year = 2002,
publisher = {Springer},
keywords = {recursive feature elimination}
}
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title = {An adaptive {Metropolis} algorithm},
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year = 2013
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author = { David Hadka and Patrick M. Reed },
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system identification and system optimization},
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title = {On the impact of the performance metric on efficient
algorithm configuration},
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year = 2022,
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publisher = {Elsevier {BV}},
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title = {Path dependence in {Operational} {Research}--{How} the
modeling process can influence the results},
author = { H{\"a}m{\"a}l{\"a}inen, Raimo P. and Lahtinen, Tuomas J.},
doi = {10.1016/j.orp.2016.03.001},
abstract = {In Operational Research practice there are almost always
alternative paths that can be followed in the modeling and
problem solving process. Path dependence refers to the impact
of the path on the outcome of the process. The steps of the
path include, e.g. forming the problem solving team, the
framing and structuring of the problem, the choice of model,
the order in which the different parts of the model are
specified and solved, and the way in which data or
preferences are collected. We identify and discuss seven
possibly interacting origins or drivers of path dependence:
systemic origins, learning, procedure, behavior, motivation,
uncertainty, and external environment. We provide several
ideas on how to cope with path dependence.},
journal = {Operations Research Perspectives},
month = jan,
volume = 3,
year = 2016,
keywords = {Behavioral Biases, Behavioral Operational Research, Ethics in
modelling, OR practice, Path dependence, Systems perspective},
pages = {14--20}
}
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author = { H{\"a}m{\"a}l{\"a}inen, Raimo P. and Luoma, Jukka and Saarinen, Esa},
title = {On the importance of behavioral operational research: {The}
case of understanding and communicating about dynamic
systems},
volume = 228,
shorttitle = {On the importance of behavioral operational research},
doi = {10.1016/j.ejor.2013.02.001},
abstract = {We point out the need for Behavioral Operational Research
(BOR) in advancing the practice of OR. So far, in OR
behavioral phenomena have been acknowledged only in
behavioral decision theory but behavioral issues are always
present when supporting human problem solving by
modeling. Behavioral effects can relate to the group
interaction and communication when facilitating with OR
models as well as to the possibility of procedural mistakes
and cognitive biases. As an illustrative example we use well
known system dynamics studies related to the understanding of
accumulation. We show that one gets completely opposite
results depending on the way the phenomenon is described and
how the questions are phrased and graphs used. The results
suggest that OR processes are highly sensitive to various
behavioral effects. As a result, we need to pay attention to
the way we communicate about models as they are being
increasingly used in addressing important problems like
climate change.},
number = 3,
journal = {European Journal of Operational Research},
month = aug,
year = 2013,
pages = {623--634}
}
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author = { Nikolaus Hansen and Anne Auger and Dimo Brockhoff and Tea Tu{\v s}ar },
title = {Anytime Performance Assessment in Blackbox Optimization
Benchmarking},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2022,
volume = 26,
number = 6,
pages = {1293--1305},
month = dec,
doi = {10.1109/TEVC.2022.3210897}
}
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title = {{COCO}: A platform for comparing continuous optimizers in a
black-box setting},
author = { Nikolaus Hansen and Anne Auger and Mersmann, Olaf and Tea Tu{\v s}ar and Dimo Brockhoff },
journal = {Arxiv preprint arXiv:1603.08785},
year = 2016,
note = {Published as \cite{HanAugMer2020coco}}
}
@article{HanAugMer2020coco,
title = {{COCO}: A platform for comparing continuous optimizers in a
black-box setting},
author = { Nikolaus Hansen and Anne Auger and Ros, Raymond and Mersmann,
Olaf and Tea Tu{\v s}ar and Dimo Brockhoff },
journal = {Optimization Methods and Software},
pages = {1--31},
year = 2020,
volume = 36,
number = 1,
doi = {10.1080/10556788.2020.1808977},
publisher = {Taylor \& Francis}
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year = 2001
}
@article{HanOst2001ec,
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keywords = {CMA-ES}
}
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author = { Nikolaus Hansen and Raymond Ros and Nikolaus Mauny and Marc Schoenauer and Anne Auger },
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title = {A multiobjective evolutionary algorithm for approximating the
efficient set},
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}
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single machine. A setup is required whenever a product type
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{NP-hard,} we solve it using four major metaheuristics and
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colony optimization. These are selected to represent various
characteristics of metaheuristics: nature-inspired
vs. artificially created, population-based vs. local search,
etc. A set of problems is generated to compare the solution
quality and computational efforts of these heuristics.
Results of the experimentation show that tabu search and ant
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annealing performs best in small-size problems. Some
conclusions are also drawn on the interactions between
various problem parameters and the performance of the
heuristics.}
}
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algorithm's runtime as a function of problem-specific
instance features. Such models have important applications to
algorithm analysis, portfolio-based algorithm selection, and
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studied for building such models. Here, we describe
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of models, and---perhaps most importantly---a much more thorough
treatment of algorithm parameters as model inputs. We also
comprehensively describe new and existing features for
predicting algorithm runtime for propositional satisfiability
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programming (MIP) problems. We evaluate these innovations
through the largest empirical analysis of its kind, comparing
to a wide range of runtime modelling techniques from the
literature. Our experiments consider 11 algorithms and 35
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SAT, MIP, and TSP instances, with the least structured having
been generated uniformly at random and the most structured
having emerged from real industrial applications. Overall, we
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title = {{IOHexperimenter}: Benchmarking Platform for Iterative
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class of objective-wise uncertain multi-objective
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optimization is discussed along with their strengths
and weaknesses. The proposed method is an
evolutionary algorithm with two distinct features:
(1) The search is restricted to feasible region
only; and (2) it utilizes a floating point decision
variable rather than integer or binary as is the
case with most other similar approaches. A numerical
example is presented as a basis for verification of
the proposed method and its comparison with the
existing solver that utilizes the nonlinear
Newtonian search. The proposed method provides
promising improvements in terms of optimality when
compared to the widespread gradient search methods
because it does not involve evaluation of the
gradient of the objective function. It also provides
potential to improve the performance of previous
evolutionary programs because it restricts the
search to the feasible region, thus eliminating
large overhead associated with generation and
inspection of solutions that are
infeasible. Comparison of the two solutions revealed
improvement of the solution in favor of the proposed
algorithm, which ranged up to 6\% depending on the
initial values of the decision variables in the
Newtonian search. The proposed method was not
sensitive to the starting value of the decision
variables.}
}
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abstract = {In this paper we present the R package PerMallows, which is a
complete toolbox to work with permutations, distances and
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Mallows and the Generalized Mallows models. The Mallows model
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langid = {english},
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author = {Maryam Karimi-Mamaghan and Mehrdad Mohammadi and Patrick
Meyer and Amir Mohammad Karimi-Mamaghan and Talbi, El-Ghazali },
title = {Machine learning at the service of meta-heuristics for
solving combinatorial optimization problems: A
state-of-the-art},
journal = {European Journal of Operational Research},
year = 2022,
volume = 296,
number = 2,
pages = {393--422},
doi = {10.1016/j.ejor.2021.04.032},
keywords = {Meta-heuristics, Machine learning, Combinatorial optimization
problems, State-of-the-art},
abstract = {In recent years, there has been a growing research interest
in integrating machine learning techniques into
meta-heuristics for solving combinatorial optimization
problems. This integration aims to lead meta-heuristics
toward an efficient, effective, and robust search and improve
their performance in terms of solution quality, convergence
rate, and robustness. Since various integration methods with
different purposes have been developed, there is a need to
review the recent advances in using machine learning
techniques to improve meta-heuristics. To the best of our
knowledge, the literature is deprived of having a
comprehensive yet technical review. To fill this gap, this
paper provides such a review on the use of machine learning
techniques in the design of different elements of
meta-heuristics for different purposes including algorithm
selection, fitness evaluation, initialization, evolution,
parameter setting, and cooperation. First, we describe the
key concepts and preliminaries of each of these ways of
integration. Then, the recent advances in each way of
integration are reviewed and classified based on a proposed
unified taxonomy. Finally, we provide a technical discussion
on the advantages, limitations, requirements, and challenges
of implementing each of these integration ways, followed by
promising future research directions.}
}
@article{KarShiDai05:medicine,
title = {Prediction of {MHC} class {II} binders using the ant colony
search strategy},
author = {Karpenko, Oleksiy and Shi, Jianming and Dai, Yang},
journal = {Artificial Intelligence in Medicine},
volume = 35,
number = 1,
pages = {147--156},
year = 2005
}
@article{KarTas2014,
author = {Korhan Karabulut and Fatih M. Tasgetiren},
title = {A Variable Iterated Greedy Algorithm for the Traveling Salesman Problem with Time Windows},
journal = {Information Sciences},
year = 2014,
volume = 279,
pages = {383--395}
}
@article{KasNatRee2017ems,
title = {Many objective robust decision making for complex
environmental systems undergoing change},
author = { Kasprzyk, Joseph R. and Nataraj, Shanthi and Patrick M. Reed and Lempert,
Robert J.},
journal = {Environmental Modelling \& Software},
volume = 42,
pages = {55--71},
year = 2013,
keywords = {scenario-based}
}
@article{KasReeCha2012ems,
title = {Many-objective de {Novo} water supply portfolio planning
under deep uncertainty},
author = { Kasprzyk, Joseph R. and Patrick M. Reed and Characklis, Gregory W. and
Kirsch, Brian R.},
journal = {Environmental Modelling \& Software},
volume = 34,
pages = {87--104},
year = 2012,
keywords = {scenario-based}
}
@article{KazCohJea2020,
author = {Artem Kaznatcheev and David A. Cohen and Peter Jeavons},
title = {Representing Fitness Landscapes by Valued Constraints to Understand
the Complexity of Local Search},
journal = {Journal of Artificial Intelligence Research},
volume = 69,
pages = {1077--1102},
year = 2020,
doi = {10.1613/jair.1.12156}
}
@article{KeArcFen08,
author = {Liangjun Ke and Claudia Archetti and Zuren Feng},
title = {Ants can solve the team orienteering problem},
volume = 54,
number = 3,
journal = {Computers and Industrial Engineering},
year = 2008,
pages = {648--665},
doi = {10.1016/j.cie.2007.10.001},
abstract = {The team orienteering problem {(TOP)} involves
finding a set of paths from the starting point to
the ending point such that the total collected
reward received from visiting a subset of locations
is maximized and the length of each path is
restricted by a pre-specified limit. In this paper,
an ant colony optimization {(ACO)} approach is
proposed for the team orienteering problem. Four
methods, i.e., the sequential,
deterministic-concurrent and random-concurrent and
simultaneous methods, are proposed to construct
candidate solutions in the framework of {ACO}. We
compare these methods according to the results
obtained on well-known problems from the
literature. Finally, we compare the algorithm with
several existing algorithms. The results show that
our algorithm is promising.},
keywords = {Ant colony optimization, Ant system, Heuristics,
Team orienteering problem}
}
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author = {R. L. Keeney},
title = {Analysis of preference dependencies among objectives},
journal = {Operations Research},
year = 1981,
volume = 29,
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@article{KenBaiBla2016good,
author = { Graham Kendall and Ruibin Bai and Jacek B{\l}azewicz and Patrick {De Causmaecker} and Michel Gendreau and Robert John and Jiawei Li and Barry McCollum and Erwin Pesch and Rong Qu and Nasser Sabar and Vanden Berghe, Greet and Angelina Yee},
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journal = {Journal of the Operational Research Society},
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@article{KerHooNeuTra2019,
author = { Pascal Kerschke and Holger H. Hoos and Frank Neumann and Heike Trautmann },
title = {Automated Algorithm Selection: Survey and Perspectives},
journal = {Evolutionary Computation},
volume = 27,
number = 1,
pages = {3--45},
year = 2019,
doi = {10.1162/evco_a_00242},
month = mar
}
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author = {B. W. Kernighan and S. Lin},
title = {An Efficient Heuristic Procedure for Partitioning
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@article{KerTra2019,
author = { Pascal Kerschke and Heike Trautmann },
title = {Automated Algorithm Selection on Continuous Black-Box
Problems by Combining Exploratory Landscape Analysis and
Machine Learning},
journal = {Evolutionary Computation},
volume = 27,
number = 1,
pages = {99--127},
year = 2019,
doi = {10.1162/evco_a_00236},
abstract = {In this article, we build upon previous work on designing
informative and efficient Exploratory Landscape Analysis
features for characterizing problems' landscapes and show
their effectiveness in automatically constructing algorithm
selection models in continuous black-box optimization
problems. Focusing on algorithm performance results of the
COCO platform of several years, we construct a representative
set of high-performing complementary solvers and present an
algorithm selection model that, compared to the portfolio's
single best solver, on average requires less than half of the
resources for solving a given problem. Therefore, there is a
huge gain in efficiency compared to classical ensemble
methods combined with an increased insight into problem
characteristics and algorithm properties by using informative
features. The model acts on the assumption that the function
set of the Black-Box Optimization Benchmark is representative
enough for practical applications. The model allows for
selecting the best suited optimization algorithm within the
considered set for unseen problems prior to the optimization
itself based on a small sample of function evaluations. Note
that such a sample can even be reused for the initial
population of an evolutionary (optimization) algorithm so
that even the feature costs become negligible. }
}
@article{KerWanPreuGrim2019search,
doi = {10.1162/evco_a_00234},
year = 2019,
publisher = {MIT Press},
volume = 27,
number = 4,
pages = {577--609},
author = { Pascal Kerschke and Wang, Hao and Mike Preuss and Christian
Grimme and Andr{\'{e}} H. Deutz and Heike Trautmann and Emmerich, Michael T. M. },
title = {Search Dynamics on Multimodal Multiobjective Problems},
journal = {Evolutionary Computation}
}
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doi = {10.1207/s15327957pspr0203_4},
year = 1998,
month = aug,
publisher = {{SAGE} Publications},
volume = 2,
number = 3,
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author = {Norbert L. Kerr},
title = {{HARKing}: Hypothesizing After the Results are Known},
journal = {Personality and Social Psychology Review}
}
@article{KhuXuHooLey16:aij,
author = { KhudaBukhsh, A. R. and Lin Xu and Holger H. Hoos and Kevin Leyton-Brown },
title = {{SATenstein}: Automatically Building Local Search {SAT}
{Solvers} from {Components}},
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year = 2016,
volume = 232,
pages = {20--42},
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}
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author = {Philip Kilby and Tommaso Urli},
title = {Fleet design optimisation from historical data using constraint programming and large neighbourhood search},
journal = {Constraints},
year = 2015,
pages = {1--20},
publisher = {Springer, US},
doi = {10.1007/s10601-015-9203-0},
keywords = {F-race}
}
@article{Kim1993,
author = {Kim, Yeong-Dae},
title = {Heuristics for Flowshop Scheduling Problems Minimizing Mean
Tardiness},
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year = 1993,
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@article{KimAllLop2020arxiv,
author = { Kim, Youngmin and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Safe Learning and Optimization Techniques: Towards a Survey
of the State of the Art},
journal = {Arxiv preprint arXiv:2101.09505 [cs.LG]},
year = 2020,
url = {https://arxiv.org/abs/2101.09505},
abstract = {Safe learning and optimization deals with learning and
optimization problems that avoid, as much as possible, the
evaluation of non-safe input points, which are solutions,
policies, or strategies that cause an irrecoverable loss
(e.g., breakage of a machine or equipment, or life
threat). Although a comprehensive survey of safe
reinforcement learning algorithms was published in 2015, a
number of new algorithms have been proposed thereafter, and
related works in active learning and in optimization were not
considered. This paper reviews those algorithms from a number
of domains including reinforcement learning, Gaussian process
regression and classification, evolutionary algorithms, and
active learning. We provide the fundamental concepts on which
the reviewed algorithms are based and a characterization of
the individual algorithms. We conclude by explaining how the
algorithms are connected and suggestions for future
research. }
}
@article{KimCouYou2021set,
title = {Bayesian Optimization with Approximate Set Kernels},
author = {Jungtaek Kim and Michael McCourt and Tackgeun You and Saehoon
Kim and Seungjin Choi},
abstract = {We propose a practical Bayesian optimization method over
sets, to minimize a black-box function that takes a set as a
single input. Because set inputs are permutation-invariant,
traditional Gaussian process-based Bayesian optimization
strategies which assume vector inputs can fall short. To
address this, we develop a Bayesian optimization method with
\emph{set kernel} that is used to build surrogate
functions. This kernel accumulates similarity over set
elements to enforce permutation-invariance, but this comes at
a greater computational cost. To reduce this burden, we
propose two key components: (i) a more efficient approximate
set kernel which is still positive-definite and is an
unbiased estimator of the true set kernel with upper-bounded
variance in terms of the number of subsamples, (ii) a
constrained acquisition function optimization over sets,
which uses symmetry of the feasible region that defines a set
input. Finally, we present several numerical experiments
which demonstrate that our method outperforms other methods.},
journal = {Machine Learning},
year = 2021,
doi = {10.1007/s10994-021-05949-0}
}
@article{KimParLee2017,
author = {Kim, J.-S. and Park, J.-H. and Lee, D.-H.},
title = {Iterated Greedy Algorithms to Minimize the Total Family Flow
Time for Job-shop Scheduling with Job Families and
Sequence-dependent Set-ups},
journal = {Engineering Optimization},
year = 2017,
volume = 49,
number = 10,
pages = {1719--1732}
}
@article{KinBa2014adam,
title = {Adam: A method for stochastic optimization},
author = {Kingma, Diederik P. and Ba, Jimmy},
journal = {Arxiv preprint arXiv:1412.6980 [cs.LG]},
year = 2014,
url = {https://arxiv.org/abs/1412.6980},
annote = {Published as a conference paper at the 3rd International
Conference for Learning Representations, San Diego, 2015~\cite{KinBa2015adam}}
}
@article{KirTou1985,
author = { Scott Kirkpatrick and G. Toulouse},
title = {Configuration Space Analysis of Travelling Salesman Problems},
journal = {Journal de Physique},
year = 1985,
volume = 46,
number = 8,
pages = {1277--1292}
}
@article{Kirkpatrick1984,
author = { Scott Kirkpatrick },
title = {Optimization by Simulated Annealing: Quantitative Studies},
journal = {Journal of Statistical Physics},
year = 1984,
volume = 34,
number = {5-6},
pages = {975--986}
}
@article{Kirkpatrick83,
author = { Scott Kirkpatrick and C. D. Gelatt and M. P. Vecchi},
title = {Optimization by Simulated Annealing},
journal = {Science},
year = 1983,
volume = 220,
number = 4598,
pages = {671--680},
annote = {Proposed Simulated Annealing},
doi = {10.1126/science.220.4598.671}
}
@article{KlaMosNau2017iwoven,
author = { Kathrin Klamroth and Mostaghim, Sanaz and Boris Naujoks and Silvia
Poles and Robin C. Purshouse and G{\"u}nther Rudolph and Ruzika,
Stefan and Serpil Say{\i}n and Margaret M. Wiecek and Xin Yao },
title = {Multiobjective optimization for interwoven systems},
journal = {Journal of Multi-Criteria Decision Analysis},
year = 2017,
volume = 24,
number = {1-2},
pages = {71--81},
doi = {10.1002/mcda.1598}
}
@article{KleShaHom2002,
author = {Anton J. Kleywegt and Alexander Shapiro and Tito Homem{-}de{-}Mello},
title = {The Sample Average Approximation Method for Stochastic Discrete Optimization},
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year = 2002,
volume = 12,
number = 2,
pages = {479--502}
}
@article{Kno2005tec,
author = { Joshua D. Knowles },
title = {{ParEGO}: A hybrid algorithm with on-line landscape
approximation for expensive multiobjective optimization
problems},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2006,
volume = 10,
number = 1,
pages = {50--66},
doi = {10.1109/TEVC.2005.851274},
keywords = {ParEGO, online, metamodel}
}
@article{Kno2009closed,
author = { Joshua D. Knowles },
title = {Closed-loop evolutionary multiobjective optimization},
journal = {IEEE Computational Intelligence Magazine},
volume = 4,
issue = 3,
pages = {77--91},
doi = {10.1109/MCI.2009.933095},
year = 2009,
abstract = {Artificial evolution has been used for more than 50 years as a method of optimization in engineering, operations research and computational intelligence. In closed-loop evolution (a term used by the statistician, George Box) or, equivalently, evolutionary experimentation (Ingo Rechenberg's terminology), the ``phenotypes'' are evaluated in the real world by conducting a physical experiment, whilst selection and breeding is simulated. Well-known early work on artificial evolution\textemdash design engineering problems in fluid dynamics, and chemical plant process optimization\textemdash was carried out in this experimental mode. More recently, the closed-loop approach has been successfully used in much evolvable hardware and evolutionary robotics research, and in some microbiology and biochemistry applications. In this article, several further new targets for closed-loop evolutionary and multiobjective optimization are considered. Four case studies from my own collaborative work are described: (i) instrument optimization in analytical biochemistry; (ii) finding effective drug combinations in vitro; (iii) onchip synthetic biomolecule design; and (iv) improving chocolate production processes. Accurate simulation in these applications is not possible due to complexity or a lack of adequate analytical models. In these and other applications discussed, optimizing experimentally brings with it several challenges: noise; nuisance factors; ephemeral resource constraints; expensive evaluations, and evaluations that must be done in (large) batches. Evolutionary algorithms (EAs) are largely equal to these vagaries, whilst modern multiobjective EAs also enable tradeoffs among conflicting optimization goals to be explored. Nevertheless, principles from other disciplines, such as statistics, Design of Experiments, machine learning and global optimization are also relevant to aspects of the closed-loop problem, and may inspire futher development of multiobjective EAs.},
langid = {english}
}
@article{KnoCor00paes,
author = { Joshua D. Knowles and David Corne },
title = {Approximating the Nondominated Front Using the
{Pareto} Archived Evolution Strategy},
journal = {Evolutionary Computation},
volume = 8,
number = 2,
pages = {149--172},
year = 2000,
doi = {10.1162/106365600568167},
annote = {Proposed PAES}
}
@article{KnoCor2003tec,
author = { Joshua D. Knowles and David Corne },
title = {Properties of an Adaptive Archiving Algorithm for Storing
Nondominated Vectors},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2003,
volume = 7,
number = 2,
pages = {100--116},
month = apr,
keywords = {S-metric, hypervolume},
annote = {Proposed to use S-metric (hypervolume metric) for
environmental selection}
}
@article{KnoVanGro2011,
author = {Knol, Mirjam J. and VanderWeele, Tyler J. and Groenwold, Rolf H. H.
and Klungel, Olaf H. and Rovers, Maroeska M. and Grobbee, Diederick E.},
title = {Estimating measures of interaction on an additive scale for preventive exposures},
journal = {European Journal of Epidemiology},
year = 2011,
volume = 26,
number = 6,
pages = {433--438}
}
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author = { Gary A. Kochenberger and Fred Glover and Alidaee, Bahram and Rego,
Cesar},
title = {A unified modeling and solution framework for combinatorial
optimization problems},
journal = {OR Spektrum},
year = 2004,
volume = 26,
number = 2,
pages = {237--250}
}
@article{KocHaoGlo2014bqap,
title = {The unconstrained binary quadratic programming problem: a
survey},
author = { Gary A. Kochenberger and Jin-Kao Hao and Fred Glover and Lewis, Mark and L{\"u}, Zhipeng and Wang, Haibo and Wang, Yang},
journal = {Journal of Combinatorial Optimization},
volume = 28,
number = 1,
pages = {58--81},
year = 2014,
doi = {10.1007/s10878-014-9734-0}
}
@article{Koe2009jmcda,
author = { Murat K{\"o}ksalan },
title = {Multiobjective Combinatorial Optimization: Some
Approaches},
journal = {Journal of Multi-Criteria Decision Analysis},
year = 2009,
volume = 15,
pages = {69--78},
doi = {10.1002/mcda.425}
}
@article{KokKar2010itdea,
title = {An Interactive Territory Defining Evolutionary Algorithm:
{iTDEA}},
volume = 14,
doi = {10.1109/TEVC.2010.2070070},
number = 5,
journal = {IEEE Transactions on Evolutionary Computation},
author = { Murat K{\"o}ksalan and Karahan, {\.I}brahim },
month = oct,
year = 2010,
pages = {702--722}
}
@article{KolHar2006ejor,
author = {Kolisch, Rainer and Hartmann, S{\"o}nke},
title = {Experimental investigation of heuristics for
resource-constrained project scheduling: An update},
volume = 174,
doi = {10.1016/j.ejor.2005.01.065},
abstract = {This paper considers heuristics for the well-known
resource-constrained project scheduling problem
({RCPSP).} It provides an update of our survey which
was published in 2000. We summarize and categorize a
large number of heuristics that have recently been
proposed in the literature. Most of these heuristics
are then evaluated in a computational study and
compared on the basis of our standardized
experimental design. Based on the computational
results we discuss features of good heuristics. The
paper closes with some remarks on our test design
and a summary of the recent developments in research
on heuristics for the {RCPSP}.},
number = 1,
journal = {European Journal of Operational Research},
month = oct,
year = 2006,
keywords = {Computational evaluation, Heuristics, Project
scheduling, Resource constraints},
pages = {23--37}
}
@article{KolPap2007approx,
title = {Approximately dominating representatives},
author = {Koltun, Vladlen and Christos H. Papadimitriou },
journal = {Theoretical Computer Science},
year = 2007,
number = 3,
pages = {148--154},
volume = 371,
publisher = {Elsevier}
}
@article{KolPes1994,
author = {A. Kolen and Erwin Pesch },
title = {Genetic Local Search in Combinatorial Optimization},
journal = {Discrete Applied Mathematics},
year = 1994,
volume = 48,
number = 3,
pages = {273--284}
}
@article{KolRee2007video,
title = {A framework for visually interactive decision-making and
design using evolutionary multi-objective optimization
({VIDEO})},
author = { Kollat, Joshua B. and Patrick M. Reed },
journal = {Environmental Modelling \& Software},
volume = 22,
number = 12,
pages = {1691--1704},
year = 2007,
keywords = {glyph plot}
}
@article{KooBec57,
author = {Tjalling C. Koopmans and Martin J. Beckmann},
title = {Assignment Problems and the Location of Economic Activities},
journal = {Econometrica},
volume = 25,
pages = {53--76},
year = 1957,
annote = {Introduced the Quadratic Assignment Problem (QAP)}
}
@article{Kor1985omega,
author = {Kornbluth, Jsh},
title = {Sequential multi-criterion decision making},
doi = {10.1016/0305-0483(85)90045-3},
abstract = {In this paper we consider a simple sequential
multicriterion decision making problem in which a
decision maker has to accept or reject a series of
multi-attributed outcomes. We show that using very
simple programming techniques, a great deal of the
decision making can be automated. The method might
be applicable to situations in which a dealer is
having to consider sequential offers in a trading
market.},
number = 6,
volume = 13,
journal = {Omega},
year = 1985,
keywords = {machine decision making},
pages = {569--574}
}
@article{KorMosWal1990choice,
author = { Pekka Korhonen and Moskowitz, Herbert and Wallenius, Jyrki },
title = {Choice Behavior in Interactive Multiple-Criteria Decision
Making},
journal = {Annals of Operations Research},
year = 1990,
volume = 23,
number = 1,
pages = {161--179},
month = dec,
doi = {10.1007/BF02204844},
abstract = {Choice behavior in an interactive multiple-criteria decision
making environment is examined experimentally. A ``free
search'' discrete visual interactive reference direction
approach was used on a microcomputer by management students
to solve two realistic and relevant multiple-criteria
decision problems. The results revealed persistent patterns
of intransitive choice behavior, and an unexpectedly rapid
degree of convergence of the reference direction approach on
a preferred solution. The results can be explained using
Tversky' additive utility difference model and
Kahneman-Tversky's prospect theory. The implications of the
results for the design of interactive multiple-criteria
decision procedures are discussed.}
}
@article{KorPagFal2001,
title = {On the ``dimensionality curse'' and the ``self-similarity
blessing''},
author = {Korn, Flip and Pagel, B.-U. and Faloutsos, Christos},
journal = {IEEE Transactions on Knowledge and Data Engineering},
volume = 13,
number = 1,
pages = {96--111},
year = 2001,
doi = {10.1109/69.908983},
abstract = {Spatial queries in high-dimensional spaces have been studied
extensively. Among them, nearest neighbor queries are
important in many settings, including spatial databases (Find
the k closest cities) and multimedia databases (Find the k
most similar images). Previous analyses have concluded that
nearest-neighbor search is hopeless in high dimensions due to
the notorious "curse of dimensionality". We show that this
may be overpessimistic. We show that what determines the
search performance (at least for R-tree-like structures) is
the intrinsic dimensionality of the data set and not the
dimensionality of the address space (referred to as the
embedding dimensionality). The typical (and often implicit)
assumption in many previous studies is that the data is
uniformly distributed, with independence between
attributes. However, real data sets overwhelmingly disobey
these assumptions; rather, they typically are skewed and
exhibit intrinsic ("fractal") dimensionalities that are much
lower than their embedding dimension, e.g. due to subtle
dependencies between attributes. We show how the Hausdorff
and Correlation fractal dimensions of a data set can yield
extremely accurate formulas that can predict the I/O
performance to within one standard deviation on multiple real
and synthetic data sets.}
}
@article{KorSilRob04:ml-aco,
author = { P. Koro{\v s}ec and Jurij {\v S}ilc and B. Robi{\v c}},
title = {Solving the mesh-partitioning problem with an
ant-colony algorithm},
journal = {Parallel Computing},
year = 2004,
volume = 30,
pages = {785--801}
}
@article{KorSilWalOor2012linear,
author = { Pekka Korhonen and Silvennoinen, Kari and Wallenius, Jyrki and {\"O}{\"o}rni, Anssi},
title = {Can a linear value function explain choices? {An}
experimental study},
journal = {European Journal of Operational Research},
year = 2012,
volume = 219,
number = 2,
pages = {360--367},
month = jun,
shorttitle = {Can a linear value function explain choices?},
doi = {10.1016/j.ejor.2011.12.040},
abstract = {We investigate in a simple bi-criteria experimental study,
whether subjects are consistent with a linear value function
while making binary choices. Many inconsistencies appeared in
our experiment. However, the impact of inconsistencies on the
linearity vs. non-linearity of the value function was
minor. Moreover, a linear value function seems to predict
choices for bi-criteria problems quite well. This ability to
predict is independent of whether the value function is
diagnosed linear or not. Inconsistencies in responses did not
necessarily change the original diagnosis of the form of the
value function. Our findings have implications for the design
and development of decision support tools for Multiple
Criteria Decision Making problems.},
language = {en},
keywords = {Binary choices, Inconsistency, Linear value function,
Multiple criteria, Weights}
}
@article{KorStuExn07:si,
author = { Oliver Korb and Thomas St{\"u}tzle and Thomas E. Exner },
title = {An Ant Colony Optimization Approach to Flexible
Protein--Ligand Docking},
journal = {Swarm Intelligence},
year = 2007,
volume = 1,
number = 2,
pages = {115--134}
}
@article{KorStuExn2009jcim,
author = { Oliver Korb and Thomas St{\"u}tzle and Thomas E. Exner },
title = {Empirical Scoring Functions for Advanced Protein-Ligand Docking with {PLANTS}},
journal = {Journal of Chemical Information and Modeling},
year = 2009,
volume = 49,
number = 2,
pages = {84--96}
}
@article{KorStuExn2010jcim,
author = { Oliver Korb and Peter Monecke and Gerhard Hessler and Thomas St{\"u}tzle and Thomas E. Exner },
title = {pharm{ACO}phore: Multiple Flexible Ligand Alignment Based on Ant Colony Optimization},
journal = {Journal of Chemical Information and Modeling},
year = 2010,
volume = 50,
number = 9,
pages = {1669--1681}
}
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procedure for multiple-objective linear programming is
presented. The method enables a decision maker to freely
search any part of the efficient frontier by controlling the
speed and direction of motion. The objective function values
are represented in numeric form and as bar graphs on a
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microcomputer and is illustrated using a multiple-objective
linear-programming model for managing disposal of sewage
sludge in the New York Bight. Some other applications are
also briefly discussed.}
}
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ant colony optimization, are widely used for different
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from a theoretical point of view. This paper contributes to
the theoretical analysis of ant colony optimization and
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combinatorial optimization problems, namely the traveling
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graph and show that it has a stronger local property than one
commonly used for constructing solutions of the TSP. The
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algorithms, based on these two construction procedures, shows
that they lead to good approximation in expected polynomial
time on random instances. Furthermore, we point out in which
situations our algorithms get trapped in local optima and
show where the use of the right amount of heuristic
information is provably beneficial.},
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$\epsilon$-Pareto},
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author = { Li, Miqing and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Xin Yao },
title = {Multi-Objective Archiving},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2023,
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doi = {10.1109/TEVC.2023.3314152},
abstract = {Most multi-objective optimisation algorithms maintain an
archive explicitly or implicitly during their search. Such an
archive can be solely used to store high-quality solutions
presented to the decision maker, but in many cases may
participate in the search process (e.g., as the population in
evolutionary computation). Over the last two decades,
archiving, the process of comparing new solutions with
previous ones and deciding how to update the
archive/population, stands as an important issue in
evolutionary multi-objective optimisation (EMO). This is
evidenced by constant efforts from the community on
developing various effective archiving methods, ranging from
conventional Pareto-based methods to more recent
indicator-based and decomposition-based ones. However, the
focus of these efforts is on empirical performance comparison
in terms of specific quality indicators; there is lack of
systematic study of archiving methods from a general
theoretical perspective. In this paper, we attempt to conduct
a systematic overview of multi-objective archiving, in the
hope of paving the way to understand archiving algorithms
from a holistic perspective of theory and practice, and more
importantly providing a guidance on how to design
theoretically desirable and practically useful archiving
algorithms. In doing so, we also present that archiving
algorithms based on weakly Pareto compliant indicators (e.g.,
$\epsilon$-indicator), as long as designed properly, can
achieve the same theoretical desirables as archivers based on
Pareto compliant indicators (e.g., hypervolume
indicator). Such desirables include the property
limit-optimal, the limit form of the possible optimal
property that a bounded archiving algorithm can have with
respect to the most general form of superiority between
solution sets.}
}
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metropolitan areas. This paper proposes a bi-level
optimization framework to settle the optimal traffic signal
setting problem. The upper-level problem determines the
traffic signal settings to minimize the drivers' average
travel time, while the lower-level problem aims for achieving
the network equilibrium using the settings calculated at the
upper level. Genetic algorithm is employed with the
integration of microscopic-traffic-simulation based dynamic
traffic assignment (DTA) to decouple the complex bi-level
problem into tractable single-level problems which are solved
sequentially. Case studies on a synthetic traffic network and
a real-world traffic subnetwork are conducted to examine the
effectiveness of the proposed model and relevant solution
methods. Additional strategies are provided for the extension
of the proposed model and the acceleration solution process
in large-area traffic network applications.}
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overview of existing algorithms, and we propose a model
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number of state-of-the-art and original strategies are
experimented on solving a permutation flowshop scheduling
problem and a traveling salesman problem, both on a two- and
a three-objective formulation. Experimental results and a
statistical comparison are reported in the paper, and some
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variants of the single-destination shortest paths problem is
studied by rigorous runtime analyses. Building upon previous
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extent an enlarged colony using $\lambda$ ants per vertex
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involved oscillations that with overwhelming probability
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optimum difficult to track are discussed. Experiments
illustrate theoretical findings and conjectures. }
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@article{LopBlu2010cor,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum },
title = {Beam-{ACO} for the travelling salesman problem with
time windows},
journal = {Computers \& Operations Research},
year = 2010,
doi = {10.1016/j.cor.2009.11.015},
volume = 37,
number = 9,
pages = {1570--1583},
keywords = {Ant colony optimization, Travelling salesman problem with
time windows, Hybridization},
abstract = {The travelling salesman problem with time windows is
a difficult optimization problem that arises, for
example, in logistics. This paper deals with the
minimization of the travel-cost. For solving this
problem, this paper proposes a Beam-ACO algorithm,
which is a hybrid method combining ant colony
optimization with beam search. In general, Beam-ACO
algorithms heavily rely on accurate and
computationally inexpensive bounding information for
differentiating between partial solutions. This work
uses stochastic sampling as a useful alternative. An
extensive experimental evaluation on seven benchmark
sets from the literature shows that the proposed
Beam-ACO algorithm is currently a state-of-the-art
technique for the travelling salesman problem with
time windows when travel-cost optimization is
concerned.}
}
@article{LopBlu2013asoc,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum and Jeffrey W. Ohlmann and Barrett W. Thomas },
title = {The Travelling Salesman Problem with Time Windows:
Adapting Algorithms from Travel-time to Makespan
Optimization},
journal = {Applied Soft Computing},
year = 2013,
volume = 13,
number = 9,
pages = {3806--3815},
doi = {10.1016/j.asoc.2013.05.009},
epub = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2013-011.pdf}
}
@article{LopBraPaq2021arxiv,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\"u}rgen Branke and Lu{\'i}s Paquete },
title = {Reproducibility in Evolutionary Computation},
journal = {Arxiv preprint arXiv:20102.03380 [cs.AI]},
year = 2021,
url = {https://arxiv.org/abs/2102.03380},
abstract = {Experimental studies are prevalent in Evolutionary
Computation (EC), and concerns about the reproducibility and
replicability of such studies have increased in recent times,
reflecting similar concerns in other scientific fields. In
this article, we suggest a classification of different types
of reproducibility that refines the badge system of the
Association of Computing Machinery (ACM) adopted by TELO. We
discuss, within the context of EC, the different types of
reproducibility as well as the concepts of artifact and
measurement, which are crucial for claiming
reproducibility. We identify cultural and technical obstacles
to reproducibility in the EC field. Finally, we provide
guidelines and suggest tools that may help to overcome some
of these reproducibility obstacles.},
keywords = {Evolutionary Computation, Reproducibility, Empirical study,
Benchmarking}
}
@article{LopBraPaq2021telo,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\"u}rgen Branke and Lu{\'i}s Paquete },
title = {Reproducibility in Evolutionary Computation},
journal = {ACM Transactions on Evolutionary Learning and Optimization},
year = 2021,
volume = 1,
number = 4,
pages = {1--21},
doi = {10.1145/3466624},
epub = {https://arxiv.org/abs/2102.03380},
abstract = {Experimental studies are prevalent in Evolutionary
Computation (EC), and concerns about the reproducibility and
replicability of such studies have increased in recent times,
reflecting similar concerns in other scientific fields. In
this article, we suggest a classification of different types
of reproducibility that refines the badge system of the
Association of Computing Machinery (ACM) adopted by TELO. We
discuss, within the context of EC, the different types of
reproducibility as well as the concepts of artifact and
measurement, which are crucial for claiming
reproducibility. We identify cultural and technical obstacles
to reproducibility in the EC field. Finally, we provide
guidelines and suggest tools that may help to overcome some
of these reproducibility obstacles.},
keywords = {Evolutionary Computation, Reproducibility, Empirical study,
Benchmarking}
}
@article{LopDubPerStuBir2016irace,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and P{\'e}rez C{\'a}ceres, Leslie and Thomas St{\"u}tzle and Mauro Birattari },
title = {The {\rpackage{irace}} Package: Iterated Racing for Automatic
Algorithm Configuration},
journal = {Operations Research Perspectives},
year = 2016,
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2016-003/},
doi = {10.1016/j.orp.2016.09.002},
volume = 3,
pages = {43--58}
}
@article{LopKesStu2017:cim,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Marie-El{\'e}onore Kessaci and Thomas St{\"u}tzle },
title = {Automatic Design of Hybrid Metaheuristics from Algorithmic Components},
journal = {Submitted},
year = 2017,
optvolume = {},
optnumber = {},
optpages = {}
}
@article{LopPaqStu05:jmma,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {Hybrid Population-based Algorithms for the
Bi-objective Quadratic Assignment Problem},
journal = {Journal of Mathematical Modelling and Algorithms},
year = 2006,
volume = 5,
number = 1,
pages = {111--137},
doi = {10.1007/s10852-005-9034-x},
abstract = {We present variants of an ant colony optimization
(MO-ACO) algorithm and of an evolutionary algorithm
(SPEA2) for tackling multi-objective combinatorial
optimization problems, hybridized with an iterative
improvement algorithm and the robust tabu search
algorithm. The performance of the resulting hybrid
stochastic local search (SLS) algorithms is
experimentally investigated for the bi-objective
quadratic assignment problem (bQAP) and compared
against repeated applications of the underlying
local search algorithms for several
scalarizations. The experiments consider structured
and unstructured bQAP instances with various degrees
of correlation between the flow matrices. We do a
systematic experimental analysis of the algorithms
using outperformance relations and the attainment
functions methodology to asses differences in the
performance of the algorithms. The experimental
results show the usefulness of the hybrid algorithms
if the available computation time is not too limited
and identify SPEA2 hybridized with very short tabu
search runs as the most promising variant.}
}
@article{LopPerStu2020ifors,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and P{\'e}rez C{\'a}ceres, Leslie and Thomas St{\"u}tzle },
title = {{irace}: A Tool for the Automatic Configuration of
Algorithms},
journal = {International Federation of Operational Research Societies
(IFORS) News},
year = 2020,
volume = 14,
number = 2,
pages = {30--32},
month = jun,
url = {https://www.ifors.org/newsletter/ifors-news-june2020.pdf}
}
@article{LopPraPae08aco,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Ant Colony Optimisation for the Optimal Control of
Pumps in Water Distribution Networks},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
year = 2008,
volume = 134,
number = 4,
pages = {337--346},
publisher = {{ASCE}},
epub = {http://link.aip.org/link/?QWR/134/337/1},
doi = {10.1061/(ASCE)0733-9496(2008)134:4(337)},
abstract = {Reducing energy consumption of water distribution
networks has never had more significance than today. The greatest
energy savings can be obtained by careful scheduling of operation of
pumps. Schedules can be defined either implicitly, in terms of other
elements of the network such as tank levels, or explicitly by
specifying the time during which each pump is on/off. The
traditional representation of explicit schedules is a string of
binary values with each bit representing pump on/off status during a
particular time interval. In this paper a new explicit
representation is presented. It is based on time controlled
triggers, where the maximum number of pump switches is specified
beforehand. In this representation a pump schedule is divided into a
series of integers with each integer representing the number of
hours for which a pump is active/inactive. This reduces the number
of potential schedules (search space) compared to the binary
representation. Ant colony optimization (ACO) is a stochastic
meta-heuristic for combinatorial optimization problems that is
inspired by the foraging behavior of some species of ants. In this
paper, an application of the ACO framework was developed for the
optimal scheduling of pumps. The proposed representation was adapted
to an ant colony Optimization framework and solved for the optimal
pump schedules. Minimization of electrical cost was considered as
the objective, while satisfying system constraints. Instead of using
a penalty function approach for constraint violations, constraint
violations were ordered according to their importance and solutions
were ranked based on this order. The proposed approach was tested on
a small test network and on a large real-world network. Results are
compared with those obtained using a simple genetic algorithm based
on binary representation and a hybrid genetic algorithm that uses
level-based triggers.}
}
@article{LopPraPae2011ec,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Representations and Evolutionary Operators for the
Scheduling of Pump Operations in Water Distribution
Networks},
journal = {Evolutionary Computation},
year = 2011,
doi = {10.1162/EVCO_a_00035},
volume = 19,
number = 3,
pages = {429--467},
abstract = {Reducing the energy consumption of water
distribution networks has never had more
significance. The greatest energy savings can be
obtained by carefully scheduling the operations of
pumps. Schedules can be defined either implicitly,
in terms of other elements of the network such as
tank levels, or explicitly by specifying the time
during which each pump is on/off. The traditional
representation of explicit schedules is a string of
binary values with each bit representing pump on/off
status during a particular time interval. In this
paper, we formally define and analyze two new
explicit representations based on time-controlled
triggers, where the maximum number of pump switches
is established beforehand and the schedule may
contain less switches than the maximum. In these
representations, a pump schedule is divided into a
series of integers with each integer representing
the number of hours for which a pump is
active/inactive. This reduces the number of
potential schedules compared to the binary
representation, and allows the algorithm to operate
on the feasible region of the search space. We
propose evolutionary operators for these two new
representations. The new representations and their
corresponding operations are compared with the two
most-used representations in pump scheduling,
namely, binary representation and level-controlled
triggers. A detailed statistical analysis of the
results indicates which parameters have the greatest
effect on the performance of evolutionary
algorithms. The empirical results show that an
evolutionary algorithm using the proposed
representations improves over the results obtained
by a recent state-of-the-art Hybrid Genetic
Algorithm for pump scheduling using level-controlled
triggers.}
}
@article{LopStu2012swarm,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An experimental analysis of design choices of multi-objective ant colony optimization algorithms},
journal = {Swarm Intelligence},
year = 2012,
number = 3,
volume = 6,
pages = {207--232},
doi = {10.1007/s11721-012-0070-7},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2012-006/}
}
@article{LopStu2012tec,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {The Automatic Design of Multi-Objective Ant Colony
Optimization Algorithms},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2012,
volume = 16,
number = 6,
pages = {861--875},
doi = {10.1109/TEVC.2011.2182651},
abstract = {Multi-objective optimization problems are problems with
several, typically conflicting criteria for evaluating
solutions. Without any a priori preference information, the
Pareto optimality principle establishes a partial order among
solutions, and the output of the algorithm becomes a set of
nondominated solutions rather than a single one. Various ant
colony optimization (ACO) algorithms have been proposed in
recent years for solving such problems. These multi-objective
ACO (MOACO) algorithms exhibit different design choices for
dealing with the particularities of the multi-objective
context. This paper proposes a formulation of algorithmic
components that suffices to describe most MOACO algorithms
proposed so far. This formulation also shows that existing
MOACO algorithms often share equivalent design choices but
they are described in different terms. Moreover, this
formulation is synthesized into a flexible algorithmic
framework, from which not only existing MOACO algorithms may
be instantiated, but also combinations of components that
were never studied in the literature. In this sense, this
paper goes beyond proposing a new MOACO algorithm, but it
rather introduces a family of MOACO algorithms. The
flexibility of the proposed MOACO framework facilitates the
application of automatic algorithm configuration
techniques. The experimental results presented in this paper
show that the automatically configured MOACO framework
outperforms the MOACO algorithms that inspired the framework
itself. This paper is also among the first to apply automatic
algorithm configuration techniques to multi-objective
algorithms.}
}
@article{LopStu2013ejor,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatically Improving the Anytime Behaviour of Optimisation
Algorithms},
journal = {European Journal of Operational Research},
year = 2014,
volume = 235,
number = 3,
pages = {569--582},
doi = {10.1016/j.ejor.2013.10.043},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2012-011/},
abstract = {Optimisation algorithms with good anytime behaviour try to
return as high-quality solutions as possible independently of
the computation time allowed. Designing algorithms with good
anytime behaviour is a difficult task, because performance is
often evaluated subjectively, by plotting the trade-off curve
between computation time and solution quality. Yet, the
trade-off curve may be modelled also as a set of mutually
nondominated, bi-objective points. Using this model, we
propose to combine an automatic configuration tool and the
hypervolume measure, which assigns a single quality measure
to a nondominated set. This allows us to improve the anytime
behaviour of optimisation algorithms by means of
automatically finding algorithmic configurations that produce
the best nondominated sets. Moreover, the recently proposed
weighted hypervolume measure is used here to incorporate the
decision-maker's preferences into the automatic tuning
procedure. We report on the improvements reached when
applying the proposed method to two relevant scenarios: (i)
the design of parameter variation strategies for MAX-MIN Ant
System and (ii) the tuning of the anytime behaviour of SCIP,
an open-source mixed integer programming solver with more
than 200 parameters.}
}
@article{LopTerRos2014esa,
author = {Eunice López-Camacho and Hugo Terashima-Marin and Peter Ross and Gabriela Ochoa },
title = {A unified hyper-heuristic framework for solving bin packing
problems},
journal = {Expert Systems with Applications},
volume = 41,
number = 15,
pages = {6876--6889},
year = 2014,
doi = {10.1016/j.eswa.2014.04.043}
}
@article{LopVerDreDoe2025,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Diederick Vermetten and Johann Dreo and Carola Doerr },
title = {Using the Empirical Attainment Function for Analyzing
Single-objective Black-box Optimization Algorithms},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2025,
annote = {Pre-print: \url{https://doi.org/10.48550/arXiv.2404.02031}},
doi = {10.1109/TEVC.2024.3462758},
abstract = {A widely accepted way to assess the performance of iterative
black-box optimizers is to analyze their empirical cumulative
distribution function (ECDF) of pre-defined quality targets
achieved not later than a given runtime. In this work, we
consider an alternative approach, based on the empirical
attainment function (EAF) and we show that the target-based
ECDF is an approximation of the EAF. We argue that the EAF
has several advantages over the target-based ECDF. In
particular, it does not require defining a priori quality
targets per function, captures performance differences more
precisely, and enables the use of additional summary
statistics that enrich the analysis. We also show that the
average area over the convergence curves is a
simpler-to-calculate, but equivalent, measure of anytime
performance. To facilitate the accessibility of the EAF, we
integrate a module to compute it into the IOHanalyzer
platform. Finally, we illustrate the use of the EAF via
synthetic examples and via the data available for the BBOB
suite.},
keywords = {EAF-based ECDF}
}
@article{LouBoi2008vns_anytime,
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title = {Combining {VNS} with constraint programming for
solving anytime optimization problems},
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year = 2008,
volume = 191,
pages = {705--735},
doi = {10.1016/j.ejor.2006.12.062}
}
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author = {Lova, Antonio and Tormos, Pilar},
title = {Analysis of Scheduling Schemes and Heuristic Rules
Performance in Resource-Constrained Multiproject
Scheduling},
volume = 102,
doi = {10.1023/A:1010966401888},
abstract = {Frequently, the availability of resources assigned
to a project is limited and not sufficient to
execute all the concurrent activities. In this
situation, decision making about their schedule is
necessary. Many times this schedule supposes an
increase in the project completion
time. Additionally, companies commonly manage
various projects simultaneously, sharing a pool of
renewable resources. Given these resource
constraints, we often can only apply heuristic
methods to solve the scheduling problem. In this
work the effect of the schedule generation schemes -
serial or parallel - and priority rules - {MINLFT},
{MINSLK}, {MAXTWK}, {SASP} or {FCFS} - with two
approaches - multi-project and single-project - are
analysed. The time criteria considered are the mean
project delay and the multiproject duration
increase. Through an extensive computational study,
results show that with the parallel scheduling
generation scheme and the multi-project approach the
project manager can obtain a good multiproject
schedule with the time criterion selected:
minimising mean project delay or minimising
multiproject duration increase. New heuristics -
based on priority rules with a two-phase approach -
that outperform classical ones are proposed to
minimise mean project delay with a multi-project
approach. Finally, the best heuristics analysed are
evaluated together with a representative sample of
commercial project management software.},
number = {1-4},
journal = {Annals of Operations Research},
month = feb,
year = 2001,
keywords = {Combinatorics, heuristic based on priority rules,
Multiproject scheduling, Operation
{Research/Decision} Theory, Project management,
project management software, Resource allocation,
Theory of Computation},
pages = {263--286}
}
@article{LovTorCer2009ijpe,
author = {Lova, Antonio and Tormos, Pilar and Cervantes,
Mariamar and Barber, Federico},
title = {An efficient hybrid genetic algorithm for scheduling
projects with resource constraints and multiple
execution modes},
volume = 117,
doi = {10.1016/j.ijpe.2008.11.002},
abstract = {Multi-mode Resource Constrained Project Scheduling
Problem ({MRCPSP)} aims at finding the start times
and execution modes for the activities of a project
that optimize a given objective function while
verifying a set of precedence and resource
constraints. In this paper, we focus on this problem
and develop a hybrid Genetic Algorithm ({MM-HGA)} to
solve it. Its main contributions are the mode
assignment procedure, the fitness function and the
use of a very efficient improving method. Its
performance is demonstrated by extensive
computational results obtained on a set of standard
instances and against the best currently available
algorithms.},
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journal = {International Journal of Production Economics},
year = 2009,
keywords = {genetic algorithm, multi-mode resource-constrained
project scheduling},
pages = {302--316}
}
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}
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}
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}
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{Pareto} Local Search, to find a good approximation of the
efficient set of the biobjective traveling salesman
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population composed of a good approximation of the extreme
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year = 2010,
note = {Published as~\cite{LusTeg2012itor}}
}
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}
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and Veness, Joel and Hausknecht, Matthew and Bowling,
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Protocols and Open Problems for General Agents},
year = 2018,
publisher = {AI Access Foundation},
address = {El Segundo, CA, USA},
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number = 1,
issn = {1076-9757},
abstract = {The Arcade Learning Environment (ALE) is an evaluation
platform that poses the challenge of building AI agents with
general competency across dozens of Atari 2600 games. It
supports a variety of different problem settings and it has
been receiving increasing attention from the scientific
community, leading to some high-pro_le success stories such
as the much publicized Deep Q-Networks (DQN). In this article
we take a big picture look at how the ALE is being used by
the research community. We show how diverse the evaluation
methodologies in the ALE have become with time, and highlight
some key concerns when evaluating agents in the ALE. We use
this discussion to present some methodological best practices
and provide new benchmark results using these best
practices. To further the progress in the field, we introduce
a new version of the ALE that supports multiple game modes
and provides a form of stochasticity we call sticky
actions. We conclude this big picture look by revisiting
challenges posed when the ALE was introduced, summarizing the
state-of-the-art in various problems and highlighting
problems that remain open.},
journal = {Journal of Artificial Intelligence Research},
month = jan,
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numpages = 40
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year = 2007,
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Mathematical programming},
abstract = {This paper develops an Improved harmony search (IHS)
algorithm for solving optimization problems. IHS employs a
novel method for generating new solution vectors that
enhances accuracy and convergence rate of harmony search (HS)
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on harmony search algorithm are discussed and a strategy for
tuning these parameters is presented. The IHS algorithm has
been successfully applied to various benchmarking and
standard engineering optimization problems. Numerical results
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Application to the Double-Row Facility Layout Problem},
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issn = {1063-6560},
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keywords = {irace}
}
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approaches for solving optimization problems. Finding the
best metaheuristic for a given problem is often difficult due
to the large number of available approaches and possible
algorithmic designs. Moreover, high-performing metaheuristics
often combine general-purpose and problem-specific
algorithmic components. We propose here an approach for
automatically designing metaheuristics using a flexible
framework of algorithmic components, from which algorithms
are instantiated and evaluated by an automatic configuration
method. The rules for composing algorithmic components are
defined implicitly by the properties of each algorithmic
component, in contrast to previous proposals, which require a
handwritten algorithmic template or grammar. As a result,
extending our framework with additional components, even
problem-specific or user-defined ones, automatically updates
the design space. Furthermore, since the generated algorithms
are made up of components, they can be easily interpreted. We
provide an implementation of our proposal and demonstrate its
benefits by outperforming previous research in three distinct
problems from completely different families: a facility
layout problem, a vehicle routing problem and a clustering
problem.},
keywords = {irace}
}
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Programming for Multiple Objective Decision Making (MODM)
problems. The method, called Interactive Sequential Goal
Programming (ISGP), combines and extends the attractive
features of both Goal Programming and interactive solution
approaches for MODM problems. ISGP is applicable to both
linear and non-linear problems. It uses existing single
objective optimization techniques and, hence, available
computer codes utilizing these techniques can be adapted for
use in ISGP. The non-dominance of the "best-compromise"
solution is assured. The information required from the
decision maker in each iteration is simple. The proposed
method is illustrated by solving a nutrition problem.}
}
@article{MasLopDubStu2014cor,
author = { Franco Mascia and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and Thomas St{\"u}tzle },
title = {Grammar-Based Generation of Stochastic Local Search
Heuristics through Automatic Algorithm Configuration Tools},
journal = {Computers \& Operations Research},
year = 2014,
doi = {10.1016/j.cor.2014.05.020},
volume = 51,
pages = {190--199}
}
@article{MasPelStuBir2014itor,
author = { Franco Mascia and Paola Pellegrini and Thomas St{\"u}tzle and Mauro Birattari },
title = {An Analysis of Parameter Adaptation in Reactive Tabu Search},
journal = {International Transactions in Operational Research},
year = 2014,
volume = 21,
number = 1,
pages = {127--152}
}
@article{MasVidMic++2013,
author = {Renaud Masson and Thibaut Vidal and Julien Michallet and Puca Huachi {Vaz Penna} and Vinicius Petrucci and Anand Subramanian and Hugues Dubedout},
title = {An Iterated Local Search Heuristic for Multi-capacity Bin Packing and Machine Reassignment Problems},
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year = 2013,
volume = 40,
number = 13,
pages = {5266--5275}
}
@article{MatDauLah2011:ejor,
author = {Yazid Mati and St{\'e}phane Dauz{\`e}re-P{\`e}r{\'e}s and Chams Lahlou},
title = {A General Approach for Optimizing Regular Criteria in the Job-shop Scheduling Problem},
journal = {European Journal of Operational Research},
year = 2011,
volume = 212,
number = 1,
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}
@article{MazLopChuMie2023tgp,
author = { Atanu Mazumdar and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Tinkle Chugh and Kaisa Miettinen },
title = {Treed {Gaussian} Process Regression for Solving Offline
Data-Driven Continuous Multiobjective Optimization Problems},
journal = {Evolutionary Computation},
year = 2023,
volume = 31,
number = 4,
pages = {375--399},
doi = {10.1162/evco_a_00329},
abstract = {For offline data-driven multiobjective optimization problems
(MOPs), no new data is available during the optimization
process. Approximation models (or surrogates) are first built
using the provided offline data and an optimizer, e.g. a
multiobjective evolutionary algorithm, can then be utilized
to find Pareto optimal solutions to the problem with
surrogates as objective functions. In contrast to online
data-driven MOPs, these surrogates cannot be updated with new
data and, hence, the approximation accuracy cannot be
improved by considering new data during the optimization
process. Gaussian process regression (GPR) models are widely
used as surrogates because of their ability to provide
uncertainty information. However, building GPRs becomes
computationally expensive when the size of the dataset is
large. Using sparse GPRs reduces the computational cost of
building the surrogates. However, sparse GPRs are not
tailored to solve offline data-driven MOPs, where good
accuracy of the surrogates is needed near Pareto optimal
solutions. Treed GPR (TGPR-MO) surrogates for offline
data-driven MOPs with continuous decision variables are
proposed in this paper. The proposed surrogates first split
the decision space into subregions using regression trees and
build GPRs sequentially in regions close to Pareto optimal
solutions in the decision space to accurately approximate
tradeoffs between the objective functions. TGPR-MO surrogates
are computationally inexpensive because GPRs are built only
in a smaller region of the decision space utilizing a subset
of the data. The TGPR-MO surrogates were tested on
distance-based visualizable problems with various data sizes,
sampling strategies, numbers of objective functions, and
decision variables. Experimental results showed that the
TGPR-MO surrogates are computationally cheaper and can handle
datasets of large size. Furthermore, TGPR-MO surrogates
produced solutions closer to Pareto optimal solutions
compared to full GPRs and sparse GPRs.},
keywords = {Gaussian processes, Kriging, Regression trees, Metamodelling,
Surrogate, Pareto optimality}
}
@article{McConMehNah2011certifying,
author = {Ross M. McConnell and Kurt Mehlhorn and Stefan N{\"a}her and
Pascal Schweitzer},
title = {Certifying algorithms},
journal = {Computer Science Review},
year = 2011,
volume = 5,
number = 2,
pages = {119--161},
issn = {1574-0137},
doi = {10.1016/j.cosrev.2010.09.009},
keywords = {Algorithms, Software reliability, Certification},
abstract = {A certifying algorithm is an algorithm that produces, with
each output, a certificate or witness (easy-to-verify proof)
that the particular output has not been compromised by a
bug. A user of a certifying algorithm inputs x, receives the
output y and the certificate w, and then checks, either
manually or by use of a program, that w proves that y is a
correct output for input x. In this way, he/she can be sure
of the correctness of the output without having to trust the
algorithm. We put forward the thesis that certifying
algorithms are much superior to non-certifying algorithms,
and that for complex algorithmic tasks, only certifying
algorithms are satisfactory. Acceptance of this thesis would
lead to a change of how algorithms are taught and how
algorithms are researched. The widespread use of certifying
algorithms would greatly enhance the reliability of
algorithmic software. We survey the state of the art in
certifying algorithms and add to it. In particular, we start
a theory of certifying algorithms and prove that the concept
is universal.}
}
@article{McCorPow03demand,
title = {Optimal Pump Scheduling in Water Supply Systems with
Maximum Demand Charges},
author = { G. McCormick and R. S. Powell },
publisher = {ASCE},
year = 2003,
journal = {Journal of Water Resources Planning and Management, {ASCE}},
volume = 129,
number = 5,
pages = {372--379},
keywords = {water supply; pumps; Markov processes; cost optimal
control},
epub = {http://link.aip.org/link/?QWR/129/372/1},
doi = {10.1061/(ASCE)0733-9496(2003)129:5(372)}
}
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author = { G. McCormick and R. S. Powell },
title = {Derivation of near-optimal pump schedules for water
distribution by simulated annealing},
journal = {Journal of the Operational Research Society},
year = 2004,
volume = 55,
number = 7,
pages = {728--736},
month = jul,
doi = {10.1057/palgrave.jors.2601718},
abstract = {The scheduling of pumps for clean water distribution is a
partially discrete non-linear problem with many
variables. The scheduling method described in this paper
typically produces costs within 1\% of a linear program-based
solution, and can incorporate realistic non-linear costs that
may be hard to incorporate in linear programming
formulations. These costs include pump switching and maximum
demand charges. A simplified model is derived from a standard
hydraulic simulator. An initial schedule is produced by a
descent method. Two-stage simulated annealing then produces
solutions in a few minutes. Iterative recalibration ensures
that the solution agrees closely with the results from a full
hydraulic simulation.}
}
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author = {James McDermott},
title = {When and Why Metaheuristics Researchers can Ignore "No Free
Lunch" Theorems},
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}
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author = { Catherine C. McGeoch },
title = {Analyzing Algorithms by Simulation: Variance Reduction
Techniques and Simulation Speedups},
abstract = {Although experimental studies have been widely applied to the
investigation of algorithm performance, very little attention
has been given to experimental method in this area. This is
unfortunate, since much can be done to improve the quality of
the data obtained; often, much improvement may be needed for
the data to be useful. This paper gives a tutorial discussion
of two aspects of good experimental technique: the use of
variance reduction techniques and simulation speedups in
algorithm studies. In an illustrative study, application of
variance reduction techniques produces a decrease in variance
by a factor 1000 in one case, giving a dramatic improvement
in the precision of experimental results. Furthermore, the
complexity of the simulation program is improved from
$\Theta(m n/H_n)$ to $\Theta(m + n \log n)$ (where $m$ is
typically much larger than $n$), giving a much faster
simulation program and therefore more data per unit of
computation time. The general application of variance
reduction techniques is also discussed for a variety of
algorithm problem domains.},
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doi = {10.1145/130844.130853},
number = 2,
journal = {{ACM} Computing Surveys},
year = 1992,
keywords = {experimental analysis of algorithms, move-to-front rule,
self-organizing sequential search, statistical analysis of
algorithms, transpose rule, variance reduction techniques},
pages = {195--212}
}
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respect to variance for a class of estimators which includes
the sample mean and the empirical distribution function.},
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@article{MieEskRui2010nautilus,
author = { Kaisa Miettinen and Eskelinen, Petri and Francisco Ruiz and Mariano Luque },
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multiobjective optimization based on the nadir point},
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year = 2010,
volume = 206,
number = 2,
pages = {426--434},
month = oct,
issn = {0377-2217},
shorttitle = {{NAUTILUS} method},
doi = {10.1016/j.ejor.2010.02.041},
abstract = {Most interactive methods developed for solving multiobjective
optimization problems sequentially generate Pareto optimal or
nondominated vectors and the decision maker must always allow
impairment in at least one objective function to get a new
solution. The NAUTILUS method proposed is based on the
assumptions that past experiences affect decision makers'
hopes and that people do not react symmetrically to gains and
losses. Therefore, some decision makers may prefer to start
from the worst possible objective values and to improve every
objective step by step according to their preferences. In
NAUTILUS, starting from the nadir point, a solution is
obtained at each iteration which dominates the previous
one. Although only the last solution will be Pareto optimal,
the decision maker never looses sight of the Pareto optimal
set, and the search is oriented so that (s)he progressively
focusses on the preferred part of the Pareto optimal
set. Each new solution is obtained by minimizing an
achievement scalarizing function including preferences about
desired improvements in objective function values. NAUTILUS
is specially suitable for avoiding undesired anchoring
effects, for example in negotiation support problems, or just
as a means of finding an initial Pareto optimal solution for
any interactive procedure. An illustrative example
demonstrates how this new method iterates.},
language = {en},
keywords = {Reference point methods, Interactive methods, Multiple
objective programming, Pareto optimality, Preference
information}
}
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colony optimization algorithm designed to solve the
problem of ordering cars on an assembly line
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shown to be {NP-hard} and evokes a great deal of
interest among practitioners. Learning in an ant
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pheromone trail, which is a central element of this
metaheuristic. Many versions of the algorithm are
found in literature, the main distinction among them
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journal = {European Journal of Operational Research},
year = 2009,
keywords = {Ant colony {optimization,Car-sequencing}
{problem,Pheromone} {trail,Scheduling}},
pages = {1185--1191}
}
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year = 2009
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traveling salesman problem (TSP). To construct a powerful GA,
we use edge assembly crossover (EAX) and make substantial
enhancements to it: (i) localization of EAX together with its
efficient implementation and (ii) the use of a local search
procedure in EAX to determine good combinations of building
blocks of parent solutions for generating even better
offspring solutions from very high-quality parent
solutions. In addition, we develop (iii) an innovative
selection model for maintaining population diversity at a
negligible computational cost. Experimental results on
well-studied TSP benchmarks demonstrate that the proposed GA
outperforms state-of-the-art heuristic algorithms in finding
very high-quality solutions on instances with up to 200,000
cities. In contrast to the state-of-the-art TSP heuristics,
which are all based on the Lin-Kernighan (LK) algorithm, our
GA achieves top performance without using an LK-based
algorithm.}
}
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abstract = {Research in multi-objective particle swarm optimizers
(MOPSOs) progresses by proposing one new MOPSO at a time. In
spite of the commonalities among different MOPSOs, it is
often unclear which algorithmic components are crucial for
explaining the performance of a particular MOPSO
design. Moreover, it is expected that different designs may
perform best on different problem families and identifying a
best overall MOPSO is a challenging task. We tackle this
challenge here by: (1) proposing AutoMOPSO, a flexible
algorithmic template for designing MOPSOs with a design space
that can instantiate thousands of potential MOPSOs; and (2)
searching for good-performing MOPSO designs given a family of
training problems by means of an automatic configuration tool
(irace). We apply this automatic design methodology to
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state-of-the-art MOPSOs on four well-known bi-objective
problem families. We also identify the key design choices and
parameters of the winning MOPSO by means of
ablation. AutoMOPSO is publicly available as part of the
jMetal framework.}
}
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mathematical programming for decision-making under
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author = { Jeffrey W. Ohlmann and Barrett W. Thomas },
title = {A Compressed-Annealing Heuristic for the Traveling
Salesman Problem with Time Windows},
journal = {INFORMS Journal on Computing},
volume = 19,
number = 1,
year = 2007,
issn = {1526-5528},
pages = {80--90},
doi = {10.1287/ijoc.1050.0145},
publisher = {{INFORMS}},
address = {Institute for Operations Research and the Management
Sciences (INFORMS), Linthicum, Maryland, USA}
}
@article{OliHeYao2007,
title = {Time complexity of evolutionary algorithms for combinatorial
optimization: A decade of results},
author = { Oliveto, Pietro S. and He, Jun and Xin Yao },
journal = {International Journal of Automation and Computing},
volume = 4,
number = 3,
pages = {281--293},
year = 2007,
publisher = {Springer}
}
@article{OliWit2015,
author = { Oliveto, Pietro S. and Carsten Witt },
title = {Improved time complexity analysis of the Simple Genetic Algorithm},
journal = {Theoretical Computer Science},
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pages = {21--41},
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doi = {10.1016/j.tcs.2015.01.002}
}
@article{Ols1992review,
author = {Olson, David L.},
title = {Review of Empirical Studies in Multiobjective Mathematical
Programming: Subject Reflection of Nonlinear Utility and
Learning},
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volume = 23,
number = 1,
pages = {1--20},
year = 1992,
keywords = {Decision Analysis, Human Information Processing, Linear
Programming},
doi = {10.1111/j.1540-5915.1992.tb00374.x},
abstract = {Multiple objective programming provides a means of
aiding decision makers facing complex decisions where
trade-offs among conflicting objectives must be
reconciled. Interactive multiobjective programming provides a
means for decision makers to learn what these trade-offs
involve, while the mathematical program generates solutions
that seek improvement of the implied utility of the decision
maker. A variety of multiobjective programming techniques
have been presented in the multicriteria decision-making
literature. This study reviews published studies with human
subjects where some of these techniques were applied. While
all of the techniques have the ability to support decision
makers under conditions of multiple objectives, a number of
features in applying these systems have been tested by these
studies. A general evolution of techniques is traced,
starting with methods relying upon linear combinations of
value, to more recent methods capable of reflecting nonlinear
trade-offs of value. Support of nonlinear utility and
enhancing decision-maker learning are considered.}
}
@article{OlsLok2013joh,
author = {Roland Olsson and Arne L{\o}kketangen },
title = {Using Automatic Programming to Generate State-of-the-art
Algorithms for Random 3-{SAT}},
journal = {Journal of Heuristics},
volume = 19,
number = 5,
year = 2013,
pages = {819--844},
doi = {10.1007/s10732-013-9226-x},
annote = {Uses evolution but it is not genetic programming, nor
grammatical evolution.}
}
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author = {Mihai Oltean},
title = {Evolving Evolutionary Algorithms Using Linear Genetic
Programming},
journal = {Evolutionary Computation},
year = 2005,
volume = 13,
number = 3,
pages = {387--410},
doi = {10.1162/1063656054794815}
}
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title = {Grammatical Evolution},
author = { O'Neill, Michael and Ryan, Conor},
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number = 4,
pages = {349--358},
year = 2001
}
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number = 2,
pages = {148--164},
note = {}
}
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volume = 120,
number = 2,
pages = {237--252}
}
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author = { Lindell E. Ormsbee and Srinivasa L. Reddy },
title = {Nonlinear Heuristic for Pump Operations},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
volume = 121,
number = 4,
pages = {302--309},
date = {1995-07/1995-08},
year = 1995,
month = jul # { / } # aug
}
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title = {Analysis of Static Simulated Annealing Algorithms},
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volume = 115,
number = 1,
pages = {165--182},
year = 2002,
publisher = {Springer}
}
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author = { Ibrahim H. Osman and Chris N. Potts },
title = {Simulated Annealing for Permutation Flow-Shop
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volume = 17,
number = 6,
pages = {551--557}
}
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pages = {297--307},
annote = {Proposed beam search}
}
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author = { G{\"u}l {\"O}zerol and Esra Karasakal },
title = {Interactive outranking approaches for multicriteria
decision-making problems with imprecise information},
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}
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title = {A branch-and-cut algorithm for the resolution of large-scale symmetric traveling salesman problems},
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@article{PagStu2016,
author = { Federico Pagnozzi and Thomas St{\"u}tzle },
title = {Speeding up Local Search for the Insert Neighborhood in the Weighted Tardiness Permutation Flowshop Problem},
journal = {Optimization Letters},
year = 2017,
volume = 11,
pages = {1283--1292},
doi = {10.1007/s11590-016-1086-5}
}
@article{PagStu2019:ejor,
author = { Federico Pagnozzi and Thomas St{\"u}tzle },
title = {Automatic Design of Hybrid Stochastic Local Search Algorithms
for Permutation Flowshop Problems},
journal = {European Journal of Operational Research},
year = 2019,
volume = 276,
pages = {409--421},
issue = 2,
doi = {10.1016/j.ejor.2019.01.018},
keywords = {EMILI},
abstract = {Stochastic local search methods are at the core of many
effective heuristics for tackling different permutation
flowshop problems (PFSPs). Usually, such algorithms require a
careful, manual algorithm engineering effort to reach high
performance. An alternative to the manual algorithm
engineering is the automated design of effective SLS
algorithms through building flexible algorithm frameworks and
using automatic algorithm configuration techniques to
instantiate high-performing algorithms. In this paper, we
automatically generate new high-performing algorithms for
some of the most widely studied variants of the PFSP. More in
detail, we (i) developed a new algorithm framework, EMILI,
that implements algorithm-specific and problem-specific
building blocks; (ii) define the rules of how to compose
algorithms from the building blocks; and (iii) employ an
automatic algorithm configuration tool to search for high
performing algorithm configurations. With these ingredients,
we automatically generate algorithms for the PFSP with the
objectives makespan, total completion time and total
tardiness, which outperform the best algorithms obtained by a
manual algorithm engineering process.}
}
@article{PagStu2020:itor,
author = { Federico Pagnozzi and Thomas St{\"u}tzle },
title = {Evaluating the impact of grammar complexity in automatic algorithm design},
journal = {International Transactions in Operational Research},
pages = {1--26},
doi = {10.1111/itor.12902},
year = 2020
}
@article{PagStu2021:orp,
author = { Federico Pagnozzi and Thomas St{\"u}tzle },
title = {Automatic design of hybrid stochastic local search algorithms
for permutation flowshop problems with additional
constraints},
journal = {Operations Research Perspectives},
year = 2021,
volume = 8,
pages = 100180,
doi = {10.1016/j.orp.2021.100180},
abstract = {Automatic design of stochastic local search algorithms has
been shown to be very effective in generating algorithms for
the permutation flowshop problem for the most studied
objectives including makespan, flowtime and total
tardiness. The automatic design system uses a configuration
tool to combine algorithmic components following a set of
rules defined as a context-free grammar. In this paper we use
the same system to tackle two of the most studied additional
constraints for these objectives: sequence dependent setup
times and no-idle constraint. Additional components have been
added to adapt the system to the new problems while keeping
intact the grammar structure and the experimental setup. The
experiments show that the generated algorithms outperform the
state of the art in each case.}
}
@article{PajBlaHerMar2021archiving,
title = {A Comparison of Archiving Strategies for Characterization of
Nearly Optimal Solutions under Multi-Objective Optimization},
author = {Pajares, Alberto and Blasco, Xavier and Herrero, Juan Manuel
and Mart{\'i}nez, Miguel A.},
journal = {Mathematics},
year = 2021,
doi = {10.3390/math9090999},
volume = 9,
number = 9,
pages = {999},
abstract = {In a multi-objective optimization problem, in addition to
optimal solutions, multimodal and/or nearly optimal
alternatives can also provide additional useful information
for the decision maker. However, obtaining all nearly optimal
solutions entails an excessive number of
alternatives. Therefore, to consider the nearly optimal
solutions, it is convenient to obtain a reduced set, putting
the focus on the potentially useful alternatives. These
solutions are the alternatives that are close to the optimal
solutions in objective space, but which differ significantly
in the decision space. To characterize this set, it is
essential to simultaneously analyze the decision and
objective spaces. One of the crucial points in an
evolutionary multi-objective optimization algorithm is the
archiving strategy. This is in charge of keeping the solution
set, called the archive, updated during the optimization
process. The motivation of this work is to analyze the three
existing archiving strategies proposed in the literature
(ArchiveUpdate$P_{Q,\epsilon}D_{xy}$, Archive\_nevMOGA, and
targetSelect) that aim to characterize the potentially useful
solutions. The archivers are evaluated on two benchmarks and
in a real engineering example. The contribution clearly shows
the main differences between the three archivers. This
analysis is useful for the design of evolutionary algorithms
that consider nearly optimal solutions.},
keywords = {multi-objective optimization; nearly optimal solutions;
non-epsilon dominance; multimodality; decision space
diversity; archiving strategy; evolutionary algorithm;
non-linear parametric identification}
}
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author = {Daniel {Palhazi Cuervo} and Peter Goos and Kenneth S{\"o}rensen and Emely Arr{\'{a}}iz},
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}
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title = {Iterated tabu search for the unconstrained binary quadratic
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year = 2006,
publisher = {Institute of Mathematics and Informatics},
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}
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title = {Local Search Methods for the Flowshop Scheduling Problem with Flowtime Minimization},
author = {Pan, Quan-Ke and Rub{\'e}n Ruiz },
journal = {European Journal of Operational Research},
volume = 222,
number = 1,
pages = {31--43},
year = 2012
}
@article{PanRui2013cor,
title = {A Comprehensive Review and Evaluation of Permutation
Flowshop Heuristics to Minimize Flowtime},
author = {Pan, Quan-Ke and Rub{\'e}n Ruiz },
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volume = 40,
number = 1,
pages = {117--128},
year = 2013
}
@article{PanRuiAlf2017:cor,
author = { Quan-Ke Pan and Rub{\'e}n Ruiz and Pedro Alfaro-Fern{\'a}ndez },
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volume = 80,
pages = {50--60}
}
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title = {A Discrete Differential Evolution Algorithm for the
Permutation Flowshop Scheduling Problem },
journal = {Computers and Industrial Engineering},
volume = 55,
number = 4,
pages = {795 -- 816},
year = 2008,
author = {Quan-Ke Pan and Mehmet Fatih Tasgetiren and Yun-Chia
Liang}
}
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year = 2008,
journal = {International Journal of Advanced Manufacturing Technology},
volume = 38,
number = {7-8},
title = {An improved iterated greedy algorithm for the
no-wait flow shop scheduling problem with makespan
criterion},
author = {Pan, Quan-Ke and Wang, Ling and Zhao, Bao-Hua},
pages = {778--786}
}
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title = {A survey on transfer learning},
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year = 2009
}
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author = { Lu{\'i}s Paquete and Tommaso Schiavinotto and Thomas St{\"u}tzle },
title = {On Local Optima in Multiobjective Combinatorial
Optimization Problems},
journal = {Annals of Operations Research},
year = 2007,
volume = 156,
pages = {83--97},
doi = {10.1007/s10479-007-0230-0},
keywords = {Pareto local search, PLS},
abstract = {In this article, local optimality in multiobjective
combinatorial optimization is used as a baseline for
the design and analysis of two iterative improvement
algorithms. Both algorithms search in a neighborhood
that is defined on a collection of sets of feasible
solutions and their acceptance criterion is based on
outperformance relations. Proofs of the soundness
and completeness of these algorithms are given.}
}
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author = { Lu{\'i}s Paquete and Thomas St{\"u}tzle },
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the biobjective {QAP} with correlated flow matrices},
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year = 2006,
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number = 3,
pages = {943--959}
}
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author = { Lu{\'i}s Paquete and Thomas St{\"u}tzle },
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Reproducibility Challenge; code reusability}
}
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author = { P{\'e}rez C{\'a}ceres, Leslie and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
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year = 2015,
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author = {P{\'{e}}res, Matias and Ruiz, Germ{\'{a}}n and Nesmachnow,
Sergio and Olivera, Ana C.},
journal = {Applied Soft Computing},
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year = 2018,
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}
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Competition. Our broad-search strategy utilized several
recently developed phase-free metrics for rapidly narrowing
the search options. Two different, adaptive, branch-and-prune
strategies were employed to build up asteroid sequences using
a rendezvous-flyby-rendezvous building block, with a robust
local optimizer in the loop. The best of these sequences were
refined end-to-end using the same direct optimizer, to yield
the winning 18-point, 18-asteroid solution.}
}
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year = 2006,
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year = 1996,
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year = 2005
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}
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year = 2007,
keywords = {Ant colony optimization, Material requirements
planning, Multi-level lot-sizing, {Wagner-Whitin}
algorithm},
pages = {2533--2552},
doi = {10.1016/j.cor.2005.09.022},
abstract = { In this paper, we present an ant-based algorithm
for solving unconstrained multi-level lot-sizing
problems called ant system for multi-level
lot-sizing algorithm {(ASMLLS).} We apply a hybrid
approach where we use ant colony optimization in
order to find a good lot-sizing sequence, i.e. a
sequence of the different items in the product
structure in which we apply a modified
{Wagner-Whitin} algorithm for each item
separately. Based on the setup costs each ant
generates a sequence of items. Afterwards a simple
single-stage lot-sizing rule is applied with
modified setup costs. This modification of the setup
costs depends on the position of the item in the
lot-sizing sequence, on the items which have been
lot-sized before, and on two further parameters,
which are tried to be improved by a systematic
search. For small-sized problems {ASMLLS} is among
the best algorithms, but for most medium- and
large-sized problems it outperforms all other
approaches regarding solution quality as well as
computational time.}
}
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doi = {10.3389/fninf.2017.00076},
year = 2018,
month = jan,
publisher = {Frontiers Media {SA}},
volume = 11,
author = {Hans E. Plesser},
title = {Reproducibility vs.\ Replicability: A Brief History of a
Confused Terminology},
journal = {Frontiers in Neuroinformatics}
}
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Eduardo and León, Coromoto and Rodríguez-León, Casiano},
title = {Application of Multi-Objective Evolutionary Algorithms for
Planning Healthy and Balanced School Lunches},
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publisher = {{MDPI} {AG}},
doi = {10.3390/math9010080},
abstract = {A multi-objective formulation of the Menu Planning Problem,
which is termed the Multi-objective Menu Planning Problem, is
presented herein. Menu planning is of great interest in the
health field due to the importance of proper nutrition in
today's society, and particularly, in school canteens. In
addition to considering the cost of the meal plan as the
classic objective to be minimized, we also introduce a second
objective aimed at minimizing the degree of repetition of
courses and food groups that a particular meal plan consists
of. The motivation behind this particular multi-objective
formulation is to offer a meal plan that is not only
affordable but also varied and balanced from a nutritional
standpoint. The plan is designed for a given number of days
and ensures that the specific nutritional requirements of
school-age children are satisfied. The main goal of the
current work is to demonstrate the multi-objective nature of
the said formulation, through a comprehensive experimental
assessment carried out over a set of multi-objective
evolutionary algorithms applied to different instances. At
the same time, we are also interested in validating the
multi-objective formulation by performing quantitative and
qualitative analyses of the solutions attained when solving
it. Computational results show the multi-objective nature of
the said formulation, as well as that it allows suitable meal
plans to be obtained.}
}
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Optimization Algorithm},
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doi = {10.1016/j.cor.2004.11.019},
abstract = { The problem of connecting a set of client nodes
with known demands to a root node through a minimum
cost tree network, subject to capacity constraints
on all links is known as the capacitated minimum
spanning tree {(CMST)} problem. As the problem is
{NP-hard,} we propose a hybrid ant colony
optimization {(ACO)} algorithm to tackle it
heuristically. The algorithm exploits two important
problem characteristics: (i) the {CMST} problem is
closely related to the capacitated vehicle routing
problem {(CVRP),} and (ii) given a clustering of
client nodes that satisfies capacity constraints,
the solution is to find a {MST} for each cluster,
which can be done exactly in polynomial time. Our
{ACO} exploits these two characteristics of the
{CMST} by a solution construction originally
developed for the {CVRP.} Given the {CVRP} solution,
we then apply an implementation of Prim's algorithm
to each cluster to obtain a feasible {CMST}
solution. Results from a comprehensive computational
study indicate the efficiency and effectiveness of
the proposed approach.}
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multi-attribute weighting, SMART, Swing}
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problems, the model with selection based on features, and the
model with variable performance criteria. One objective of
this chapter is to explore the applicability of the
approximation theory to the algorithm selection
problem. There is an intimate relationship here and that the
approximation theory forms an appropriate base upon which to
develop a theory of algorithm selection methods. The
approximation theory currently lacks much of the necessary
machinery for the algorithm selection problem. There is a
need to develop new results and apply known techniques to
these new circumstances. The final pages of this chapter form
a sort of appendix, which lists 15 specific open problems and
questions in this area. There is a close relationship between
the algorithm selection problem and the general optimization
theory. This is not surprising since the approximation
problem is a special form of the optimization problem. Most
realistic algorithm selection problems are of moderate to
high dimensionality and thus one should expect them to be
quite complex. One consequence of this is that most
straightforward approaches (even well-conceived ones) are
likely to lead to enormous computations for the best
selection. The single most important part of the solution of
a selection problem is the appropriate choice of the form for
selection mapping. It is here that theories give the least
guidance and that the art of problem solving is most
crucial.}
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author = { Rivadeneira, Luc{\'i}a and Yang, Jian-Bo and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Predicting tweet impact using a novel evidential reasoning
prediction method},
journal = {Expert Systems with Applications},
year = 2021,
volume = 169,
pages = 114400,
month = may,
doi = {10.1016/j.eswa.2020.114400},
abstract = {This study presents a novel evidential reasoning (ER)
prediction model called MAKER-RIMER to examine how different
features embedded in Twitter posts (tweets) can predict the
number of retweets achieved during an electoral campaign. The
tweets posted by the two most voted candidates during the
official campaign for the 2017 Ecuadorian Presidential
election were used for this research. For each tweet, five
features including type of tweet, emotion, URL, hashtag, and
date are identified and coded to predict if tweets are of
either high or low impact. The main contributions of the new
proposed model include its suitability to analyse tweet
datasets based on likelihood analysis of data. The model is
interpretable, and the prediction process relies only on the
use of available data. The experimental results show that
MAKER-RIMER performed better, in terms of misclassification
error, when compared against other predictive machine
learning approaches. In addition, the model allows observing
which features of the candidates' tweets are linked to high
and low impact. Tweets containing allusions to the contender
candidate, either with positive or negative connotations,
without hashtags, and written towards the end of the
campaign, were persistently those with the highest
impact. URLs, on the other hand, is the only variable that
performs differently for the two candidates in terms of
achieving high impact. MAKER-RIMER can provide campaigners of
political parties or candidates with a tool to measure how
features of tweets are predictors of their impact, which can
be useful to tailor Twitter content during electoral
campaigns.},
keywords = {Evidential reasoning rule,Belief rule-based inference,Maximum
likelihood data analysis,Twitter,Retweet,Prediction}
}
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both data sets are discrete or when both data sets are
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for the case of one discrete data set and one continuous data
set. This case applies when measuring, for example, the
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abstract = {When solving multiobjective optimization problems,
preference-based evolutionary multiobjective optimization
(EMO) algorithms introduce preference information into an
evolutionary algorithm in order to focus the search for
objective vectors towards the region of interest of the
Pareto optimal front. In this paper, we suggest a
preference-based EMO algorithm called weighting achievement
scalarizing function genetic algorithm (WASF-GA), which
considers the preferences of the decision maker (DM)
expressed by means of a reference point. The main purpose of
WASF-GA is to approximate the region of interest of the
Pareto optimal front determined by the reference point, which
contains the Pareto optimal objective vectors that obey the
preferences expressed by the DM in the best possible way. The
proposed approach is based on the use of an achievement
scalarizing function (ASF) and on the classification of the
individuals into several fronts. At each generation of
WASF-GA, this classification is done according to the values
that each solution takes on the ASF for the reference point
and using different weight vectors. These vectors of weights
are selected so that the vectors formed by their inverse
components constitute a well-distributed representation of
the weight vectors space. The efficiency and usefulness of
WASF-GA is shown in several test problems in comparison to
other preference-based EMO algorithms. Regarding a metric
based on the hypervolume, we can say that WASF-GA has
outperformed the other algorithms considered in most of the
problems.}
}
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Support Vector Machine},
annote = {Proposed EGO-LS-SVM}
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}
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problem of planning individual tourist routes in a
city. Each point of interest in a city provides
different benefits for different categories (e.g.,
culture, shopping). Each tourist has different
preferences for the different categories when
selecting and visiting the points of interests
(e.g., museums, churches). Hence, a multi-objective
decision situation arises. To determine all the
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techniques are developed and applied. We use the
Pareto ant colony optimization algorithm and extend
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solutions for {MOPs}},
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number = 4,
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Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren
and Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and Polat,
{\.I}lhan and Feng, Yu and Moore, Eric W. and {VanderPlas},
Jake and Laxalde, Denis and Perktold, Josef and Cimrman,
Robert and Henriksen, Ian and Quintero, E. A. and Harris,
Charles R. and Archibald, Anne M. and Ribeiro, Ant{\^o}nio
H. and Pedregosa, Fabian and {van Mulbregt}, Paul and {SciPy
1.0 Contributors}},
author = {Virtanen, Pauli and others},
title = {{SciPy} 1.0: Fundamental Algorithms for Scientific Computing
in {Python}},
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eprint = {http://www.jstor.org/stable/2004840},
keywords = {BFGS}
}
@article{ShaLopAlm2023hidden,
author = { Shavarani, Seyed Mahdi and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Allmendinger, Richard },
title = {Detecting Hidden and Irrelevant Objectives in Interactive
Multi-Objective Optimization},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2023,
volume = 28,
number = 2,
pages = {544--557},
doi = {10.1109/TEVC.2023.3269348},
abstract = {Evolutionary multi-objective optimization algorithms (EMOAs)
typically assume that all objectives that are relevant to the
decision-maker (DM) are optimized by the EMOA. In some
scenarios, however, there are irrelevant objectives that are
optimized by the EMOA but ignored by the DM, as well as,
hidden objectives that the DM considers when judging the
utility of solutions but are not optimized. This discrepancy
between the EMOA and the DM's preferences may impede the
search for the most-preferred solution and waste resources
evaluating irrelevant objectives. Research on objective
reduction has focused so far on the structure of the problem
and correlations between objectives and neglected the role of
the DM. We formally define here the concepts of irrelevant
and hidden objectives and propose methods for detecting them,
based on uni-variate feature selection and recursive feature
elimination, that use the preferences already elicited when a
DM interacts with a ranking-based interactive EMOA
(iEMOA). We incorporate the detection methods into an iEMOA
capable of dynamically switching the objectives being
optimized. Our experiments show that this approach can
efficiently identify which objectives are relevant to the DM
and reduce the number of objectives being optimized, while
keeping and often improving the utility, according to the DM,
of the best solution found.}
}
@article{ShaLopKno2023bench,
author = { Shavarani, Seyed Mahdi and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Joshua D. Knowles },
title = {On Benchmarking Interactive Evolutionary Multi-Objective
Algorithms},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2023,
volume = 28,
number = 4,
pages = {1084--1098},
doi = {10.1109/TEVC.2023.3289872},
abstract = {We carry out a detailed performance assessment of two
interactive evolutionary multi-objective algorithms (EMOAs)
using a machine decision maker that enables us to repeat
experiments and study specific behaviours modeled after human
decision makers (DMs). Using the same set of benchmark test
problems as in the original papers on these interactive EMOAs
(in up to 10 objectives), we bring to light interesting
effects when we use a machine DM based on sigmoidal utility
functions that have support from the psychology literature
(replacing the simpler utility functions used in the original
papers). Our machine DM enables us to go further and simulate
human biases and inconsistencies as well. Our results from
this study, which is the most comprehensive assessment of
multiple interactive EMOAs so far conducted, suggest that
current well-known algorithms have shortcomings that need
addressing. These results further demonstrate the value of
improving the benchmarking of interactive EMOAs}
}
@article{ShaLopMie2021visual,
title = {Visualizations for Decision Support in Scenario-based
Multiobjective Optimization},
author = { Shavazipour, Babooshka and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Kaisa Miettinen },
journal = {Information Sciences},
volume = 578,
pages = {1--21},
year = 2021,
abstract = {We address challenges of decision problems when managers need
to optimize several conflicting objectives simultaneously
under uncertainty. We propose visualization tools to support
the solution of such scenario-based multiobjective
optimization problems. Suitable graphical visualizations are
necessary to support managers in understanding, evaluating,
and comparing the performances of management decisions
according to all objectives in all plausible scenarios. To
date, no appropriate visualization has been suggested. This
paper fills this gap by proposing two visualization methods:
a novel extension of empirical attainment functions for
scenarios and an adapted version of heatmaps. They help a
decision-maker in gaining insight into realizations of
trade-offs and comparisons between objective functions in
different scenarios. Some fundamental questions that a
decision-maker may wish to answer with the help of
visualizations are also identified. Several examples are
utilized to illustrate how the proposed visualizations
support a decision-maker in evaluating and comparing
solutions to be able to make a robust decision by answering
the questions. Finally, we validate the usefulness of the
proposed visualizations in a real-world problem with a real
decision-maker. We conclude with guidelines regarding which
of the proposed visualizations are best suited for different
problem classes.},
doi = {10.1016/j.ins.2021.07.025},
supplement = {https://doi.org/10.5281/zenodo.5040421}
}
@article{ShaPiSha2017:asoco,
author = { Weishi Shao and Dechang Pi and Zhongshi Shao },
title = {Memetic algorithm with node and edge histogram for no-idle flow shop scheduling problem to minimize the makespan criterion},
journal = {Applied Soft Computing},
volume = 54,
pages = {164--182},
year = 2017
}
@article{ShaPiSha2018:cor,
author = { Weishi Shao and Dechang Pi and Zhongshi Shao },
title = {A hybrid discrete teaching-learning based meta-heuristic for solving no-idle flow shop scheduling problem with total tardiness criterion},
journal = {Computers \& Operations Research},
volume = 94,
pages = {89--105},
year = 2018
}
@article{ShaShuIsh2023is,
doi = {10.1016/j.ins.2022.11.155},
year = 2023,
volume = 622,
pages = {755--770},
author = {Ke Shang and Tianye Shu and Ishibuchi, Hisao and Yang Nan and
Lie Meng Pang},
title = {Benchmarking large-scale subset selection in evolutionary
multi-objective optimization},
journal = {Information Sciences}
}
@article{ShaSte2019deep,
author = { Shavazipour, Babooshka and T. J. Stewart },
title = {Multi-objective optimisation under deep uncertainty},
journal = {Operational Research},
year = 2019,
month = sep,
abstract = {This paper presents a scenario-based Multi-Objective
structure to handle decision problems under deep
uncertainty. Most of the decisions in real-life problems need
to be made in the absence of complete knowledge about the
consequences of the decision and/or are characterised by
uncertainties about the future which is unpredictable. These
uncertainties are almost impossible to reduce by gathering
more information and are not statistical in
nature. Therefore, classical probability-based approaches,
such as stochastic programming, do not address these
problems; as they require a correctly-defined complete sample
space, strong assumptions (e.g. normality), or both. The
proposed method extends the concept of two-stage stochastic
programming with recourse to address the capability of
dealing with deep uncertainty through the use of scenario
planning rather than statistical expectation. In this
research, scenarios are used as a dimension of preference to
avoid problems relating to the assessment and use of
probabilities under deep uncertainty. Such scenario-based
thinking involved a multi-objective representation of
performance under different future conditions as an
alternative to expectation. To the best of our knowledge,
this is the first attempt of performing a multi-criteria
evaluation under deep uncertainty through a structured
optimisation model. The proposed structure replacing
probabilities (in dynamic systems with deep uncertainties) by
aspirations within a goal programming structure. In fact,
this paper also proposes an extension of the goal programming
paradigm to deal with deep uncertainty. Furthermore, we will
explain how this structure can be modelled, implemented, and
solved by Goal Programming using some simple, but not
trivial, examples. Further discussion and comparisons with
some popular existing methods will also provided to highlight
the superiorities of the proposed structure.},
doi = {10.1007/s12351-019-00512-1}
}
@article{ShaStrSte2020cce,
author = { Shavazipour, Babooshka and Jonas Stray and T. J. Stewart },
title = {Sustainable planning in sugar-bioethanol supply chain under
deep uncertainty: A case study of {South} {African} sugarcane
industry},
journal = {Computers \& Chemical Engineering},
volume = 143,
pages = 107091,
year = 2020,
doi = {10.1016/j.compchemeng.2020.107091},
keywords = {Supply chain management, Multi-objective optimisation, Deep
uncertainty, Scenario planning, Renewable energy,},
abstract = {In this paper, the strategic planning of sugar-bioethanol
supply chains (SCs) under deep uncertainty has been addressed
by applying a two-stage scenario-based multiobjective
optimisation methodology. In practice, the depth of
uncertainty is very high, potential outcomes are not
precisely enumerable, and probabilities of outcomes are not
properly definable. To date, no appropriate framework has
been suggested for dealing with deep uncertainty in supply
chain management and energy-related problems. This study is
the first try to fills this gap. Particularly, the
sustainability of the whole infrastructure of the
sugar-bioethanol SCs is analysed in such a way that the final
solutions are sustainable, robust and adaptable for a broad
range of plausible futures. Three objectives are considered
in this problem under six uncertain parameters. A case study
of South African sugarcane industry is utilised to study and
examine the proposed model. The results prove the economic
profitability and sustainability of the project.}
}
@article{ShaSweWan2016taking,
author = {Shahriari, B. and Swersky, K. and Wang, Z. and Adams, R. P. and Nando de Freitas },
journal = {Proceedings of the IEEE},
title = {Taking the human out of the loop: A review of {Bayesian}
optimization},
year = 2016,
number = 1,
pages = {148--175},
volume = 104,
publisher = {IEEE}
}
@article{ShaSweWanAdaFre2016,
author = {Bobak Shahriari and Kevin Swersky and Ziyu Wang and Ryan P. Adams and Nando de Freitas },
title = {Taking the Human Out of the Loop: {A} Review of {Bayesian} Optimization},
journal = {Proceedings of the IEEE},
year = 2016,
volume = 104,
number = 1,
pages = {148--175}
}
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title = {Niching with derandomized evolution strategies in artificial
and real-world landscapes},
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number = 1,
pages = {171--196},
year = 2009,
doi = {10.1007/s11047-007-9065-5},
publisher = {Springer}
}
@article{ShiCebLoz2018space,
author = {Shirazi, Abolfazl and Josu Ceberio and Jos{\'e} A. Lozano },
title = {Spacecraft trajectory optimization: A review of models,
objectives, approaches and solutions},
journal = { Progress in Aerospace Sciences },
year = 2018,
volume = 102,
pages = {76--98},
month = oct,
doi = {10.1016/j.paerosci.2018.07.007}
}
@article{ShiMarDud2008stat,
author = {David Shilane and Jarno Martikainen and Sandrine Dudoit and
Seppo J. Ovaska},
title = {A general framework for statistical performance comparison of
evolutionary computation algorithms},
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volume = 178,
number = 14,
pages = {2870--2879},
year = 2008,
doi = {10.1016/j.ins.2008.03.007}
}
@article{ShiZha2016,
title = {The generalization of {Latin} hypercube sampling},
author = {Shields, Michael D. and Zhang, Jiaxin},
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year = 2016,
pages = {96--108},
volume = 148
}
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author = { A. Shmygelska and Holger H. Hoos },
title = {An Ant Colony Optimisation Algorithm for the {2D}
and {3D} Hydrophobic Polar Protein Folding Problem},
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}
@article{SilConRey2023automatic,
author = { Silva-Mu\~noz, Mois\'es and Contreras-Bolton, Carlos and Rey, Carlos and
Parada, Victor},
title = {Automatic generation of a hybrid algorithm for the maximum
independent set problem using genetic programming},
journal = {Applied Soft Computing},
year = 2023,
pages = 110474,
publisher = {Elsevier},
doi = {10.1016/j.asoc.2023.110474}
}
@article{SilFraBer2021,
author = { Silva-Mu\~noz, Mois\'es and Alberto Franzin and Hughes Bersini },
title = {Automatic configuration of the {Cassandra} database using irace},
year = 2021,
journal = {{PeerJ} Computer Science},
volume = 7,
pages = {e634},
doi = {10.7717/peerj-cs.634}
}
@article{SilRit2017:cor,
author = {Paulo Vitor Silvestrin and Marcus Ritt},
title = {An Iterated Tabu Search for the Multi-compartment Vehicle Routing Problem},
journal = {Computers \& Operations Research},
year = 2017,
volume = 81,
pages = {192--202}
}
@article{SilSubOch2015,
author = {Marcos {Melo Silva} and Anand Subramanian and Luiz Satoru Ochi },
title = {An Iterated Local Search Heuristic for the Split Delivery Vehicle
Routing Problem},
journal = {Computers \& Operations Research},
year = 2015,
volume = 53,
pages = {234--249}
}
@article{SimChaThi2014:swarm,
author = {Olivier Simonin and Fran{\c{c}}ois Charpillet and Eric Thierry},
title = {Revisiting wavefront construction with collective agents: an approach to foraging},
journal = {Swarm Intelligence},
year = 2014,
volume = 9,
number = 2,
pages = {113--138},
doi = {10.1007/s11721-014-0093-3},
keywords = {irace}
}
@article{SimHarPae2015ecj,
author = {Kevin Sim and Emma Hart and Ben Paechter },
title = {A Lifelong Learning Hyper-heuristic Method for Bin Packing},
volume = 23,
number = 1,
pages = {37--67},
year = 2015,
doi = {10.1162/EVCO_a_00121},
journal = {Evolutionary Computation}
}
@article{SimNelSim2011phack,
author = {Simmons, Joseph P. and Nelson, Leif D. and Simonsohn, Uri},
title = {False-Positive Psychology: Undisclosed Flexibility in Data
Collection and Analysis Allows Presenting Anything as
Significant},
journal = {Psychological Science},
year = 2011,
url = {https://ssrn.com/abstract=1850704},
annote = {Proposed the term p-hacking}
}
@article{SimNew1958heur,
title = {Heuristic Problem Solving: The Next Advance in Operations
Research},
volume = 6,
doi = {10.1287/opre.6.1.1},
number = 1,
journal = {Operations Research},
author = { Simon, Herbert A. and Newell, Allen},
year = 1958,
pages = {1--10}
}
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author = {Simmons, Joseph P. and LeBoeuf, Robyn A. and Nelson, Leif D.},
title = {The effect of accuracy motivation on anchoring and
adjustment: {Do} people adjust from provided anchors?},
journal = {Journal of Personality and Social Psychology},
year = 2010,
volume = 99,
number = 6,
pages = {917--932},
issn = {1939-1315, 0022-3514},
shorttitle = {The effect of accuracy motivation on anchoring and
adjustment},
doi = {10.1037/a0021540},
abstract = {Increasing accuracy motivation (e.g., by providing monetary
incentives for accuracy) often fails to increase adjustment
away from provided anchors, a result that has led researchers
to conclude that people do not effortfully adjust away from
such anchors. We challenge this conclusion. First, we show
that people are typically uncertain about which way to adjust
from provided anchors and that this uncertainty often causes
people to believe that they have initially adjusted too far
away from such anchors (Studies 1a and 1b). Then, we show
that although accuracy motivation fails to increase the gap
between anchors and final estimates when people are uncertain
about the direction of adjustment, accuracy motivation does
increase anchor-estimate gaps when people are certain about
the direction of adjustment, and that this is true regardless
of whether the anchors are provided or self-generated
(Studies 2, 3a, 3b, and 5). These results suggest that people
do effortfully adjust away from provided anchors but that
uncertainty about the direction of adjustment makes that
adjustment harder to detect than previously assumed. This
conclusion has important theoretical implications, suggesting
that currently emphasized distinctions between anchor types
(self-generated vs. provided) are not fundamental and that
ostensibly competing theories of anchoring (selective
accessibility and anchoring-and-adjustment) are
complementary.},
language = {en}
}
@article{Simon1955,
author = { Simon, Herbert A. },
title = {A Behavioral Model of Rational Choice},
journal = {The Quarterly Journal of Economics},
volume = 69,
number = 1,
pages = {99--118},
year = 1955,
epub = {http://www.jstor.org/stable/1884852}
}
@article{SinIsaTap2011pareto,
author = {Singh, Hemant Kumar and Isaacs, Amitay and Ray, Tapabrata },
title = {A {Pareto} Corner Search Evolutionary Algorithm and
Dimensionality Reduction in Many-Objective Optimization
Problems},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2011,
volume = 15,
number = 4,
pages = {539--556},
abstract = {Many-objective optimization refers to the optimization
problems containing large number of objectives, typically
more than four. Non-dominance is an inadequate strategy for
convergence to the Pareto front for such problems, as almost
all solutions in the population become non-dominated,
resulting in loss of convergence pressure. However, for some
problems, it may be possible to generate the Pareto front
using only a few of the objectives, rendering the rest of the
objectives redundant. Such problems may be reducible to a
manageable number of relevant objectives, which can be
optimized using conventional multiobjective evolutionary
algorithms (MOEAs). For dimensionality reduction, most
proposals in the paper rely on analysis of a representative
set of solutions obtained by running a conventional MOEA for
a large number of generations, which is computationally
overbearing. A novel algorithm, Pareto corner search
evolutionary algorithm (PCSEA), is introduced in this paper,
which searches for the corners of the Pareto front instead of
searching for the complete Pareto front. The solutions
obtained using PCSEA are then used for dimensionality
reduction to identify the relevant objectives. The potential
of the proposed approach is demonstrated by studying its
performance on a set of benchmark test problems and two
engineering examples. While the preliminary results obtained
using PCSEA are promising, there are a number of areas that
need further investigation. This paper provides a number of
useful insights into dimensionality reduction and, in
particular, highlights some of the roadblocks that need to be
cleared for future development of algorithms attempting to
use few selected solutions for identifying relevant
objectives},
doi = {10.1109/TEVC.2010.2093579}
}
@article{SinPin1998:IIE,
author = {Marcos Singer and Michael L. Pinedo},
title = {A Computational Study of Branch and Bound Techniques for Minimizing the Total Weighted Tardiness in Job Shops},
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year = 1998,
volume = 30,
number = 2,
pages = {109--118}
}
@article{SinSaxDeb2013asc,
author = {Ankur Sinha and Saxena, Dhish Kumar and Kalyanmoy Deb and Ashutosh Tiwari },
title = {Using objective reduction and interactive procedure to handle
many-objective optimization problems},
journal = {Applied Soft Computing},
volume = 13,
number = 1,
pages = {415--427},
year = 2013,
doi = {10.1016/j.asoc.2012.08.030},
keywords = {Evolutionary algorithms, Evolutionary multi- and
many-objective optimization, Multi-criteria decision making,
Machine learning, Interactive optimization},
abstract = {A number of practical optimization problems are posed as
many-objective (more than three objectives) problems. Most of
the existing evolutionary multi-objective optimization
algorithms, which target the entire Pareto-front are not
equipped to handle many-objective problems. Though there have
been copious efforts to overcome the challenges posed by such
problems, there does not exist a generic procedure to
effectively handle them. This paper presents a simplify and
solve framework for handling many-objective optimization
problems. In that, a given problem is simplified by
identification and elimination of the redundant objectives,
before interactively engaging the decision maker to converge
to the most preferred solution on the Pareto-optimal
front. The merit of performing objective reduction before
interacting with the decision maker is two fold. Firstly, the
revelation that certain objectives are redundant,
significantly reduces the complexity of the optimization
problem, implying lower computational cost and higher search
efficiency. Secondly, it is well known that human beings are
not efficient in handling several factors (objectives in the
current context) at a time. Hence, simplifying the problem a
priori addresses the fundamental issue of cognitive overload
for the decision maker, which may help avoid inconsistent
preferences during the different stages of interactive
engagement. The implementation of the proposed framework is
first demonstrated on a three-objective problem, followed by
its application on two real-world engineering problems.}
}
@article{SinBahRay2019distance,
title = {Distance-based subset selection for benchmarking in
evolutionary multi/many-objective optimization},
author = {Singh, Hemant Kumar and Bhattacharjee, Kalyan Shankar and Ray, Tapabrata },
journal = {IEEE Transactions on Evolutionary Computation},
year = 2019,
number = 5,
pages = {904--912},
volume = 23,
publisher = {IEEE}
}
@article{SioGag2018:ejor,
author = {Sioud, Aymen and Caroline Gagn{\'e} },
title = {Enhanced migrating birds optimization algorithm for the permutation flow shop problem with sequence dependent setup times},
journal = {European Journal of Operational Research},
volume = 264,
number = 1,
pages = {66--73},
year = 2018
}
@article{SmaMcCAll2011efficient,
title = {Efficient discovery of anti-inflammatory small-molecule
combinations using evolutionary computing},
author = {Small, Ben G. and McColl, Barry W. and Allmendinger, Richard and Pahle, J{\"u}rgen and L{\'o}pez-Castej{\'o}n, Gloria and
Rothwell, Nancy J. and Joshua D. Knowles and Mendes, Pedro and
Brough, David and Kell, Douglas B.},
journal = {Nature Chemical Biology},
volume = 7,
number = 12,
pages = {902--908},
year = 2011,
publisher = {Nature Publishing Group}
}
@article{SmiBaaWreLew2014isa,
author = { Kate Smith{-}Miles and Baatar, Davaatseren and Wreford, Brendan and Lewis, Rhyd M. R. },
title = {Towards Objective Measures of Algorithm Performance across
Instance Space},
journal = {Computers \& Operations Research},
year = 2014,
volume = 45,
pages = {12--24},
doi = {10.1016/j.cor.2013.11.015},
abstract = {This paper tackles the difficult but important task of
objective algorithm performance assessment for
optimization. Rather than reporting average performance of
algorithms across a set of chosen instances, which may bias
conclusions, we propose a methodology to enable the strengths
and weaknesses of different optimization algorithms to be
compared across a broader instance space. The results
reported in a recent Computers and Operations Research paper
comparing the performance of graph coloring heuristics are
revisited with this new methodology to demonstrate (i) how
pockets of the instance space can be found where algorithm
performance varies significantly from the average performance
of an algorithm; (ii) how the properties of the instances can
be used to predict algorithm performance on previously unseen
instances with high accuracy; and (iii) how the relative
strengths and weaknesses of each algorithm can be visualized
and measured objectively.},
keywords = {Algorithm selection; Instance Space Analysis; Graph coloring;
Heuristics; Performance prediction}
}
@article{SmiBow2015:cor,
author = { Kate Smith{-}Miles and Simon Bowly},
title = {Generating New Test Instances by Evolving in Instance Space},
journal = {Computers \& Operations Research},
year = 2015,
volume = 63,
pages = {102--113},
doi = {10.1016/j.cor.2015.04.022},
abstract = {Our confidence in the future performance of any algorithm,
including optimization algorithms, depends on how carefully
we select test instances so that the generalization of
algorithm performance on future instances can be inferred. In
recent work, we have established a methodology to generate a
2-d representation of the instance space, comprising a set of
known test instances. This instance space shows the
similarities and differences between the instances using
measurable features or properties, and enables the
performance of algorithms to be viewed across the instance
space, where generalizations can be inferred. The power of
this methodology is the insights that can be generated into
algorithm strengths and weaknesses by examining the regions
in instance space where strong performance can be
expected. The representation of the instance space is
dependent on the choice of test instances however. In this
paper we present a methodology for generating new test
instances with controllable properties, by filling observed
gaps in the instance space. This enables the generation of
rich new sets of test instances to support better the
understanding of algorithm strengths and weaknesses. The
methodology is demonstrated on graph colouring as a case
study.},
keywords = {Benchmarking; Evolving instances; Graph colouring; Instance
space; Test instances}
}
@article{SmiChrMun2021where,
author = { Kate Smith{-}Miles and Jeffrey Christiansen and Mario A. Mu{\~{n}}oz },
title = {Revisiting Where Are the Hard Knapsack Problems? Via
{Instance} {Space} {Analysis}},
journal = {Computers \& Operations Research},
year = 2021,
volume = 128,
pages = 105184,
doi = {10.1016/j.cor.2020.105184},
keywords = {0-1 Knapsack problem; Algorithm portfolios; Algorithm
selection; Instance difficulty; Instance generation; Instance
Space Analysis; Performance evaluation}
}
@article{SmiLop2012:cor,
author = { Kate Smith{-}Miles and Lopes, Leo},
title = {Measuring instance difficulty for combinatorial optimization
problems},
journal = {Computers \& Operations Research},
year = 2012,
volume = 39,
pages = {875--889}
}
@article{SmiMun2023isa,
author = { Kate Smith{-}Miles and Mario A. Mu{\~{n}}oz },
title = {Instance Space Analysis for Algorithm Testing: Methodology
and Software Tools},
journal = {{ACM} Computing Surveys},
year = 2023,
volume = 55,
number = 12,
month = mar,
issue_date = {December 2023},
doi = {10.1145/3572895},
abstract = {Instance Space Analysis (ISA) is a recently developed
methodology to (a) support objective testing of algorithms
and (b) assess the diversity of test instances. Representing
test instances as feature vectors, the ISA methodology
extends Rice's 1976 Algorithm Selection Problem framework to
enable visualization of the entire space of possible test
instances, and gain insights into how algorithm performance
is affected by instance properties. Rather than reporting
algorithm performance on average across a chosen set of test
problems, as is standard practice, the ISA methodology offers
a more nuanced understanding of the unique strengths and
weaknesses of algorithms across different regions of the
instance space that may otherwise be hidden on average. It
also facilitates objective assessment of any bias in the
chosen test instances and provides guidance about the
adequacy of benchmark test suites. This article is a
comprehensive tutorial on the ISA methodology that has been
evolving over several years, and includes details of all
algorithms and software tools that are enabling its worldwide
adoption in many disciplines. A case study comparing
algorithms for university timetabling is presented to
illustrate the methodology and tools.},
articleno = 255,
numpages = 31,
keywords = {test instance diversity, benchmarking, timetabling, Algorithm
footprints, MATLAB, software as a service, meta-heuristics,
algorithm selection, meta-learning}
}
@article{Smith-Miles2008,
author = { Kate Smith{-}Miles },
title = {Cross-disciplinary Perspectives on Meta-learning for Algorithm Selection},
journal = {{ACM} Computing Surveys},
year = 2008,
volume = 41,
number = 1,
pages = {1--25}
}
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author = { Krzysztof Socha and Christian Blum },
title = {An ant colony optimization algorithm for continuous
optimization: An application to feed-forward neural
network training},
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number = 3,
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}
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author = { Krzysztof Socha and Marco Dorigo },
title = {Ant Colony Optimization for Continuous Domains},
year = 2008,
journal = {European Journal of Operational Research},
volume = 185,
number = 3,
pages = {1155--1173},
doi = {10.1016/j.ejor.2006.06.046},
annote = {Proposed ACOR (ACO$_\mathbb{R}$)},
keywords = {ACOR}
}
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author = { Christine Solnon },
title = {Ants Can Solve Constraint Satisfaction Problems},
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year = 2002,
volume = 6,
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@article{SonWanHeJin2021kriging,
title = {A {Kriging}-assisted two-archive evolutionary algorithm for
expensive many-objective optimization},
author = {Song, Zhenshou and Wang, Handing and He, Cheng and Yaochu Jin },
journal = {IEEE Transactions on Evolutionary Computation},
volume = 25,
number = 6,
pages = {1013--1027},
year = 2021,
publisher = {IEEE}
}
@article{Sor2013,
title = {Metaheuristics---the metaphor exposed},
author = { Kenneth S{\"o}rensen },
journal = {International Transactions in Operational Research},
year = 2015,
volume = 22,
number = 1,
pages = {3--18},
doi = {10.1111/itor.12001}
}
@article{SorArnPal2017,
author = { Kenneth S{\"o}rensen and Florian Arnold and Daniel {Palhazi Cuervo}},
title = {A critical analysis of the ``improved {Clarke} and {Wright}
savings algorithm''},
journal = {International Transactions in Operational Research},
volume = 26,
number = 1,
pages = {54--63},
year = 2017,
doi = {10.1111/itor.12443},
keywords = {reproducibility, vehicle routing}
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successfully used to find good parameter settings for
optimization algorithms. Configuration is costly, because it
is necessary to evaluate many configurations on different
instances. For decision problems, when the objective is to
minimize the running time of the algorithm, many
configurators implement capping methods to discard poor
configurations early. Such methods are not directly
applicable to optimization problems, when the objective is to
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predefined running time limit. We propose new capping methods
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two aggressive methods, that save an average of about 20\%
and 45\% of the configuration effort, respectively. We also
provide evidence that capping can help to better use the
available budget in scenarios with a configuration time
limit.}
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@article{TerSumTam2021auto,
title = {Black-Box Optimization for Automated Discovery},
author = {Terayama, Kei and Sumita, Masato and Tamura, Ryo and Tsuda,
Koji},
year = 2021,
month = mar,
journal = {Accounts of Chemical Research},
volume = 54,
number = 6,
pages = {1334--1346},
publisher = {American Chemical Society},
doi = {10.1021/acs.accounts.0c00713},
abstract = {In chemistry and materials science, researchers and engineers
discover, design, and optimize chemical compounds or
materials with their professional knowledge and
techniques. At the highest level of abstraction, this process
is formulated as black-box optimization. For instance, the
trial-and-error process of synthesizing various molecules for
better material properties can be regarded as optimizing a
black-box function describing the relation between a chemical
formula and its properties. Various black-box optimization
algorithms have been developed in the machine learning and
statistics communities. Recently, a number of researchers
have reported successful applications of such algorithms to
chemistry. They include the design of photofunctional
molecules and medical drugs, optimization of thermal emission
materials and high Li-ion conductive solid electrolytes, and
discovery of a new phase in inorganic thin films for solar
cells.There are a wide variety of algorithms available for
black-box optimization, such as Bayesian optimization,
reinforcement learning, and active learning. Practitioners
need to select an appropriate algorithm or, in some cases,
develop novel algorithms to meet their demands. It is also
necessary to determine how to best combine machine learning
techniques with quantum mechanics- and molecular
mechanics-based simulations, and experiments. In this
Account, we give an overview of recent studies regarding
automated discovery, design, and optimization based on
black-box optimization. The Account covers the following
algorithms: Bayesian optimization to optimize the chemical or
physical properties, an optimization method using a quantum
annealer, best-arm identification, gray-box optimization, and
reinforcement learning. In addition, we introduce active
learning and boundless objective-free exploration, which may
not fall into the category of black-box optimization.Data
quality and quantity are key for the success of these
automated discovery techniques. As laboratory automation and
robotics are put forward, automated discovery algorithms
would be able to match human performance at least in some
domains in the near future.}
}
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preference information into evolutionary multi-objective
optimization and propose a preference-based evolutionary
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interactive algorithm. One algorithm is proposed in the
paper. At each iteration, the decision maker is asked to give
preference information in terms of his or her reference point
consisting of desirable aspiration levels for objective
functions. The information is used in an evolutionary
algorithm to generate a new population by combining the
fitness function and an achievement scalarizing function. In
multi-objective optimization, achievement scalarizing
functions are widely used to project a given reference point
into the Pareto optimal set. In our approach, the next
population is thus more concentrated in the area where more
preferred alternatives are assumed to lie and the whole
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accuracy. The approach is demonstrated by numerical
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number = 4,
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title = {Decomposition-based interactive evolutionary algorithm for
multiple objective optimization},
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volume = 24,
number = 2,
pages = {320--334},
year = 2019,
publisher = {IEEE},
doi = {10.1109/TEVC.2019.2915767},
abstract = {We propose a decomposition-based interactive evolutionary
algorithm (EA) for multiple objective optimization. During an
evolutionary search, a decision maker (DM) is asked to
compare pairwise solutions from the current population. Using
the Monte Carlo simulation, the proposed algorithm generates
from a uniform distribution a set of instances of the
preference model compatible with such an indirect preference
information. These instances are incorporated as the search
directions with the aim of systematically converging a
population toward the DMs most preferred region of the Pareto
front. The experimental comparison proves that the proposed
decomposition-based method outperforms the state-of-the-art
interactive counterparts of the dominance-based EAs. We also
show that the quality of constructed solutions is highly
affected by the form of the incorporated preference model.},
keywords = {interactive multi-objective; decision-making}
}
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author = { Tomczyk, Micha{\l} K and Kadzi{\'n}ski, Mi{\l}osz },
title = {{EMOSOR}: Evolutionary multiple objective optimization guided
by interactive stochastic ordinal regression},
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pages = {134--154},
year = 2019,
doi = {10.1016/j.cor.2019.04.008},
keywords = {Multiple objective optimization, Interactive evolutionary
hybrids, Stochastic ordinal regression, Preference
disaggregation, Pairwise comparisons, Active learning},
abstract = {We propose a family of algorithms, called EMOSOR, combining
Evolutionary Multiple Objective Optimization with Stochastic
Ordinal Regression. The proposed methods ask the Decision
Maker (DM) to holistically compare, at regular intervals, a
pair of solutions, and use the Monte Carlo simulation to
construct a set of preference model instances compatible with
such indirect and incomplete information. The specific
variants of EMOSOR are distinguished by the following three
aspects. Firstly, they make use of two different preference
models, i.e., either an additive value function or a
Chebyshev function. Secondly, they aggregate the
acceptability indices derived from the stochastic analysis in
various ways, and use thus constructed indicators or
relations to sort the solutions obtained in each
generation. Thirdly, they incorporate different active
learning strategies for selecting pairs of solutions to be
critically judged by the DM. The extensive computational
experiments performed on a set of benchmark optimization
problems reveal that EMOSOR is able to bias an evolutionary
search towards a part of the Pareto front being the most
relevant to the DM, outperforming in this regard the
state-of-the-art interactive evolutionary hybrids. Moreover,
we demonstrate that the performance of EMOSOR improves in
case the forms of a preference model used by the method and
the DM's value system align. Furthermore, we discuss how
vastly incorporation of different indicators based on the
stochastic acceptability indices influences the quality of
both the best constructed solution and an entire
population. Finally, we demonstrate that our novel
questioning strategies allow to reduce a number of
interactions with the DM until a high-quality solution is
constructed or, alternatively, to discover a better solution
after the same number of interactions.}
}
@article{TomKad2021ciemod,
author = { Tomczyk, Micha{\l} K and Kadzi{\'n}ski, Mi{\l}osz },
title = {Decomposition-based co-evolutionary algorithm for interactive
multiple objective optimization},
journal = {Information Sciences},
volume = 549,
pages = {178--199},
year = 2021,
doi = {10.1016/j.ins.2020.11.030},
keywords = {Evolutionary multiple objective optimization, Co-evolution,
Decomposition, Indirect preference information, Preference
learning},
abstract = {We propose a novel co-evolutionary algorithm for interactive
multiple objective optimization, named CIEMO/D. It aims at
finding a region in the Pareto front that is highly relevant
to the Decision Maker (DM). For this reason, CIEMO/D asks the
DM, at regular intervals, to compare pairs of solutions from
the current population and uses such preference information
to bias the evolutionary search. Unlike the existing
interactive evolutionary algorithms dealing with just a
single population, CIEMO/D co-evolves a pool of
subpopulations in a steady-state decomposition-based
evolutionary framework. The evolution of each subpopulation
is driven by the use of a different preference model. In this
way, the algorithm explores various regions in the objective
space, thus increasing the chances of finding DM's most
preferred solution. To improve the pace of the evolutionary
search, CIEMO/D allows for the migration of solutions between
different subpopulations. It also dynamically alters the
subpopulations' size based on compatibility between the
incorporated preference models and the decision examples
supplied by the DM. The extensive experimental evaluation
reveals that CIEMO/D can successfully adjust to different
DM's decision policies. We also compare CIEMO/D with selected
state-of-the-art interactive evolutionary hybrids that make
use of the DM's pairwise comparisons, demonstrating its high
competitiveness.}
}
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volume = 29,
number = 5,
pages = {519--529},
year = 1983,
publisher = {{INFORMS}}
}
@article{ZitThi99:spea,
author = { Eckart Zitzler and Lothar Thiele },
title = {Multiobjective Evolutionary Algorithms: {A} Comparative Case
Study and the Strength {Pareto} Approach},
journal = {IEEE Transactions on Evolutionary Computation},
volume = 3,
number = 4,
pages = {257--271},
year = 1999,
doi = {10.1109/4235.797969},
annote = {Proposed SPEA,
\url{http://www.tik.ee.ethz.ch/sop/publicationListFiles/zt1999a.pdf}}
}
@article{ZitThiBad2010tec,
author = { Eckart Zitzler and Lothar Thiele and Johannes Bader },
title = {On Set-Based Multiobjective Optimization},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2010,
volume = 14,
number = 1,
pages = {58--79},
doi = {10.1109/TEVC.2009.2016569},
keywords = {Performance assessment; Preference articulation; refinement;
Set Partitioning; Set-preference},
annote = {Proposed SPAM and explores combination of quality indicators}
}
@article{ZitThiDeb2000ec,
author = { Eckart Zitzler and Lothar Thiele and Kalyanmoy Deb },
title = {Comparison of Multiobjective Evolutionary
Algorithms: Empirical Results},
journal = {Evolutionary Computation},
year = 2000,
volume = 8,
number = 2,
pages = {173--195},
doi = {10.1162/106365600568202},
keywords = {ZDT benchmark}
}
@article{ZitThiLauFon2003:tec,
author = { Eckart Zitzler and Lothar Thiele and Marco Laumanns and Carlos M. Fonseca and Viviane {Grunert da Fonseca} },
title = {Performance Assessment of Multiobjective Optimizers: an
Analysis and Review},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2003,
volume = 7,
number = 2,
amonth = apr,
pages = {117--132},
doi = {10.1109/TEVC.2003.810758},
annote = {Proposed the combination of quality indicators; proposed epsilon-indicator}
}
@article{ZloBirMeuDor2004:aor,
author = {M. Zlochin and Mauro Birattari and N. Meuleau and Marco Dorigo },
journal = {Annals of Operations Research},
number = {1--4},
pages = {373--395},
title = {Model-Based Search for Combinatorial Optimization: A
Critical Survey},
volume = 131,
year = 2004
}
@article{mlrMBO,
title = {{mlrMBO}: A Modular Framework for Model-Based Optimization of
Expensive Black-Box Functions},
author = { Bernd Bischl and Jakob Richter and Jakob Bossek and Daniel Horn and Janek Thomas and Michel Lang},
year = 2017,
journal = {Arxiv preprint arXiv:1703.03373 [stat.ML]},
url = {http://arxiv.org/abs/1703.03373}
}
@article{msc:special-issue,
author = { Oscar Cord{\'o}n and Francisco Herrera and Thomas St{\"u}tzle },
title = {Special Issue on Ant Colony Optimization: Models and
Applications},
journal = {Mathware \& Soft Computing},
year = 2002,
volume = 9,
number = 3,
pages = {137--268}
}
@article{powel03:demand,
author = { G. McCormick and R. S. Powell },
title = {Optimal Pump Scheduling in Water Supply Systems with Maximum
Demand Charges},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
volume = 129,
number = 5,
pages = {372--379},
date = {2003-09/2003-10},
year = 2003,
month = sep # { / } # oct
}
@article{QuaGreLiuHu2007searching,
title = {Searching for multiobjective preventive maintenance
schedules: Combining preferences with evolutionary
algorithms},
journal = {European Journal of Operational Research},
volume = 177,
number = 3,
pages = {1969--1984},
year = 2007,
doi = {10.1016/j.ejor.2005.12.015},
author = {Gang Quan and Garrison W. Greenwood and Donglin Liu and
Sharon Hu},
keywords = {Evolutionary computations, Scheduling, Utility theory,
Preventive maintenance, Multi-objective optimization,
ranking-based, interactive},
abstract = {Heavy industry maintenance facilities at aircraft service
centers or railroad yards must contend with scheduling
preventive maintenance tasks to ensure critical equipment
remains available. The workforce that performs these tasks
are often high-paid, which means the task scheduling should
minimize worker idle time. Idle time can always be minimized
by reducing the workforce. However, all preventive
maintenance tasks should be completed as quickly as possible
to make equipment available. This means the completion time
should be also minimized. Unfortunately, a small workforce
cannot complete many maintenance tasks per hour. Hence, there
is a tradeoff: should the workforce be small to reduce idle
time or should it be large so more maintenance can be
performed each hour? A cost effective schedule should strike
some balance between a minimum schedule and a minimum size
workforce. This paper uses evolutionary algorithms to solve
this multiobjective problem. However, rather than conducting
a conventional dominance-based Pareto search, we introduce a
form of utility theory to find Pareto optimal solutions. The
advantage of this method is the user can target specific
subsets of the Pareto front by merely ranking a small set of
initial solutions. A large example problem is used to
demonstrate our method.}
}
@article{ranger2015,
author = {Marvin N. Wright and Andreas Ziegler},
title = {{\rpackage{ranger}}: A Fast Implementation of Random Forests for High
Dimensional Data in {\proglang{C++}} and {\proglang{R}}},
journal = {Arxiv preprint arXiv:1508.04409 [stat.ML]},
url = {https://arxiv.org/abs/1508.04409},
year = 2015
}
@article{ranger2017:jss,
author = {Marvin N. Wright and Andreas Ziegler},
title = {{\rpackage{ranger}}: A Fast Implementation of Random Forests for High
Dimensional Data in {\proglang{C++}} and {\proglang{R}}},
journal = {Journal of Statistical Software},
year = 2017,
volume = 77,
number = 1,
pages = {1--17},
doi = {10.18637/jss.v077.i01}
}
@article{scikit-learn2011,
title = {Scikit-learn: Machine learning in {\proglang{Python}}},
author = {Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel,
V. and Thirion, B. and Grisel, O. and Blondel, M. and
Prettenhofer, P. and Weiss, R. and Dubourg, V. and
Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher,
M. and Perrot, M. and Duchesnay, E.},
journal = {Journal of Machine Learning Research},
volume = 12,
pages = {2825--2830},
year = 2011
}
@article{vanZyl04,
author = { Jakobus E. van Zyl and Dragan A. Savic and Godfrey A. Walters },
title = {Operational Optimization of Water Distribution
Systems using a Hybrid Genetic Algorithm},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
year = 2004,
volume = 130,
number = 2,
pages = {160--170},
month = mar
}
@misc{AAAI2021checklist,
author = {{AAAI}},
title = {35th AAAI Conference on Artificial Intelligence:
Reproducibility Checklist},
howpublished = {\url{https://aaai.org/Conferences/AAAI-21/reproducibility-checklist/}},
year = 2021,
note = {Last accessed: June 6th, 2021}
}
@misc{ACM2020badging_v1_1,
author = {{ACM}},
title = {Artifact Review and Badging Version 1.1},
howpublished = {\url{https://www.acm.org/publications/policies/artifact-review-and-badging-current}},
year = 2020,
month = aug
}
@incollection{AarKorMic2005,
year = 2005,
address = {Boston, MA},
publisher = {Springer},
doi = {10.1007/0-387-28356-0},
editor = { Edmund K. Burke and Graham Kendall },
booktitle = {Search Methodologies},
title = {Simulated Annealing},
author = { Emile H. L. Aarts and Jan H. M. Korst and Wil Michiels },
pages = {187--210}
}
@inproceedings{Abb2002selfpde,
year = 2002,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2002 Congress on Evolutionary Computation (CEC'02)},
key = {IEEE CEC},
title = {The self-adaptive {Pareto} differential evolution algorithm},
author = { Abbass, Hussein A. },
pages = {831--836}
}
@inproceedings{LimPoz2017automopso,
year = 2017,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2017 Congress on Evolutionary Computation (CEC 2017)},
key = {IEEE CEC},
author = {de Lima, Ricardo Henrique Remes and Pozo, Aurora Trinidad
Ramirez},
title = {A study on auto-configuration of Multi-Objective Particle
Swarm Optimization Algorithm},
pages = {718--725},
doi = {10.1109/CEC.2017.7969381}
}
@inproceedings{AbbSarNew2001pde,
address = {Piscataway, NJ},
publisher = {IEEE Press},
year = 2001,
booktitle = {Proceedings of the 2001 Congress on Evolutionary Computation (CEC'01)},
key = {IEEE CEC},
title = {{PDE}: a {Pareto}-frontier differential evolution approach
for multi-objective optimization problems},
author = { Abbass, Hussein A. and Sarker, Ruhul and Newton, Charles},
pages = {971--978}
}
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year = 1997,
booktitle = {Proceedings of MIC 1997, the 2nd Metaheuristics International
Conference},
editor = { Mauricio G. C. Resende and Pinho de Souza, Jorge},
title = {A hybrid heuristic for multiobjective knapsack problems},
author = {Ben Abdelaziz, F. and Krichen, S. and Chaouachi, J.},
pages = {205--212},
doi = {10.1007/978-1-4615-5775-3_14}
}
@incollection{Aca2004memaco,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 3172,
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and L. M. Gambardella and Francesco Mondada and Thomas St{\"u}tzle and Mauro Birattari and Christian Blum },
year = 2004,
booktitle = {Ant Colony Optimization and Swarm Intelligence, 4th
International Workshop, ANTS 2004 },
author = {Acan, A.},
title = {An external memory implementation in ant colony optimization},
pages = {73--84},
keywords = {memory-based ACO}
}
@incollection{Aca2005evocop,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
year = 2005,
editor = { G{\"u}nther R. Raidl and Gottlieb, Jens},
volume = 3448,
booktitle = {Proceedings of EvoCOP 2005 -- 5th European Conference on Evolutionary Computation in Combinatorial Optimization },
author = {Acan, A.},
title = {An external partial permutations memory for ant colony
optimization},
pages = {1--11},
keywords = {memory-based ACO}
}
@incollection{AguZapLieVer2016many,
address = { Cham, Switzerland},
publisher = {Springer},
year = 2016,
volume = 9554,
fulleditor = {St\'ephane Bonnevay and Pierrick Legrand and Nicolas Monmarch{\'e} and Evelyne Lutton and Marc Schoenauer },
editor = {St\'ephane Bonnevay and others},
series = {Lecture Notes in Computer Science},
booktitle = {Artificial Evolution: 12th International Conference, Evolution Artificielle, EA, 2015},
title = {Approaches for Many-Objective Optimization: Analysis and
Comparison on {MNK}-Landscapes},
author = { Aguirre, Hern\'{a}n E. and Zapotecas, Sa{\'{u}}l and Arnaud Liefooghe and Verel, S{\'e}bastien and Tanaka, Kiyoshi },
pages = {14--28},
doi = {10.1007/978-3-319-31471-6_2}
}
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author = { A. Aho and J. Hopcroft and J. Ullman },
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year = 1983,
publisher = {Addison-Wesley},
address = { Reading, MA}
}
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publisher = {ACM Press},
address = { New York, NY},
editor = {Andrea Pohoreckyj Danyluk and L{\'{e}}on Bottou and Michael
L. Littman},
booktitle = {Proceedings of the 26th International Conference on Machine Learning, {ICML} 2009},
year = 2009,
author = {Cheng, Weiwei and H\"{u}hn, Jens and Eyke H{\"u}llermeier },
title = {Decision Tree and Instance-Based Learning for Label Ranking},
doi = {10.1145/1553374.1553395},
pages = {161--168},
numpages = 8
}
@incollection{AguTan2009:space,
address = { Heidelberg, Germany},
publisher = {Springer},
year = 2009,
series = {Lecture Notes in Computer Science},
volume = 5467,
editor = { Matthias Ehrgott and Carlos M. Fonseca and Xavier Gandibleux and Jin-Kao Hao and Marc Sevaux },
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2009},
title = {Many-Objective Optimization by Space Partitioning and
Adaptive $\epsilon$-Ranking on {MNK}-Landscapes},
author = { Aguirre, Hern\'{a}n E. and Tanaka, Kiyoshi },
pages = {407--422}
}
@incollection{Aguirre2013,
year = 2013,
address = { New York, NY},
publisher = {ACM Press},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2013},
editor = { Christian Blum and Alba, Enrique },
author = { Aguirre, Hern\'{a}n E. },
title = {Advances on Many-objective Evolutionary Optimization},
pages = {641--666},
keywords = {many-objective evolutionary optimization}
}
@book{AhujMagOrl1993netflows,
author = { R. K. Ahuja and T. Magnanti and J. B. Orlin },
title = {Network Flows: Theory, Algorithms and Applications},
publisher = {Prentice-Hall},
year = 1993
}
@incollection{AikBurLi2006,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 4193,
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {IX}},
publisher = {Springer},
year = 2006,
editor = {Runarsson, Thomas Philip and Hans-Georg Beyer and Edmund K. Burke and Juan-Juli{\'a}n Merelo and Darrell Whitley and Xin Yao },
author = {Uwe Aickelin and Edmund K. Burke and Jingpeng Li},
title = {Improved Squeaky Wheel Optimisation for Driver Scheduling},
pages = {182--191}
}
@incollection{AisRoy2010:isorms,
year = 2010,
volume = 142,
publisher = {Springer, US},
editor = { Matthias Ehrgott and Jos{\'e} Rui Figueira and Salvatore Greco },
series = {International Series in Operations Research \& Management Science},
booktitle = {Trends in Multiple Criteria Decision Analysis},
author = { Hassene Aissi and Bernard Roy },
title = {Robustness in Multi-criteria Decision Aiding},
chapter = 4,
pages = {87--121}
}
@incollection{AkiSanYan2019optuna,
key = {SIGKDD},
month = jul,
address = { New York, NY},
publisher = {ACM Press},
year = 2019,
editor = {Teredesai and others},
booktitle = {25th {ACM} {SIGKDD} International Conference on Knowledge
Discovery and Data Mining},
doi = {10.1145/3292500.3330701},
author = {Takuya Akiba and Shotaro Sano and Toshihiko Yanase and Takeru
Ohta and Masanori Koyama},
title = {Optuna: A Next-generation Hyperparameter Optimization Framework},
pages = {2623--2631}
}
@techreport{AktAtaGur2007conic,
author = {S. M. Akt{\"u}rk and Alper Atamt{\"u}rk and S. G{\"u}rel},
title = {A Strong Conic Quadratic Reformulation for Machine-Job
Assignment with Controllable Processing Times},
institution = {University of California-Berkeley},
year = 2007,
type = {Research Report},
number = {BCOL.07.01}
}
@incollection{AlaSolGhe07,
author = {I. Alaya and Christine Solnon and Khaled Gh{\'e}dira},
title = {Ant Colony Optimization for Multi-Objective
Optimization Problems},
booktitle = {19th IEEE International Conference on Tools with
Artificial Intelligence (ICTAI 2007)},
year = 2007,
volume = 1,
publisher = {IEEE Computer Society Press},
address = {Los Alamitos, CA},
pages = {450--457}
}
@inproceedings{AlaSolGhe2004:bioma,
url = {https://books.google.be/books?id=0ZLsAAAACAAJ},
editor = {Bogdan Filipi{\v c} and Jurij {\v S}ilc },
year = 2004,
booktitle = {International Conference on Bioinspired Optimization Methods
and their Applications (BIOMA 2004)},
author = {I. Alaya and Christine Solnon and Khaled Gh{\'e}dira},
title = {Ant algorithm for the multi-dimensional knapsack
problem},
pages = {63--72}
}
@incollection{AlbChi2007gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2007,
editor = {Dirk Thierens and others},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007},
author = { Alba, Enrique and Chicano, Francisco },
title = {{ACOhg}: dealing with huge graphs},
pages = {10--17},
doi = {10.1145/1276958.1276961}
}
@incollection{AliSimHar2019,
doi = {10.1145/3321707},
isbn = {978-1-4503-6111-8},
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
author = {Alissa, Mohamad and Sim, Kevin and Emma Hart },
title = {Algorithm Selection Using Deep Learning without Feature Extraction},
pages = {198--206}
}
@incollection{AllBurHyd2009reusable,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2009},
address = { New York, NY},
year = 2009,
publisher = {ACM Press},
editor = { Franz Rothlauf },
author = {Allen, Sam and Edmund K. Burke and Matthew R. Hyde and Graham Kendall },
title = {Evolving reusable 3d packing heuristics with genetic
programming},
pages = {931--938},
doi = {10.1145/1569901.1570029},
keywords = {hyper-heuristic}
}
@incollection{AllKno2010variables,
volume = 6238,
year = 2010,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
booktitle = {Parallel Problem Solving from Nature, PPSN XI},
author = { Allmendinger, Richard and Joshua D. Knowles },
title = {Evolutionary Optimization on Problems Subject to Changes of
Variables},
editor = {Schaefer, Robert and Carlos Cotta and Ko{\l}odziej, Joanna and G{\"u}nther Rudolph },
pages = {151--160},
abstract = {Motivated by an experimental problem involving the
identification of effective drug combinations drawn from a
non-static drug library, this paper examines evolutionary
algorithm strategies for dealing with changes of
variables. We consider four standard techniques from dynamic
optimization, and propose one new technique. The results show
that only little additional diversity needs to be introduced
into the population when changing a small number of
variables, while changing many variables or optimizing a
rugged landscape requires often a restart of the optimization
process}
}
@inproceedings{AllKno2011ecta,
author = { Allmendinger, Richard and Joshua D. Knowles },
title = {Evolutionary Search in Lethal Environments},
booktitle = {International Conference on Evolutionary Computation Theory
and Applications},
year = 2011,
pages = {63--72},
publisher = {SciTePress},
doi = {10.5220/0003673000630072},
epub = {https://www.scitepress.org/papers/2011/36730/36730.pdf}
}
@incollection{AllKno2011policy,
address = { New York, NY},
publisher = {ACM Press},
year = 2011,
editor = {Natalio Krasnogor and Pier Luca Lanzi},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011},
author = { Allmendinger, Richard and Joshua D. Knowles },
title = {Policy Learning in Resource-Constrained Optimization},
pages = {1971--1979},
doi = {10.1145/2001576.2001841},
abstract = {We consider an optimization scenario in which resources are
required in the evaluation process of candidate
solutions. The challenge we are focussing on is that certain
resources have to be committed to for some period of time
whenever they are used by an optimizer. This has the effect
that certain solutions may be temporarily non-evaluable
during the optimization. Previous analysis revealed that
evolutionary algorithms (EAs) can be effective against this
resourcing issue when augmented with static strategies for
dealing with non-evaluable solutions, such as repairing,
waiting, or penalty methods. Moreover, it is possible to
select a suitable strategy for resource-constrained problems
offline if the resourcing issue is known in advance. In this
paper we demonstrate that an EA that uses a reinforcement
learning (RL) agent, here Sarsa({$\lambda$}), to learn
offline when to switch between static strategies, can be more
effective than any of the static strategies themselves. We
also show that learning the same task as the RL agent but
online using an adaptive strategy selection method, here
D-MAB, is not as effective; nevertheless, online learning is
an alternative to static strategies.},
isbn = {978-1-4503-0557-0},
langid = {english}
}
@inproceedings{AllMouLiu2019human,
year = 2019,
publisher = {{AAAI} Press},
booktitle = {Proceedings of the Thirty-Second International Florida Artificial
Intelligence Research Society Conference},
editor = {Roman Bart{\'{a}}k and Keith W. Brawner},
author = {Joseph Allen and Ahmed Moussa and Xudong Liu},
title = {Human-in-the-Loop Learning of Qualitative Preference Models},
pages = {108--111},
doi = {10.48550/arXiv.1909.09064}
}
@phdthesis{Allmendinger2012phd,
author = { Allmendinger, Richard },
title = {Tuning Evolutionary Search for Closed-Loop Optimization},
school = {The University of Manchester, UK},
year = 2012,
month = jan
}
@inproceedings{AlsTsa2009,
address = {Hamburg, Germany},
publisher = {University of Hamburg},
editor = {M. Caserta and Stefan Vo{\ss} },
year = 2010,
booktitle = {Proceedings of MIC 2009, the 8th Metaheuristics International Conference},
title = {Guided {Pareto} local search and its application to
the 0/1 multi-objective knapsack problems},
author = {Alsheddy, A. and Tsang, E.}
}
@inproceedings{AmaAliThr2019nips,
year = 2019,
editor = {Hanna M. Wallach and Hugo Larochelle and Alina Beygelzimer
and Florence d'Alch{\'{e}}{-}Buc and Emily B. Fox and Roman
Garnett},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS 32)},
title = {Linear Stochastic Bandits Under Safety Constraints},
author = {Amani, Sanae and Alizadeh, Mahnoosh and Thrampoulidis,
Christos},
pages = {9256--9266},
epub = {http://papers.nips.cc/paper/9124-linear-stochastic-bandits-under-safety-constraints.pdf}
}
@incollection{AndVidIve1993,
publisher = {Springer},
year = 1993,
editor = { Vidal, Ren{\'e} Victor Valqui },
booktitle = {Applied Simulated Annealing},
title = {Design of a Teleprocessing Communication Network Using Simulated Annealing},
author = { Klaus Andersen and Vidal, Ren{\'e} Victor Valqui and Villy B{\ae}k Iversen },
pages = {201--215}
}
@incollection{Andersen99,
author = { J. H. Andersen and R. S. Powell },
title = {The Use of Continuous Decision Variables in an
Optimising Fixed Speed Pump Scheduling Algorithm},
booktitle = {Computing and Control for the Water Industry},
pages = {119--128},
publisher = { Research Studies Press Ltd. },
year = 1999,
editor = { R. S. Powell and K. S. Hindi }
}
@incollection{AngBocPaoVec08,
year = 2008,
volume = 5361,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
editor = {X. Li and others},
fulleditor = {X. Li and M. Kirley and M. Zhang and D. G. Green and
V. Ciesielski and Abbass, Hussein A. and Z. Michalewicz and
T. Hendtlass and Kalyanmoy Deb and Tan, Kay Chen and J{\"u}rgen Branke and Y. Shi},
booktitle = {Simulated Evolution and Learning, 7th International
Conference, SEAL 2008},
title = {Performance Evaluation of an Adaptive Ant Colony
Optimization Applied to Single Machine Scheduling},
author = {D. Anghinolfi and A. Boccalatte and M. Paolucci and
C. Vecchiola},
pages = {411--420}
}
@incollection{Angus2007,
editor = {Marcus Randall and Abbass, Hussein A. and Janet Wiles},
volume = 4828,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
year = 2007,
booktitle = {Progress in Artificial Life (ACAL)},
author = { Daniel Angus },
title = {Population-Based Ant Colony Optimisation for
Multi-objective Function Optimisation},
pages = {232--244},
doi = {10.1007/978-3-540-76931-6_21}
}
@inproceedings{AnsKamVeeRag2014open,
year = 2014,
address = { New York, NY},
publisher = {ACM Press},
booktitle = {Proceedings of the 23rd International Conference on Parallel
Architectures and Compilation},
key = {PACT},
author = {J. Ansel and S. Kamil and K. Veeramachaneni and J. Ragan-Kelley and J. Bosboom and Una-May O'Reilly and S. Amarasinghe},
title = {{OpenTuner}: An extensible framework for program autotuning},
pages = {303--315},
doi = {10.1145/2628071.2628092}
}
@inproceedings{AnsMalSamSelTie2015:ijcai,
publisher = {IJCAI/AAAI Press, Menlo Park, CA},
editor = {Qiang Yang and Michael Wooldridge},
year = 2015,
booktitle = {Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI-15)},
author = { Carlos Ans{\'o}tegui and Yuri Malitsky and Horst Samulowitz and Meinolf Sellmann and Kevin Tierney },
title = {Model-Based Genetic Algorithms for Algorithm Configuration},
pages = {733--739},
keywords = {GGA++},
epub = {https://www.ijcai.org/Abstract/15/109}
}
@inproceedings{AnsMalSel2014isacpp,
year = 2014,
publisher = {{AAAI} Press},
booktitle = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
editor = {David Stracuzzi and others},
author = { Carlos Ans{\'o}tegui and Yuri Malitsky and Meinolf Sellmann },
title = {{MaxSAT} by Improved Instance-Specific Algorithm
Configuration},
pages = {2594--2600}
}
@incollection{AnsSelTie2009cp,
year = 2009,
volume = 5732,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
booktitle = {Principles and Practice of Constraint Programming,
CP 2009},
editor = { Ian P. Gent },
author = { Carlos Ans{\'o}tegui and Meinolf Sellmann and Kevin Tierney },
title = {A Gender-Based Genetic Algorithm for the Automatic
Configuration of Algorithms},
pages = {142--157},
doi = {10.1007/978-3-642-04244-7_14},
keywords = {GGA}
}
@techreport{AppBixChvCoo95:tr,
author = { David Applegate and Robert E. Bixby and Va{\v{s}}ek Chv{\'a}tal and William J. Cook },
title = {Finding Cuts in the {TSP}},
institution = {DIMACS Center, Rutgers University, Piscataway, NJ, USA},
year = 1995,
number = {95--05},
month = mar
}
@techreport{AppBixChvCoo99:tr,
author = { David Applegate and Robert E. Bixby and Va{\v{s}}ek Chv{\'a}tal and William J. Cook },
title = {Finding Tours in the {TSP}},
institution = {Forschungsinstitut f{\"u}r Diskrete Mathematik, University of Bonn, Germany},
year = 1999,
number = 99885
}
@book{AppEtAl06,
author = { David Applegate and Robert E. Bixby and Va{\v{s}}ek Chv{\'a}tal and William J. Cook },
title = {The Traveling Salesman Problem: A Computational Study},
publisher = {Princeton University Press, Princeton, NJ},
year = 2006
}
@inproceedings{AprGloKel2003,
volume = 1,
month = dec,
address = { New York, NY},
publisher = {ACM Press},
editor = {Stephen E. Chick and Paul J. Sanchez and David M. Ferrin and Douglas J. Morrice},
year = 2003,
booktitle = {Proceedings of the 35th Winter Simulation Conference: Driving Innovation},
author = { Jay April and Fred Glover and James P. Kelly and Manuel Laguna },
title = {Simulation-based optimization: Practical introduction to simulation optimization},
pages = {71--78},
doi = {10.1109/WSC.2003.1261410}
}
@book{AroBar2009,
title = {Computational complexity: a modern approach},
author = {Arora, Sanjeev and Barak, Boaz},
year = 2009,
publisher = {Cambridge University Press}
}
@incollection{ArzCebPer2019qap,
isbn = {978-1-4503-6748-6},
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
author = {Etor Arza and Josu Ceberio and Aritz P{\'{e}}rez and Irurozki, Ekhine },
title = {Approaching the quadratic assignment problem with kernels of
mallows models under the hamming distance},
doi = {10.1145/3319619.3321976},
keywords = {QAP, EDA, Mallows}
}
@incollection{AsaIwaMiy96,
series = {{DIMACS} Series on Discrete Mathematics and Theoretical Computer Science},
volume = 26,
year = 1996,
address = { Providence, RI},
publisher = {American Mathematical Society},
booktitle = {Cliques, Coloring, and Satisfiability: Second {DIMACS}
Implementation Challenge},
editor = {David S. Johnson and Michael A. Trick },
author = {Y. Asahiro and K. Iwama and E. Miyano},
title = {Random Generation of Test Instances with Controlled
Attributes},
pages = {377--393}
}
@phdthesis{Asch95PhD,
author = { N. Ascheuer },
title = {Hamiltonian Path Problems in the On-line
Optimization of Flexible Manufacturing Systems},
school = {Technische Universit{\"a}t Berlin},
year = 1995,
address = {Berlin, Germany}
}
@incollection{Atkinson00,
author = { R. Atkinson and Jakobus E. van Zyl and Godfrey A. Walters and Dragan A. Savic },
title = {Genetic algorithm optimisation of level-controlled
pumping station operation},
booktitle = {Water network modelling for optimal design and
management},
pages = {79--90},
publisher = {Centre for Water Systems, Exeter, UK},
year = 2000
}
@incollection{AudDanOrb10,
editor = {K. Naono and K. Teranishi and J. Cavazos and R. Suda},
year = 2010,
publisher = {Springer},
booktitle = {Software Automatic Tuning: From Concepts to State-of-the-Art Results},
author = { Charles Audet and Cong-Kien Dang and Dominique Orban },
title = {Algorithmic Parameter Optimization of the {DFO} Method with
the {OPAL} Framework},
pages = {255--274}
}
@incollection{AugBadBroZit2009gecco,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2009},
address = { New York, NY},
year = 2009,
publisher = {ACM Press},
editor = { Franz Rothlauf },
author = { Anne Auger and Johannes Bader and Dimo Brockhoff and Eckart Zitzler },
title = {Articulating User Preferences in Many-Objective
Problems by Sampling the Weighted Hypervolume},
pages = {555--562}
}
@incollection{AugBadBroZit2009gecco2,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2009},
address = { New York, NY},
year = 2009,
publisher = {ACM Press},
editor = { Franz Rothlauf },
author = { Anne Auger and Johannes Bader and Dimo Brockhoff and Eckart Zitzler },
title = {Investigating and Exploiting the Bias of the
Weighted Hypervolume to Articulate User Preferences},
pages = {563--570}
}
@incollection{AugBadBroZit2009hv,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2009},
address = { New York, NY},
year = 2009,
publisher = {ACM Press},
editor = { Franz Rothlauf },
title = {Theory of the hypervolume indicator: optimal
$\mu$-distributions and the choice of the reference point},
author = { Anne Auger and Johannes Bader and Dimo Brockhoff and Eckart Zitzler },
pages = {87--102}
}
@incollection{AugBroLop2012dagstuhl,
doi = {10.4230/DagRep.2.1.50},
series = {Dagstuhl Reports},
volume = {2(1)},
year = 2012,
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik, Germany},
booktitle = {Learning in Multiobjective Optimization (Dagstuhl Seminar
12041)},
editor = { Salvatore Greco and Joshua D. Knowles and Kaisa Miettinen and Eckart Zitzler },
author = { Anne Auger and Dimo Brockhoff and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Kaisa Miettinen and Boris Naujoks and G{\"u}nther Rudolph },
title = {Which questions should be asked to find the most appropriate
method for decision making and problem solving? ({Working}
{Group} ``{Algorithm} {Design} {Methods}'')},
pages = {92--93}
}
@book{AugDoe2011,
editor = { Anne Auger and Benjamin Doerr },
title = {Theory of Randomized Search Heuristics: Foundations and Recent Developments},
series = {Series on Theoretical Computer Science},
volume = 1,
publisher = {World Scientific Publishing Co., Singapore},
year = 2011
}
@inproceedings{AugHan2005cec,
address = {Piscataway, NJ},
publisher = {IEEE Press},
month = sep,
year = 2005,
booktitle = {Proceedings of the 2005 Congress on Evolutionary Computation (CEC 2005)},
key = {IEEE CEC},
author = { Anne Auger and Nikolaus Hansen },
title = {A restart {CMA} evolution strategy with increasing population
size},
pages = {1769--1776},
doi = {10.1109/CEC.2005.1554902},
keywords = {IPOP-CMA-ES}
}
@inproceedings{AugHan2005lrcmaes,
address = {Piscataway, NJ},
publisher = {IEEE Press},
month = sep,
year = 2005,
booktitle = {Proceedings of the 2005 Congress on Evolutionary Computation (CEC 2005)},
key = {IEEE CEC},
author = { Anne Auger and Nikolaus Hansen },
title = {Performance evaluation of an advanced local search
evolutionary algorithm},
pages = {1777--1784},
keywords = {LR-CMAES}
}
@incollection{AvrAllLop2021evo,
volume = {12694},
series = {Lecture Notes in Computer Science},
address = { Cham, Switzerland},
publisher = {Springer},
booktitle = {Applications of Evolutionary Computation},
year = 2021,
editor = {Pedro Castillo and Jim{\'e}nez Laredo, Juan Luis },
author = { Andreea Avramescu and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {A Multi-Objective Multi-Type Facility Location Problem for
the Delivery of Personalised Medicine},
pages = {388--403},
doi = {10.1007/978-3-030-72699-7_25},
abstract = {Advances in personalised medicine targeting specific
sub-populations and individuals pose a challenge to the
traditional pharmaceutical industry. With a higher level of
personalisation, an already critical supply chain is facing
additional demands added by the very sensitive nature of its
products. Nevertheless, studies concerned with the efficient
development and delivery of these products are scarce. Thus,
this paper presents the case of personalised medicine and the
challenges imposed by its mass delivery. We propose a
multi-objective mathematical model for the
location-allocation problem with two interdependent facility
types in the case of personalised medicine products. We show
its practical application through a cell and gene therapy
case study. A multi-objective genetic algorithm with a novel
population initialisation procedure is used as solution
method.},
supplement = {https://doi.org/10.5281/zenodo.4495162},
keywords = {Personalised medicine, Biopharmaceuticals Supply chain,
Facility location-allocation, Evolutionary multi-objective
optimisation}
}
@incollection{AydYavOzyYasStu2017,
address = { New York, NY},
publisher = {ACM Press},
year = 2017,
editor = { Peter A. N. Bosman },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2017},
author = { Do\v{g}an Ayd{\i}n and G{\"{u}}rcan Yavuz and Serdar \"Ozy\"on and Celal Yasar and Thomas St{\"u}tzle },
title = {Artificial Bee Colony Framework to Non-convex Economic
Dispatch Problem with Valve Point Effects: A Case Study},
pages = {1311--1318}
}
@incollection{AyoAllLop2023gecco,
location = {Lisbon, Portugal},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2023},
annote = {ISBN: 979-8-4007-0120-7},
address = { New York, NY},
year = 2023,
publisher = {ACM Press},
editor = {Silva, Sara and Lu{\'i}s Paquete },
author = { Ayodele, Mayowa and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Parizy, Matthieu and Arnaud Liefooghe },
title = {Applying {Ising} Machines to Multi-Objective {QUBOs}},
pages = {2166--2174},
doi = {10.1145/3583133.3596312},
abstract = {Multi-objective optimisation problems involve finding
solutions with varying trade-offs between multiple and often
conflicting objectives. Ising machines are physical devices
that aim to find the absolute or approximate ground states of
an Ising model. To apply Ising machines to multi-objective
problems, a weighted sum objective function is used to
convert multi-objective into single-objective
problems. However, deriving scalarisation weights that
archives evenly distributed solutions across the Pareto front
is not trivial. Previous work has shown that adaptive weights
based on dichotomic search, and one based on averages of
previously explored weights can explore the Pareto front
quicker than uniformly generated weights. However, these
adaptive methods have only been applied to bi-objective
problems in the past. In this work, we extend the adaptive
method based on averages in two ways: (i) we extend the
adaptive method of deriving scalarisation weights for
problems with two or more objectives, and (ii) we use an
alternative measure of distance to improve performance. We
compare the proposed method with existing ones and show that
it leads to the best performance on multi-objective
Unconstrained Binary Quadratic Programming (mUBQP) instances
with 3 and 4 objectives and that it is competitive with the
best one for instances with 2 objectives.},
numpages = 9,
keywords = {digital annealer, multi-objective, bi-objective QAP, QUBO}
}
@incollection{AyoAllLop2022gecco,
location = {Boston, Massachusetts},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2022},
address = { New York, NY},
year = 2022,
publisher = {ACM Press},
editor = { Jonathan E. Fieldsend and Markus Wagner },
author = { Ayodele, Mayowa and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Parizy, Matthieu },
title = {Multi-Objective {QUBO} Solver: Bi-Objective Quadratic
Assignment Problem},
pages = {467--475},
doi = {10.1145/3512290.3528698},
abstract = {Quantum and quantum-inspired optimisation algorithms are
designed to solve problems represented in binary, quadratic
and unconstrained form. Combinatorial optimisation problems
are therefore often formulated as Quadratic Unconstrained
Binary Optimisation Problems (QUBO) to solve them with these
algorithms. Moreover, these QUBO solvers are often
implemented using specialised hardware to achieve enormous
speedups, e.g. Fujitsu's Digital Annealer (DA) and D-Wave's
Quantum Annealer. However, these are single-objective
solvers, while many real-world problems feature multiple
conflicting objectives. Thus, a common practice when using
these QUBO solvers is to scalarise such multi-objective
problems into a sequence of single-objective problems. Due to
design trade-offs of these solvers, formulating each
scalarisation may require more time than finding a local
optimum. We present the first attempt to extend the algorithm
supporting a commercial QUBO solver as a multi-objective
solver that is not based on scalarisation. The proposed
multi-objective DA algorithm is validated on the bi-objective
Quadratic Assignment Problem. We observe that algorithm
performance significantly depends on the archiving strategy
adopted, and that combining DA with non-scalarisation methods
to optimise multiple objectives outperforms the current
scalarised version of the DA in terms of final solution
quality.},
numpages = 9,
keywords = {digital annealer, multi-objective, bi-objective QAP, QUBO}
}
@incollection{AyoAllLop2022or,
booktitle = {Operations Research Proceedings 2022, OR 2022},
address = { Cham, Switzerland},
series = {Lecture Notes in Operations Research},
year = 2022,
publisher = {Springer},
editor = {Oliver Grothe and Stefan Nickel and Steffen Rebennack and
Oliver Stein},
author = { Ayodele, Mayowa and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Parizy, Matthieu },
title = {A Study of Scalarisation Techniques for Multi-objective
{QUBO} Solving},
pages = {393--399},
doi = {10.1007/978-3-031-24907-5_47}
}
@incollection{Ayodele2022penalty,
address = { Cham, Switzerland},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
year = 2022,
booktitle = {Proceedings of EvoCOP 2022 -- 22nd European Conference on Evolutionary Computation in Combinatorial Optimization },
editor = { P{\'e}rez C{\'a}ceres, Leslie and Verel, S{\'e}bastien },
title = {Penalty Weights in {QUBO} Formulations: Permutation Problems},
author = { Ayodele, Mayowa },
pages = {159--174}
}
@incollection{AziDoeDre2021,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2021},
address = { New York, NY},
year = 2021,
publisher = {ACM Press},
editor = { Chicano, Francisco and Krzysztof Krawiec },
author = {Aziz-Alaoui, Amine and Carola Doerr and Johann Dreo },
title = {Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks},
pages = {1365--1374},
doi = {10.1145/3449726.3463155}
}
@misc{BBCOMP2017,
title = {Black Box Optimization Competition},
author = {Ilya Loshchilov and T. Glasmachers },
year = 2017,
url = {https://bbcomp.ini.rub.de/}
}
@misc{BBOB2016bi,
author = { Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Dejan Tusar and Tea Tu{\v s}ar and Tobias Wagner },
title = {{GECCO} Workshop on Real-Parameter Black-Box Optimization
Benchmarking ({BBOB} 2016): Focus on multi-objective
problems},
howpublished = {\url{https://numbbo.github.io/workshops/BBOB-2016/}},
year = 2016
}
@incollection{ZitLauBleu2004tutorial,
year = 2004,
address = {Berlin\slash Heidelberg},
publisher = {Springer},
volume = 535,
series = {Lecture Notes in Economics and Mathematical Systems},
editor = { Xavier Gandibleux and Marc Sevaux and Kenneth S{\"o}rensen and V. {T'Kindt} },
booktitle = {Metaheuristics for Multiobjective Optimisation},
title = {A tutorial on evolutionary multiobjective optimization},
author = { Eckart Zitzler and Marco Laumanns and S. Bleuler },
pages = {3--37}
}
@incollection{BLTZ2003a,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 2632,
series = {Lecture Notes in Computer Science},
editor = { Carlos M. Fonseca and Peter J. Fleming and Eckart Zitzler and Kalyanmoy Deb and Lothar Thiele },
year = 2003,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2003},
author = { S. Bleuler and Marco Laumanns and Lothar Thiele and Eckart Zitzler },
title = {{PISA} -- A Platform and Programming Language
Independent Interface for Search Algorithms },
pages = {494--508}
}
@misc{Bab2008spear,
author = { Domagoj Babi{\'c} },
title = {Spear theorem prover},
howpublished = {\url{https://www.domagoj-babic.com/index.php/ResearchProjects/Spear}},
year = 2008
}
@inproceedings{BabHu2007cav,
author = { Domagoj Babi{\'c} and Alan J. Hu},
title = {Structural Abstraction of Software Verification
Conditions},
booktitle = {Computer Aided Verification: 19th International
Conference, CAV 2007},
year = 2007,
pages = {366--378},
annote = {Spear-swv instances,
\url{http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/benchmark_instances/SpearSWV/SWV-scrambled-first302.tar.gz},
\url{http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/benchmark_instances/SpearSWV/SWV-scrambled-last302.tar.gz}}
}
@inproceedings{BabHut2008spear,
author = { Domagoj Babi{\'c} and Frank Hutter },
title = {Spear Theorem Prover},
booktitle = {SAT'08: Proceedings of the SAT 2008 Race},
year = 2008,
annote = {Unreviewed paper},
epub = {https://www.domagoj-babic.com/index.php/Pubs/SAT08},
supplement = {https://www.domagoj-babic.com/index.php/ResearchProjects/Spear}
}
@book{BacFogMic1997,
title = {Handbook of evolutionary computation},
author = { Thomas B{\"a}ck and David B. Fogel and Zbigniew Michalewicz },
year = 1997,
publisher = {IOP Publishing}
}
@techreport{BacSteWot1994tr,
author = {Achim Bachem and Barthel Steckemetz and Michael
Wottawa},
title = {An efficient parallel cluster-heuristic for large
Traveling Salesman Problems},
year = 1994,
institution = {University of Koln, Germany},
number = {94-150},
keywords = {Genetic Edge Recombination (ERX)}
}
@book{Back1996evolutionary,
author = { Thomas B{\"a}ck },
title = {Evolutionary algorithms in theory and practice: evolution
strategies, evolutionary programming, genetic algorithms},
year = 1996,
publisher = {Oxford University Press}
}
@incollection{BalBirStu06,
address = { Heidelberg, Germany},
publisher = {Springer},
year = 2006,
volume = 4150,
series = {Lecture Notes in Computer Science},
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and L. M. Gambardella and Mauro Birattari and
Martinoli, A. and Poli, R. and Thomas St{\"u}tzle },
booktitle = {Ant Colony Optimization and Swarm Intelligence, 5th
International Workshop, ANTS 2006},
author = { Prasanna Balaprakash and Mauro Birattari and Thomas St{\"u}tzle and Marco Dorigo },
title = {Incremental local search in ant colony optimization:
Why it fails for the quadratic assignment problem},
pages = {156--166}
}
@incollection{BalBirStu07,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 4771,
editor = { Thomas Bartz-Beielstein and Mar{\'i}a J. Blesa and Christian Blum and Boris Naujoks and Andrea Roli and G{\"u}nther Rudolph and M. Sampels },
year = 2007,
booktitle = {Hybrid Metaheuristics},
author = { Prasanna Balaprakash and Mauro Birattari and Thomas St{\"u}tzle },
title = {Improvement Strategies for the {F}-Race Algorithm:
Sampling Design and Iterative Refinement},
pages = {108--122},
keywords = {Iterated Race},
doi = {10.1007/978-3-540-75514-2_9}
}
@incollection{BalHo1980,
author = { Egon Balas and Andrew Ho},
title = {Set Covering Algorithms Using Cutting Planes, Heuristics, and
Subgradient Optimization: A Computational Study},
booktitle = {Combinatorial optimization},
series = {Mathematical Programming Studies},
year = 1980,
volume = 12,
publisher = {Springer},
address = {Berlin\slash Heidelberg},
pages = {37--60},
editor = {Padberg, M. W.},
doi = {10.1007/BFb0120886}
}
@inproceedings{BapHgu1997,
author = {P. Baptiste and L. K. Hguny},
title = {A branch and bound algorithm for the F$/$no\_idle$/C_\text{max}$},
booktitle = {Proceedings of the international conference on industrial engineering and production management, IEPM'97},
year = 1997,
address = {Lyon},
pages = {429--438}
}
@book{Bar2006newexp,
author = { Thomas Bartz-Beielstein },
title = {Experimental Research in Evolutionary Computation:
The New Experimentalism},
publisher = {Springer},
year = 2006,
address = { Berlin, Germany},
keywords = {SPO}
}
@incollection{Bar2015genera,
address = {Berlin\slash Heidelberg},
publisher = {Springer},
editor = {Kacprzyk, Janusz and Pedrycz, Witold},
booktitle = {Springer Handbook of Computational Intelligence},
year = 2015,
author = { Thomas Bartz-Beielstein },
title = {How to Create Generalizable Results},
pages = {1127--1142},
keywords = {Mixed-effects models, random-effects model, problem instance
generation}
}
@inproceedings{BarFlaKocKon2010spot,
title = {{SPOT}: A Toolbox for Interactive and Automatic Tuning in the
\proglang{R} Environment},
author = { Thomas Bartz-Beielstein and Flasch, Oliver and Koch, Patrick
and Konen, Wolfgang},
booktitle = {Proceedings 20. Workshop Computational Intelligence},
year = 2010,
address = {Karlsruhe},
publisher = {KIT Scientific Publishing},
pages = {264--273}
}
@inproceedings{BarLasPre2005cec,
address = {Piscataway, NJ},
publisher = {IEEE Press},
month = sep,
year = 2005,
booktitle = {Proceedings of the 2005 Congress on Evolutionary Computation (CEC 2005)},
key = {IEEE CEC},
author = { Thomas Bartz-Beielstein and C. Lasarczyk and Mike Preuss },
title = {Sequential Parameter Optimization},
pages = {773--780}
}
@incollection{BarLasPre2010emaoa,
editor = { Thomas Bartz-Beielstein and Marco Chiarandini and Lu{\'i}s Paquete and Mike Preuss },
year = 2010,
address = {Berlin\slash Heidelberg},
publisher = {Springer},
booktitle = {Experimental Methods for the Analysis of
Optimization Algorithms},
author = { Thomas Bartz-Beielstein and C. Lasarczyk and Mike Preuss },
title = {The Sequential Parameter Optimization Toolbox},
pages = {337--360},
keywords = {SPOT},
doi = {10.1007/978-3-642-02538-9_14}
}
@inproceedings{BarMar2004,
address = {Piscataway, NJ},
publisher = {IEEE Press},
month = sep,
year = 2004,
booktitle = {Proceedings of the 2004 Congress on Evolutionary
Computation (CEC 2004)},
key = {IEEE CEC},
title = {Tuning search algorithms for real-world applications: A
regression tree based approach},
author = { Thomas Bartz-Beielstein and Markon, Sandor},
pages = {1111--1118}
}
@inproceedings{BarPea2012aaai,
year = 2012,
publisher = {{AAAI} Press},
booktitle = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
editor = {Jorg Hoffmann and Bart Selman},
title = {Transportability of causal effects: Completeness results},
author = { Elias Bareinboim and Judea Pearl },
pages = {698,704}
}
@inproceedings{BarPre2005em,
address = {Reykjavik, Iceland},
editor = { Lu{\'i}s Paquete and Marco Chiarandini and Dario Basso},
year = {2006},
booktitle = {Empirical Methods for the Analysis of Algorithms, Workshop EMAA 2006, Proceedings},
author = { Thomas Bartz-Beielstein and Mike Preuss },
title = {Considerations of budget allocation for sequential parameter
optimization ({SPO})},
pages = {35--40}
}
@incollection{BarPre2014experimental,
address = {Berlin\slash Heidelberg},
publisher = {Springer},
year = 2014,
series = {Natural Computing Series},
booktitle = {Theory and Principled Methods for the Design of
Metaheuristics},
editor = {Borenstein, Yossi and A. Moraglio },
author = { Thomas Bartz-Beielstein and Mike Preuss },
title = {Experimental Analysis of Optimization Algorithms: Tuning and
Beyond},
doi = {10.1007/978-3-642-33206-7_10},
pages = {205--245}
}
@inproceedings{BarSch03,
author = { Benjam{\'i}n Bar{\'a}n and Matilde Schaerer },
title = {A multiobjective ant colony system for vehicle
routing problem with time windows},
booktitle = {Proceedings of the Twenty-first IASTED International
Conference on Applied Informatics},
pages = {97--102},
year = 2003,
address = {Insbruck, Austria}
}
@incollection{BasGoeLie2013gecco,
isbn = {978-1-4503-1963-8},
address = { New York, NY},
publisher = {ACM Press},
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title = {Archiver Effects on the Performance of State-of-the-art Multi- and Many-objective Evolutionary Algorithms: Supplementary material},
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year = 2019
}
@incollection{BezLopStu2020chapter,
address = { Cham, Switzerland},
publisher = {Springer International Publishing},
editor = { Thomas Bartz-Beielstein and Bogdan Filipi{\v c} and P. Koro{\v s}ec and Talbi, El-Ghazali },
year = 2020,
booktitle = {High-Performance Simulation-Based Optimization},
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Configuration of Multi-objective Optimizers and
Multi-objective Configuration},
pages = {69--92},
doi = {10.1007/978-3-030-18764-4_4},
abstract = {Heuristic optimizers are an important tool in academia and industry, and their performance-optimizing configuration requires a significant amount of expertise. As the proper configuration of algorithms is a crucial aspect in the engineering of heuristic algorithms, a significant research effort has been dedicated over the last years towards moving this step to the computer and, thus, make it automatic. These research efforts go way beyond tuning only numerical parameters of already fully defined algorithms, but exploit automatic configuration as a means for automatic algorithm design. In this chapter, we review two main aspects where the research on automatic configuration and multi-objective optimization intersect. The first is the automatic configuration of multi-objective optimizers, where we discuss means and specific approaches. In addition, we detail a case study that shows how these approaches can be used to design new, high-performing multi-objective evolutionary algorithms. The second aspect is the research on multi-objective configuration, that is, the possibility of using multiple performance metrics for the evaluation of algorithm configurations. We highlight some few examples in this direction.}
}
@phdthesis{Bezerra2016PhD,
author = { Leonardo C. T. Bezerra },
title = {A component-wise approach to multi-objective evolutionary
algorithms: from flexible frameworks to automatic design},
school = {IRIDIA, {\'E}cole polytechnique, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2016,
annote = {Supervised by Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }
}
@incollection{BiaGamDor02:ppsn,
anote = {IC.34},
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Juan-Juli{\'a}n Merelo and others},
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series = {Lecture Notes in Computer Science},
year = 2002,
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pages = {883--892}
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author = {Armin Biere},
pages = {39--40}
}
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publisher = {IOS Press},
editor = {Giuseppe De Giacomo and Alejandro Catala and Bistra Dilkina
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year = 2020,
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title = {Dynamic Algorithm Configuration: Foundation of a New
Meta-Algorithmic Framework},
epub = {https://ecai2020.eu/papers/1237_paper.pdf},
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title = {Efficient Parameter Importance Analysis via Ablation with
Surrogates},
doi = {10.1609/aaai.v31i1.10657}
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editor = { Roberto Battiti and Mauro Brunato and Ilias Kotsireas and Panos M. Pardalos },
author = { Biedenkapp, Andr{\'e} and Marben, Joshua and Marius Thomas Lindauer and Frank Hutter },
title = {{CAVE}: Configuration assessment, visualization and
evaluation},
pages = {115--130},
doi = {10.1007/978-3-030-05348-2_10}
}
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pages = {189--203}
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title = {Learning Graphical Models for Algorithm Configuration}
}
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address = { Heidelberg, Germany},
publisher = {Springer},
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series = {Lecture Notes in Computer Science},
year = 2002,
booktitle = {Ant Algorithms, Third International Workshop, ANTS
2002},
author = { Mauro Birattari and Gianni A. {Di Caro} and Marco Dorigo },
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pages = {188--201}
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Computation Conference, GECCO 2002},
author = { Mauro Birattari and Thomas St{\"u}tzle and Lu{\'i}s Paquete and Klaus Varrentrapp },
title = {A Racing Algorithm for Configuring Metaheuristics},
pages = {11--18},
keywords = {F-race},
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year = 2010,
address = {Berlin\slash Heidelberg},
publisher = {Springer},
booktitle = {Experimental Methods for the Analysis of
Optimization Algorithms},
author = { Mauro Birattari and Zhi Yuan and Prasanna Balaprakash and Thomas St{\"u}tzle },
title = {{F}-Race and Iterated {F}-Race: An Overview},
pages = {311--336},
keywords = {F-race, iterated F-race, irace, tuning},
doi = {10.1007/978-3-642-02538-9_13}
}
@inproceedings{BirYuaBalStu2010:mic,
address = {Hamburg, Germany},
publisher = {University of Hamburg},
editor = {M. Caserta and Stefan Vo{\ss} },
year = 2010,
booktitle = {Proceedings of MIC 2009, the 8th Metaheuristics International Conference},
author = { Mauro Birattari and Zhi Yuan and Prasanna Balaprakash and Thomas St{\"u}tzle },
title = {Parameter Adaptation in Ant Colony Optimization}
}
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Perspective},
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author = { Mauro Birattari },
year = 2009,
volume = 197,
series = {Studies in Computational Intelligence},
publisher = {Springer},
address = {Berlin\slash Heidelberg},
annote = {Based on the PhD thesis~\cite{Birattari2004PhD}}
}
@phdthesis{Birattari2004PhD,
author = { Mauro Birattari },
title = {The Problem of Tuning Metaheuristics as Seen from a
Machine Learning Perspective},
school = {IRIDIA, {\'E}cole polytechnique, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2004,
annote = {Supervised by Marco Dorigo}
}
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Engineering Optimisation},
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booktitle = {Astrodynamics Tools and Techniques (ICATT 2010), 4th
International Conference on},
year = 2010,
url = {http://arxiv.org/abs/1004.3824},
keywords = {PaGMO}
}
@incollection{BisMerTraPre12:gecco,
address = { New York, NY},
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year = 2012,
editor = {Terence Soule and Jason H. Moore},
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author = { Bernd Bischl and Olaf Mersmann and Heike Trautmann and Mike Preuss },
title = {Algorithm Selection Based on Exploratory Landscape Analysis and Cost-sensitive Learning},
pages = {313--320},
keywords = {continuous optimization, landscape analysis, algorithm selection}
}
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title = {Pattern recognition and machine learning},
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year = 2019,
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key = {ACC2019},
author = {Erdem B{\i }y{\i }k and Jonathan Margoliash and Shahrouz Ryan Alimo
and Dorsa Sadigh},
title = {Efficient and Safe Exploration in Deterministic {Markov}
Decision Processes with Unknown Transition Models},
pages = {1792--1799},
doi = {10.23919/ACC.2019.8815276}
}
@incollection{BleBlu04:disjoint,
aeditor = { G{\"u}nther R. Raidl and S. Cagnoni and J{\"u}rgen Branke and D. W. Corne and
R. Drechsler and Y. Jin and C. G. Johnson and Penousal Machado and E. Marchiori and R. Rothlauf and G. D. Smith and
G. Squillero},
booktitle = {Applications of Evolutionary Computing, Proceedings of EvoWorkshops 2004},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 3005,
year = 2004,
publisher = {Springer},
editor = { G{\"u}nther R. Raidl and others},
author = { Mar{\'i}a J. Blesa and Christian Blum },
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Edge-Disjoint Paths Problem},
pages = {160--169}
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year = 2006,
editor = {Jurafsky, Dan and Gaussier, Eric},
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title = {Domain adaptation with structural correspondence learning},
author = {Blitzer, John and McDonald, Ryan and Pereira, Fernando},
pages = {120--128}
}
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publisher = {Springer},
year = 2016,
editor = {Paola Festa and Meinolf Sellmann and Joaquin Vanschoren },
author = { Aymeric Blot and Holger H. Hoos and Laetitia Jourdan and Marie-El{\'e}onore Kessaci-Marmion and Heike Trautmann },
title = {{MO-ParamILS}: A Multi-objective Automatic Algorithm
Configuration Framework},
pages = {32--47},
doi = {10.1007/978-3-319-50349-3_3}
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@incollection{BloJouKess2017gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2017,
editor = { Peter A. N. Bosman },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017},
author = { Aymeric Blot and Laetitia Jourdan and Marie-El{\'e}onore Kessaci-Marmion },
title = {Automatic design of multi-objective local search algorithms:
case study on a bi-objective permutation flowshop scheduling
problem},
pages = {227--234},
doi = {10.1145/3071178.3071323}
}
@incollection{BloLopKesJou2018ppsn,
volume = 11101,
year = 2018,
address = { Cham, Switzerland},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Anne Auger and Carlos M. Fonseca and Louren{\c c}o, N. and Penousal Machado and Lu{\'i}s Paquete and Darrell Whitley },
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XV}},
author = { Aymeric Blot and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Marie-El{\'e}onore Kessaci-Marmion and Laetitia Jourdan },
title = {New Initialisation Techniques for Multi-Objective Local
Search: Application to the Bi-objective Permutation Flowshop},
doi = {10.1007/978-3-319-99253-2_26},
pages = {323--334}
}
@incollection{BloPerJouKesHoo2017emo,
editor = {Heike Trautmann and G{\"{u}}nter Rudolph and Kathrin Klamroth
and Oliver Sch{\"{u}}tze and Margaret M. Wiecek and Yaochu
Jin and Christian Grimme},
year = 2017,
volume = 10173,
series = {Lecture Notes in Computer Science},
address = { Cham, Switzerland},
publisher = {Springer International Publishing},
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title = {Automatically Configuring Multi-objective Local Search Using
Multi-objective Optimisation},
pages = {61--76}
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publisher = {Springer},
year = 2006,
volume = 4150,
series = {Lecture Notes in Computer Science},
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and L. M. Gambardella and Mauro Birattari and
Martinoli, A. and Poli, R. and Thomas St{\"u}tzle },
booktitle = {Ant Colony Optimization and Swarm Intelligence, 5th
International Workshop, ANTS 2006},
author = { Christian Blum and J. Bautista and J. Pereira },
title = {{Beam-ACO} applied to assembly line balancing},
pages = {96--107},
doi = {10.1007/11839088_9}
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author = { Christian Blum and Mar{\'i}a J. Blesa and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Beam Search for the Longest Common Subsequence
Problem},
institution = {Department LSI, Universitat Polit{\`e}cnica de Catalunya},
year = 2008,
number = {LSI-08-29},
note = {Published in Computers \& Operations Research~\cite{BluBleLop09-BeamSearch-LCS}}
}
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booktitle = {Proceedings of EvoCOP 2007 -- Seventh European Conference on
Evolutionary Computation in Combinatorial Optimisation},
author = { Christian Blum and Carlos Cotta and Antonio J. Fern{\'a}ndez and J. E. Gallardo },
title = {A probabilistic beam search algorithm for the
shortest common supersequence problem},
pages = {36--47}
}
@incollection{BluLop2011ieh,
author = { Christian Blum and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
booktitle = {The Industrial Electronics Handbook: Intelligent Systems},
title = {Ant Colony Optimization},
publisher = {CRC Press},
year = 2011,
edition = {2nd},
isbn = 9781439802830,
url = {http://www.crcpress.com/product/isbn/9781439802830},
annnote = {http://www.eng.auburn.edu/~wilambm/ieh/}
}
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address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 4771,
editor = { Thomas Bartz-Beielstein and Mar{\'i}a J. Blesa and Christian Blum and Boris Naujoks and Andrea Roli and G{\"u}nther Rudolph and M. Sampels },
year = 2007,
booktitle = {Hybrid Metaheuristics},
author = { Christian Blum and M. Mastrolilli},
title = {Using Branch {\&} Bound Concepts in
Construction-Based Metaheuristics: {Exploiting} the
Dual Problem Knowledge},
pages = {123--139}
}
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title = {Swarm Intelligence--Introduction and Applications},
year = 2008,
editor = { Christian Blum and D. Merkle },
series = {Natural Computing Series},
publisher = {Springer Verlag, Berlin, Germany}
}
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author = { Christian Blum and G{\"u}nther R. Raidl },
title = {Hybrid Metaheuristics---Powerful Tools for Optimization},
publisher = {Springer},
year = 2016,
series = {Artificial Intelligence: Foundations, Theory, and Algorithms},
address = { Berlin, Germany}
}
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volume = 114,
year = 2008,
address = { Berlin, Germany},
publisher = {Springer},
editor = { Christian Blum and Mar{\'i}a J. Blesa and Andrea Roli and M. Sampels },
booktitle = {Hybrid Metaheuristics: An emergent approach for optimization},
title = {Hybrid metaheuristics: an introduction},
author = { Christian Blum and Andrea Roli },
pages = {1--30}
}
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address = { Heidelberg, Germany},
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volume = 4030,
editor = {F. Almeida and others},
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and M. P{\'e}rez and A. Roli and M. Sampels },
year = 2006,
booktitle = {Hybrid Metaheuristics},
author = { Christian Blum and M. {Y{\'a}bar Vall{\`e}s} },
title = {Multi-level ant colony optimization for {DNA}
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pages = {94--109},
doi = {10.1007/11890584}
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title = {Artificial Decision Maker Driven by {PSO}: An Approach for
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(Dagstuhl Seminar 15031)},
editor = { Salvatore Greco and Kathrin Klamroth and Joshua D. Knowles and G{\"u}nther Rudolph },
author = { J{\"u}rgen Branke and Salvatore Corrente and Salvatore Greco and Kadzi{\'n}ski, Mi{\l}osz and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Vincent Mousseau and Mauro Munerato and Roman S{\l}owi{\'n}ski },
title = {Behavior-Realistic Artificial Decision-Makers to Test
Preference-Based Multi-objective Optimization Method
({Working} {Group} ``{Machine} {Decision}-{Making}'')},
pages = {110--116}
}
@incollection{BraDeb2055integrating,
author = { J{\"u}rgen Branke and Kalyanmoy Deb },
title = {Integrating User Preferences into Evolutionary
Multi-Objective Optimization},
booktitle = {Knowledge Incorporation in Evolutionary Computation},
publisher = {Springer},
year = 2005,
editor = { Yaochu Jin },
pages = {461--477},
address = {Berlin\slash Heidelberg},
abstract = {Many real-world optimization problems involve multiple,
typically conflicting objectives. Often, it is very difficult
to weigh the different criteria exactly before alternatives
are known. Evolutionary multi-objective optimization usually
solves this predicament by searching for the whole
Pareto-optimal front of solutions. However, often the user
has at least a vague idea about what kind of solutions might
be preferred. In this chapter, we argue that such knowledge
should be used to focus the search on the most interesting
(from a user's perspective) areas of the Paretooptimal
front. To this end, we present and compare two methods which
allow to integrate vague user preferences into evolutionary
multi-objective algorithms. As we show, such methods may
speed up the search and yield a more fine-grained selection
of alternatives in the most relevant parts of the
Pareto-optimal front.},
doi = {10.1007/978-3-540-44511-1_21}
}
@incollection{BraFerLuq2016lncs,
address = { Cham, Switzerland},
series = {Lecture Notes in Computer Science},
publisher = {Springer},
year = 2016,
booktitle = {Smart Cities (Smart-CT 2016)},
editor = { Alba, Enrique and Chicano, Francisco and Gabriel J. Luque },
author = {Bravo, Yesnier and Javier Ferrer and Gabriel J. Luque and Alba, Enrique },
title = {Smart Mobility by Optimizing the Traffic Lights: A New Tool
for Traffic Control Centers},
pages = {147--156},
abstract = {Urban traffic planning is a fertile area of Smart Cities to
improve efficiency, environmental care, and safety, since the
traffic jams and congestion are one of the biggest sources of
pollution and noise. Traffic lights play an important role in
solving these problems since they control the flow of the
vehicular network at the city. However, the increasing number
of vehicles makes necessary to go from a local control at one
single intersection to a holistic approach considering a
large urban area, only possible using advanced computational
resources and techniques. Here we propose HITUL, a system
that supports the decisions of the traffic control managers
in a large urban area. HITUL takes the real traffic
conditions and compute optimal traffic lights plans using
bio-inspired techniques and micro-simulations. We compare our
system against plans provided by experts. Our solutions not
only enable continuous traffic flows but reduce the
pollution. A case study of M{\'a}laga city allows us to
validate the approach and show its benefits for other cities
as well.},
doi = {10.1007/978-3-319-39595-1_15},
keywords = {Multi-objective optimization, Smart mobility, Traffic lights
planning}
}
@book{BraMar2002:book,
author = { Jean-Pierre Brans and Bertrand Mareschal },
title = {{PROMETHEE-GAIA}. Une m{\'e}thode d'aide {\`a} la d{\'e}cision en pr{\'e}sence de crit{\`e}res multiples},
year = 2002,
isbn = {2-7298-1253-9},
publisher = {Editions Ellipses},
address = { Paris, France}
}
@incollection{BraMar2005:mcda,
editor = { Jos{\'e} Rui Figueira and Salvatore Greco and Matthias Ehrgott },
year = 2005,
publisher = {Springer},
booktitle = {Multiple Criteria Decision Analysis, State of the
Art Surveys},
author = { Jean-Pierre Brans and Bertrand Mareschal },
title = {{PROMETHEE} Methods},
chapter = 5,
pages = {163--195}
}
@incollection{BraSchSch2001gecco,
publisher = {Morgan Kaufmann Publishers, San Francisco, CA},
editor = {Erik D. Goodman},
year = 2001,
booktitle = {Proceedings of the 3rd Annual Conference on Genetic and
Evolutionary Computation, GECCO 2001},
author = { J{\"u}rgen Branke and C. Schmidt and H. Schmeck},
title = {Efficient fitness estimation in noisy environments},
pages = {243--250}
}
@techreport{BranCorrGreSlow2014,
author = { J{\"u}rgen Branke and Salvatore Corrente and Salvatore Greco and Roman S{\l}owi{\'n}ski and Zielniewicz, P.},
title = {Using {Choquet} integral as preference model in interactive
evolutionary multiobjective optimization},
institution = {WBS, University of Warwick},
year = 2014
}
@incollection{BranElo2011gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2011,
editor = {Natalio Krasnogor and Pier Luca Lanzi},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011},
author = { J{\"u}rgen Branke and Jawad Elomari },
title = {Simultaneous tuning of metaheuristic parameters for
various computing budgets},
pages = {263--264},
doi = {10.1145/2001858.2002006},
keywords = {meta-optimization, offline parameter optimization}
}
@incollection{BranElo2013lion,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 7997,
booktitle = {Learning and Intelligent Optimization, 7th International Conference, LION 7},
publisher = {Springer},
year = 2013,
editor = { Panos M. Pardalos and G. Nicosia},
author = { J{\"u}rgen Branke and Jawad Elomari },
title = {Racing with a Fixed Budget and a Self-Adaptive
Significance Level}
}
@book{BreFriSto1984trees,
title = {Classification and regression trees},
author = {Breiman, Leo and Friedman, Jerome and Stone, Charles J. and
Olshen, Richard A.},
year = 1984,
publisher = {CRC Press}
}
@incollection{BreSch2011ea,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 7401,
booktitle = {Artificial Evolution: 10th International Conference, Evolution Artificielle, EA, 2011},
publisher = {Springer},
year = 2012,
editor = { Jin-Kao Hao and Legrand, Pierrick and Collet, Pierre and
Monmarch{\'e}, Nicolas and Lutton, Evelyne and Schoenauer,
Marc},
author = {M\'aty\'as Brendel and Marc Schoenauer },
title = {Learn-and-Optimize: A Parameter Tuning Framework for Evolutionary {AI} Planning},
pages = {145--155},
doi = {10.1007/978-3-642-35533-2_13}
}
@incollection{BreSch2011gecco,
year = 2011,
address = { New York, NY},
publisher = {ACM Press},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2011},
editor = {Natalio Krasnogor and Pier Luca Lanzi},
author = {M{\'a}ty{\'a}s Brendel and Marc Schoenauer },
title = {Instance-based Parameter Tuning for Evolutionary {AI} Planning},
pages = {591--598},
doi = {10.1145/2001858.2002053}
}
@incollection{BriFri2009emo,
address = { Heidelberg, Germany},
publisher = {Springer},
year = 2009,
series = {Lecture Notes in Computer Science},
volume = 5467,
editor = { Matthias Ehrgott and Carlos M. Fonseca and Xavier Gandibleux and Jin-Kao Hao and Marc Sevaux },
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2009},
author = { Karl Bringmann and Tobias Friedrich },
title = {Approximating the Least Hypervolume Contributor:
{NP}-Hard in General, But Fast in Practice},
pages = {6--20},
annote = {Extended version published in \cite{BriFri2012tcs}}
}
@incollection{BriFri2010gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2010,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2010},
editor = {Martin Pelikan and J{\"u}rgen Branke },
author = { Karl Bringmann and Tobias Friedrich },
title = {The Maximum Hypervolume Set Yields Near-optimal
Approximation},
pages = {511--518},
annote = {Proved that hypervolume approximates the additive
$\epsilon$-indicator, converging quickly as $N$ increases,
that is, sets that maximize hypervolume are near optimal on
additive $\epsilon$ too, with the gap diminishing as quickly
as O(1/N).}
}
@incollection{BriFri2010ppsn,
volume = 6238,
year = 2010,
address = { Heidelberg, Germany},
publisher = {Springer},
editor = {Schaefer, Robert and Cotta, Carlos and Kolodziej,
Joanna and G{\"u}nther Rudolph },
series = {Lecture Notes in Computer Science},
booktitle = {Parallel Problem Solving from Nature, PPSN XI},
title = {Tight bounds for the approximation ratio of the hypervolume
indicator},
author = { Karl Bringmann and Tobias Friedrich },
pages = {607--616}
}
@incollection{BriFri2011gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2011,
editor = {Natalio Krasnogor and Pier Luca Lanzi},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011},
title = {Convergence of Hypervolume-Based Archiving Algorithms~{I}:
Effectiveness},
author = { Karl Bringmann and Tobias Friedrich },
pages = {745--752},
doi = {10.1145/2001576.2001678},
annote = {Extended version published as \cite{BriFri2014convergence}}
}
@incollection{BriFri2012gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2012,
editor = {Terence Soule and Jason H. Moore},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2012},
title = {Convergence of Hypervolume-Based Archiving Algorithms~{II}:
Competitiveness},
author = { Karl Bringmann and Tobias Friedrich },
pages = {457--464},
doi = {10.1145/2330163.2330229},
annote = {Extended version published as \cite{BriFri2014convergence}}
}
@incollection{BriFriKli2014generic,
year = 2014,
volume = 8672,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Thomas Bartz-Beielstein and J{\"u}rgen Branke and Bogdan Filipi{\v c} and Jim Smith},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XIII}},
title = {Generic postprocessing via subset selection for hypervolume
and epsilon-indicator},
author = { Karl Bringmann and Tobias Friedrich and Patrick Klitzke},
pages = {518--527}
}
@incollection{BriFriKli2014subset,
address = { New York, NY},
publisher = {ACM Press},
year = 2014,
editor = {Christian Igel and Dirk V. Arnold},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2014},
title = {Two-dimensional subset selection for hypervolume and
epsilon-indicator},
author = { Karl Bringmann and Tobias Friedrich and Patrick Klitzke},
doi = {10.1145/2576768.2598276}
}
@inproceedings{BriPoz2012cec,
year = 2012,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2012 Congress on Evolutionary Computation (CEC 2012)},
key = {IEEE CEC},
title = {Using archiving methods to control convergence and diversity
for many-objective problems in particle swarm optimization},
author = {Britto, Andre and Pozo, Aurora},
pages = {1--8},
doi = {10.1109/CEC.2012.6256149}
}
@incollection{BrigFri2009foga,
isbn = {978-1-60558-414-0},
publisher = {{ACM}},
editor = {Ivan I. Garibay and Thomas Jansen and R. Paul Wiegand and
Annie S. Wu},
year = 2009,
booktitle = {Proceedings of the Tenth ACM SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA)},
author = { Karl Bringmann and Tobias Friedrich },
title = {Don't be greedy when calculating hypervolume contributions},
pages = {103--112},
annote = {Extended version published in \cite{BriFri2010eff}}
}
@inproceedings{BrinFriNeuWag2011,
publisher = {IJCAI/AAAI Press, Menlo Park, CA},
editor = {Toby Walsh},
year = 2011,
booktitle = {Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI-11)},
author = { Karl Bringmann and Tobias Friedrich and Frank Neumann and Markus Wagner },
title = {Approximation-guided Evolutionary Multi-objective
Optimization},
pages = {1198--1203}
}
@incollection{Bro2015emo,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2015 Part {I}},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 9018,
year = 2015,
publisher = {Springer},
editor = { Ant{\'o}nio Gaspar{-}Cunha and Carlos Henggeler Antunes and Carlos A. {Coello Coello} },
title = {A Bug in the Multiobjective Optimizer {IBEA}: Salutary
Lessons for Code Release and a Performance Re-Assessment},
author = { Dimo Brockhoff },
doi = {10.1007/978-3-319-15934-8_13},
pages = {187--201}
}
@incollection{BroCalLop2018dagstuhl,
keywords = {multiple criteria decision making, evolutionary
multiobjective optimization},
doi = {10.4230/DagRep.8.1.33},
volume = {8(1)},
year = 2018,
series = {Dagstuhl Reports},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik, Germany},
booktitle = {Personalized Multiobjective Optimization: An Analytics
Perspective (Dagstuhl Seminar 18031)},
editor = { Kathrin Klamroth and Joshua D. Knowles and G{\"u}nther Rudolph and Margaret M. Wiecek },
author = { Dimo Brockhoff and Roberto Calandra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Frank Neumann and Selvakumar Ulaganathan},
title = {Meta-modeling for (interactive) multi-objective optimization
(WG5)},
pages = {85--94}
}
@incollection{BroFriHebKle2007gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2007,
editor = {Dirk Thierens and others},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007},
author = { Dimo Brockhoff and Tobias Friedrich and N. Hebbinghaus and
C. Klein and Frank Neumann and Eckart Zitzler },
title = {Do Additional Objectives Make a Problem Harder?},
pages = {765--772},
doi = {10.1145/1276958.1277114}
}
@incollection{BroLopNau2012ppsn,
volume = 7491,
year = 2012,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
fulleditor = { Carlos A. {Coello Coello} and Vincenzo Cutello and Kalyanmoy Deb and Stephanie
Forrest and Giuseppe Nicosia and Mario Pavone},
editor = { Carlos A. {Coello Coello} and others},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XII}, Part {I}},
author = { Dimo Brockhoff and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Boris Naujoks and G{\"u}nther Rudolph },
title = {Runtime Analysis of Simple Interactive Evolutionary
Biobjective Optimization Algorithms},
pages = {123--132},
doi = {10.1007/978-3-642-32937-1_13},
abstract = {Development and deployment of interactive evolutionary
multiobjective optimization algorithms (EMOAs) have recently
gained broad interest. In this study, first steps towards a
theory of interactive EMOAs are made by deriving bounds on
the expected number of function evaluations and queries to a
decision maker. We analyze randomized local search and the
(1+1)-EA on the biobjective problems LOTZ and COCZ under the
scenario that the decision maker interacts with these
algorithms by providing a subjective preference whenever
solutions are incomparable. It is assumed that this decision
is based on the decision maker's internal utility
function. We show that the performance of the interactive
EMOAs may dramatically worsen if the utility function is
non-linear instead of linear.}
}
@incollection{BroSaxDeb2008handling,
address = {Berlin\slash Heidelberg},
publisher = {Springer},
series = {Natural Computing Series},
year = 2008,
booktitle = {Multiobjective Problem Solving from Nature},
author = { Dimo Brockhoff and Saxena, Dhish Kumar and Kalyanmoy Deb and Eckart Zitzler },
editor = { Joshua D. Knowles and David Corne and Kalyanmoy Deb and Chair, Deva Raj},
title = {On Handling a Large Number of Objectives A Posteriori and
During Optimization},
pages = {377--403},
abstract = {Dimensionality reduction methods are used routinely in
statistics, pattern recognition, data mining, and machine
learning to cope with high-dimensional spaces. Also in the
case of high-dimensional multiobjective optimization
problems, a reduction of the objective space can be
beneficial both for search and decision making. New questions
arise in this context, e.g., how to select a subset of
objectives while preserving most of the problem structure. In
this chapter, two different approaches to the task of
objective reduction are developed, one based on assessing
explicit conflicts, the other based on principal component
analysis (PCA). Although both methods use different
principles and preserve different properties of the
underlying optimization problems, they can be effectively
utilized either in an a posteriori scenario or during
search. Here, we demonstrate the usability of the
conflict-based approach in a decision-making scenario after
the search and show how the principal-component-based
approach can be integrated into an evolutionary
multicriterion optimization (EMO) procedure.},
doi = {10.1007/978-3-540-72964-8_18}
}
@incollection{BroTus2019bench,
doi = {10.1145/3319619},
isbn = {978-1-4503-6748-6},
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
title = {Benchmarking algorithms from the platypus framework on the
biobjective bbob-biobj testbed},
author = { Dimo Brockhoff and Tea Tu{\v s}ar },
pages = {1905--1911},
keywords = {unbounded archive}
}
@incollection{BroWagTrau2012r2,
address = { New York, NY},
publisher = {ACM Press},
year = 2012,
editor = {Terence Soule and Jason H. Moore},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2012},
title = {On the properties of the {R2} indicator},
author = { Dimo Brockhoff and Tobias Wagner and Heike Trautmann },
pages = {465--472},
annote = {Proof that R2 is weakly Pareto compliant.}
}
@incollection{BroZit2006allobjectives,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 4193,
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {IX}},
publisher = {Springer},
year = 2006,
editor = {Runarsson, Thomas Philip and Hans-Georg Beyer and Edmund K. Burke and Juan-Juli{\'a}n Merelo and Darrell Whitley and Xin Yao },
author = { Dimo Brockhoff and Eckart Zitzler },
title = {Are All Objectives Necessary? {On} Dimensionality Reduction in
Evolutionary Multiobjective Optimization},
pages = {533--542},
abstract = {Most of the available multiobjective evolutionary algorithms
(MOEA) for approximating the Pareto set have been designed
for and tested on low dimensional problems ($\leq$3
objectives). However, it is known that problems with a high
number of objectives cause additional difficulties in terms
of the quality of the Pareto set approximation and running
time. Furthermore, the decision making process becomes the
harder the more objectives are involved. In this context, the
question arises whether all objectives are necessary to
preserve the problem characteristics. One may also ask under
which conditions such an objective reduction is feasible, and
how a minimum set of objectives can be computed. In this
paper, we propose a general mathematical framework, suited to
answer these three questions, and corresponding algorithms,
exact and heuristic ones. The heuristic variants are geared
towards direct integration into the evolutionary search
process. Moreover, extensive experiments for four well-known
test problems show that substantial dimensionality reductions
are possible on the basis of the proposed methodology.}
}
@incollection{BroZit2006dimensionality,
author = { Dimo Brockhoff and Eckart Zitzler },
editor = {Waldmann, Karl-Heinz and Stocker, Ulrike M.},
title = {Dimensionality Reduction in Multiobjective Optimization: The
Minimum Objective Subset Problem},
booktitle = {Operations Research Proceedings 2006},
year = 2007,
publisher = {Springer},
address = {Berlin\slash Heidelberg},
pages = {423--429},
abstract = {The number of objectives in a multiobjective optimization
problem strongly influences both the performance of
generating methods and the decision making process in
general. On the one hand, with more objectives, more
incomparable solutions can arise, the number of which affects
the generating method's performance. On the other hand, the
more objectives are involved the more complex is the choice
of an appropriate solution for a (human) decision maker. In
this context, the question arises whether all objectives are
actually necessary and whether some of the objectives may be
omitted; this question in turn is closely linked to the
fundamental issue of conflicting and non-conflicting
optimization criteria. Besides a general definition of
conflicts between objective sets, we here introduce the
NP-hard problem of computing a minimum subset of objectives
without losing information (MOSS). Furthermore, we present
for MOSS both an approximation algorithm with optimum
approximation ratio and an exact algorithm which works well
for small input instances. We conclude with experimental
results for a random problem and the multiobjective
0/1-knapsack problem},
doi = {10.1007/978-3-540-69995-8_68},
keywords = {objective reduction}
}
@inproceedings{BroZit2007hypervolumeReduction,
address = {Piscataway, NJ},
publisher = {IEEE Press},
year = 2007,
booktitle = {Proceedings of the 2007 Congress on Evolutionary Computation (CEC 2007)},
key = {IEEE CEC},
author = { Dimo Brockhoff and Eckart Zitzler },
title = {Improving hypervolume-based multiobjective evolutionary
algorithms by using objective reduction methods},
pages = {2086--2093},
doi = {10.1109/CEC.2007.4424730},
keywords = {objective reduction}
}
@inproceedings{BruRit2018cec,
year = 2018,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2018 Congress on Evolutionary Computation (CEC 2018)},
key = {IEEE CEC},
author = { Artur Brum and Marcus Ritt},
title = {Automatic Design of Heuristics for Minimizing the Makespan in
Permutation Flow Shops},
pages = {1--8},
doi = {10.1109/CEC.2018.8477787}
}
@incollection{BruRit2018evo,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 10782,
series = {Lecture Notes in Computer Science},
year = 2018,
booktitle = {Proceedings of EvoCOP 2018 -- 18th European Conference on Evolutionary Computation in Combinatorial Optimization },
editor = { Arnaud Liefooghe and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
author = { Artur Brum and Marcus Ritt},
title = {Automatic Algorithm Configuration for the Permutation Flow
Shop Scheduling Problem Minimizing Total Completion Time},
pages = {85--100},
doi = {10.1007/978-3-319-77449-7_6}
}
@incollection{BuiRiz04:gecco,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 3102,
editor = { Kalyanmoy Deb and others},
year = 2004,
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2004, Part I},
author = {T. N. Bui and Rizzo, Jr, J. R. },
title = {Finding Maximum Cliques with Distributed Ants},
pages = {24--35}
}
@techreport{BurByk2012,
author = { Edmund K. Burke and Yuri Bykov },
title = {The Late Acceptance Hill-Climbing Heuristic},
institution = {University of Stirling},
number = {CSM-192},
year = 2012
}
@incollection{BurHydKen2007gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2007,
editor = {Dirk Thierens and others},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007},
author = { Edmund K. Burke and Matthew R. Hyde and Graham Kendall and John R. Woodward},
title = {Automatic Heuristic Generation with Genetic Programming: Evolving a Jack-of-all-trades or a Master of One},
pages = {1559--1565},
doi = {10.1145/1276958.1277273}
}
@incollection{BurHydKen2019hb,
publisher = {Springer},
series = {International Series in Operations Research \& Management
Science},
volume = 272,
booktitle = {Handbook of Metaheuristics},
year = 2019,
editor = { Michel Gendreau and Jean-Yves Potvin },
author = { Edmund K. Burke and Matthew R. Hyde and Graham Kendall and Gabriela Ochoa and Ender {\"O}zcan and John R. Woodward},
title = {A Classification of Hyper-Heuristic Approaches: Revisited},
chapter = 14,
pages = {453--477},
doi = {10.1007/978-3-319-91086-4_14}
}
@incollection{Burkard:QAP,
volume = 2,
editor = { Panos M. Pardalos and D.-Z. Du },
year = 1998,
publisher = {Kluwer Academic Publishers},
booktitle = {Handbook of Combinatorial Optimization},
author = { Burkard, Rainer E. and Eranda {\c C}ela and Panos M. Pardalos and L. S. Pitsoulis },
title = {The quadratic assignment problem},
pages = {241--338}
}
@incollection{Buz2019signif,
isbn = {978-1-4503-6748-6},
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
author = {Maxim Buzdalov},
title = {Towards better estimation of statistical significance when
comparing evolutionary algorithms},
pages = {1782--1788},
doi = {10.1145/3319619.3326899}
}
@techreport{CI-235-07,
author = { Nicola Beume and Carlos M. Fonseca and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Jan Vahrenhold },
title = {On the Complexity of Computing the Hypervolume
Indicator},
institution = {University of Dortmund},
year = 2007,
number = {CI-235/07},
month = dec,
note = {Published in IEEE Transactions on Evolutionary Computation~\cite{BeuFonLopPaqVah09:tec}}
}
@misc{COSEAL,
title = {COnfiguration and SElection of ALgorithms},
key = {COSEAL},
howpublished = {http://www.coseal.net},
year = 2017
}
@misc{CPLEX,
author = {IBM},
title = {{ILOG} {CPLEX} Optimizer},
year = 2017,
howpublished = {\url{http://www.ibm.com/software/integration/optimization/cplex-optimizer/}}
}
@incollection{CalShiCebDoe2019bayesian,
doi = {10.1145/3319619},
isbn = {978-1-4503-6748-6},
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
author = {Calvo, Borja and Shir, Ofer M. and Josu Ceberio and Carola Doerr and Wang, Hao and Thomas B{\"a}ck and Jos{\'e} A. Lozano },
title = {Bayesian Performance Analysis for Black-box Optimization
Benchmarking},
pages = {1789--1797},
numpages = 9,
acmid = 3326888,
keywords = {bayesian inference, benchmarking, black-box optimization,
evolutionary algorithms, performance measures, plackett-luce
model}
}
@incollection{CamDorStu2018ants,
volume = 11172,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Marco Dorigo and Mauro Birattari and Christensen, Anders L. and Reina, Andreagiovanni and Vito Trianni },
year = 2018,
booktitle = {Swarm Intelligence, 11th International Conference, ANTS 2018},
author = {Camacho-Villal\'{o}n, Christian Leonardo and Marco Dorigo and Thomas St{\"u}tzle },
title = {Why the Intelligent Water Drops Cannot Be Considered as a Novel Algorithm},
pages = {302--314}
}
@incollection{CamPas2010lion,
doi = {10.1007/978-3-642-13800-3},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 6073,
booktitle = {Learning and Intelligent Optimization, 4th International Conference, LION 4},
publisher = {Springer},
year = 2010,
editor = { Christian Blum and Roberto Battiti },
title = {Adapting to a realistic decision maker: experiments
towards a reactive multi-objective optimizer},
author = { Paolo Campigotto and Andrea Passerini },
pages = {338--341}
}
@incollection{CamStuDor2020ants,
volume = 12421,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Marco Dorigo and Thomas St{\"u}tzle and Mar{\'i}a J. Blesa and Christian Blum and Heiko Hamann and Heinrich, Mary Katherine},
year = 2020,
booktitle = {Swarm Intelligence, 12th International Conference, ANTS 2020},
title = {Grey Wolf, Firefly and Bat Algorithms: Three Widespread Algorithms that Do Not Contain Any Novelty},
author = {Camacho-Villal\'{o}n, Christian Leonardo and Thomas St{\"u}tzle and Marco Dorigo },
pages = {121--133}
}
@unpublished{CamTriLop2017pseudo,
author = {Felipe Campelo and \'Athila R. Trindade and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Pseudoreplication in Racing Methods for Tuning Metaheuristics},
note = {In preparation},
year = 2017
}
@book{Can00:book,
author = {E. Cant{\'u}-Paz},
title = {Efficient and Accurate Parallel Genetic Algorithms},
publisher = {Kluwer Academic Publishers, Boston, MA},
year = 2000
}
@inproceedings{CarJesMar2003,
author = {P. Cardoso and M. Jesus and A. Marquez},
title = {{MONACO}: multi-objective network optimisation based on an {ACO}},
booktitle = {Proc. X Encuentros de Geometr\'ia Computacional},
year = 2003,
address = {Seville, Spain}
}
@incollection{CarPinOli2017recipe,
address = { Heidelberg, Germany},
publisher = {Springer},
isbn = {978-3-319-55695-6},
year = 2017,
volume = 10196,
series = {Lecture Notes in Computer Science},
booktitle = {Proceedings of the 20th European Conference on Genetic Programming, EuroGP 2017},
editor = {James McDermott and Mauro Castelli and Luk{\'{a}}s Sekanina
and Evert Haasdijk and Pablo Garc{\'i}a-S{\'a}nchez },
author = {de S{\'{a}}, Alex Guimar{\~{a}}es Cardoso and Pinto, Walter
Jos{\'{e}} G. S. and Oliveira, Luiz Ot{\'{a}}vio Vilas Boas and Gisele Pappa },
title = {{RECIPE:} {A} Grammar-Based Framework for Automatically
Evolving Classification Pipelines},
pages = {246--261},
doi = {10.1007/978-3-319-55696-3_16}
}
@incollection{CarProSha2013votes,
address = { New York, NY},
publisher = {ACM Press},
year = 2013,
editor = {Michael J. Kearns and R. Preston McAfee and {\'{E}}va Tardos},
booktitle = {Proceedings of the Fourteenth ACM Conference on Electronic
Commerce},
title = {When Do Noisy Votes Reveal the Truth?},
author = {Ioannis Caragiannis and Ariel D. Procaccia and Nisarg Shah},
doi = {10.1145/2482540.2482570},
keywords = {computer social choice, mallows model, sample complexity},
pages = {143--160},
abstract = {A well-studied approach to the design of voting rules views
them as maximum likelihood estimators; given votes that are
seen as noisy estimates of a true ranking of the
alternatives, the rule must reconstruct the most likely true
ranking. We argue that this is too stringent a requirement,
and instead ask: How many votes does a voting rule need to
reconstruct the true ranking? We define the family of
pairwise-majority consistent rules, and show that for all
rules in this family the number of samples required from the
Mallows noise model is logarithmic in the number of
alternatives, and that no rule can do asymptotically better
(while some rules like plurality do much worse). Taking a
more normative point of view, we consider voting rules that
surely return the true ranking as the number of samples tends
to infinity (we call this property accuracy in the limit);
this allows us to move to a higher level of abstraction. We
study families of noise models that are parametrized by
distance functions, and find voting rules that are accurate
in the limit for all noise models in such general
families. We characterize the distance functions that induce
noise models for which pairwise-majority consistent rules are
accurate in the limit, and provide a similar result for
another novel family of position-dominance consistent
rules. These characterizations capture three well-known
distance functions.}
}
@incollection{CebMenLoz2015mallows,
address = { New York, NY},
publisher = {ACM Press},
year = 2015,
editor = {Sara Silva and Anna I. Esparcia{-}Alc{\'{a}}zar },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015},
title = {Kernels of {Mallows} Models for Solving Permutation-based
Problems},
author = { Josu Ceberio and Alexander Mendiburu and Jos{\'e} A. Lozano },
pages = {505--512}
}
@book{Cela:QAP,
author = { Eranda {\c C}ela },
title = {The Quadratic Assignment Problem: Theory and Algorithms},
year = 1998,
publisher = {Kluwer Academic Publishers},
address = { Dordrecht, The Netherlands}
}
@inproceedings{CesOddSmi2000:aaai,
publisher = {{AAAI} Press\slash {MIT} Press, Menlo Park, CA},
year = 2000,
booktitle = {Proceedings of AAAI 2000 -- Seventeenth National Conference
on Artificial Intelligence},
editor = {Henry A. Kautz and Bruce W. Porter},
author = { Amadeo Cesta and Angelo Oddi and Stephen F. Smith },
title = {Iterative Flattening: A Scalable Method for Solving Multi-Capacity Scheduling Problems},
pages = {742--747}
}
@mastersthesis{Chang99,
author = { S. T. H. Chang },
title = {Optimizing the Real Time Operation of a Pumping
Station at a Water Filtration Plant using Genetic
Algorithms},
school = {Department of Civil and Environmental Engineering,
The University of Adelaide},
year = 1999,
type = {Honors Thesis}
}
@inproceedings{Chase89,
author = { Donald V. Chase and Lindell E. Ormsbee },
title = {Optimal pump operation of water distribution systems
with multiple storage tanks},
booktitle = {Proceedings of American Water Works Association
Computer Specialty Conference},
pages = {205--214},
year = 1989,
address = {Denver, USA},
organization = {AWWA}
}
@inproceedings{Chase91,
author = { Donald V. Chase and Lindell E. Ormsbee },
title = {An alternate formulation of time as a decision
variable to facilitate real-time operation of water
supply systems},
booktitle = {Proceedings of the 18th Annual Conference of Water
Resources Planning and Management},
pages = {923--927},
year = 1991,
address = { New York, NY},
organization = {ASCE}
}
@incollection{CheBuzDoeDan2023aac,
publisher = {{ACM}},
editor = { Chicano, Francisco and Tobias Friedrich and K{\"o}tzing, Timo and Franz Rothlauf },
year = 2023,
booktitle = {Proceedings of the 17th {ACM}/{SIGEVO} Conference on Foundations of Genetic Algorithms},
author = {Chen, Deyao and Buzdalov, Maxim and Carola Doerr and Nguyen Dang},
title = {Using Automated Algorithm Configuration for Parameter
Control},
pages = {38--49},
doi = {10.1145/3594805.3607127}
}
@inproceedings{CheGaoChen2005scga,
title = {{SCGA}: Controlling genetic algorithms with {Sarsa}(0)},
author = {Chen, Fei and Gao, Yang and Chen, Zhao-qian and Chen, Shi-fu},
booktitle = {Computational Intelligence for Modelling, Control and
Automation, 2005 and International Conference on Intelligent
Agents, Web Technologies and Internet Commerce, International
Conference on},
volume = 1,
pages = {1177--1183},
year = 2005,
publisher = {IEEE},
doi = {10.1109/CIMCA.2005.1631422}
}
@incollection{CheGinBecMol2013moda,
address = { Heidelberg, Germany},
publisher = {Springer International Publishing},
booktitle = {mODa 10--Advances in Model-Oriented Design and Analysis},
year = 2013,
editor = {Ucinski, Dariusz and Atkinson, Anthony C. and Patan, Maciej},
author = {Chevalier, Cl{\'e}ment and Ginsbourger, David and Bect,
Julien and Molchanov, Ilya},
title = {Estimating and Quantifying Uncertainties on Level Sets Using
the {Vorob}'ev Expectation and Deviation with {Gaussian}
Process Models},
pages = {35--43},
abstract = {Several methods based on Kriging have recently been proposed
for calculating a probability of failure involving
costly-to-evaluate functions. A closely related problem is to
estimate the set of inputs leading to a response exceeding a
given threshold. Now, estimating such a level set---and not
solely its volume---and quantifying uncertainties on it are
not straightforward. Here we use notions from random set
theory to obtain an estimate of the level set, together with
a quantification of estimation uncertainty. We give explicit
formulae in the Gaussian process set-up and provide a
consistency result. We then illustrate how space-filling
versus adaptive design strategies may sequentially reduce
level set estimation uncertainty.},
doi = {10.1007/978-3-319-00218-7_5}
}
@inproceedings{CheIshSha2021clustering,
title = {Clustering-Based Subset Selection in Evolutionary
Multiobjective Optimization},
author = {Chen, Weiyu and Ishibuchi, Hisao and Shang, Ke},
booktitle = {2021 IEEE International Conference on Systems, Man, and
Cybernetics},
year = 2021,
organization = {IEEE},
pages = {468--475}
}
@incollection{CheKanTay1991ijcai,
publisher = {Morgan Kaufmann Publishers},
editor = {Mylopoulos, John and Reiter, Raymond},
year = 1995,
booktitle = {Proceedings of the 12th International Joint Conference on Artificial Intelligence (IJCAI-91)},
author = {Cheeseman, Peter C. and Kanefsky, Bob and Taylor, William M.},
title = {Where the Really Hard Problems Are},
pages = {331--340}
}
@inproceedings{CheXuChe04,
publisher = {IEEE Press},
year = 2004,
booktitle = {Proceedings of the International Conference on
Machine Learning and Cybernetics},
editor = {Cloete, Ian and Wong, Kit-Po and Berthold, Michael},
author = {L. Chen and X. H. Xu and Y. X. Chen},
title = {An adaptive ant colony clustering algorithm},
pages = {1387--1392}
}
@inproceedings{CheIshSha2020subset,
year = 2020,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2020 Congress on Evolutionary Computation (CEC 2020)},
key = {IEEE CEC},
title = {Modified Distance-based Subset Selection for Evolutionary
Multi-objective Optimization Algorithms},
author = {Chen, Weiyu and Ishibuchi, Hisao and Shang, Ke},
pages = {1--8},
keywords = {IGD+}
}
@inproceedings{CheXinChe2017vdmlibrary,
year = 2017,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2017 Congress on Evolutionary Computation (CEC 2017)},
key = {IEEE CEC},
author = {Chen, Lu and Xin, Bin and Chen, Jie and Juan Li},
title = {A virtual-decision-maker library considering personalities
and dynamically changing preference structures for
interactive multiobjective optimization},
pages = {636--641},
doi = {10.1109/CEC.2017.7969370},
keywords = {machine DM, interactive EMOA}
}
@incollection{ChiDerVer2023fourier,
doi = {10.1145/3583131},
location = {Lisbon, Portugal},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2023},
annote = {ISBN: 9798400701191},
address = { New York, NY},
year = 2023,
publisher = {ACM Press},
editor = {Silva, Sara and Lu{\'i}s Paquete },
author = { Chicano, Francisco and Bilel Derbel and Verel, S{\'e}bastien },
title = {Fourier Transform-based Surrogates for Permutation Problems},
pages = {275--283}
}
@incollection{ChiGoe2010,
editor = { Thomas Bartz-Beielstein and Marco Chiarandini and Lu{\'i}s Paquete and Mike Preuss },
year = 2010,
address = {Berlin\slash Heidelberg},
publisher = {Springer},
booktitle = {Experimental Methods for the Analysis of
Optimization Algorithms},
author = { Marco Chiarandini and Yuri Goegebeur},
title = {Mixed Models for the Analysis of Optimization Algorithms},
pages = {225--264},
annote = {Preliminary version available as \emph{Tech.\ Rep.}
MF-2009-07-001 at the The Danish Mathematical Society},
doi = {10.1007/978-3-642-02538-9}
}
@phdthesis{ChiarandiniPhD,
author = { Marco Chiarandini },
title = {Stochastic Local Search Methods for Highly
Constrained Combinatorial Optimisation Problems},
school = {FB Informatik, TU Darmstadt, Germany},
year = 2005
}
@misc{Chieng2014,
author = {Tsung-Che Chiang},
title = {nsga3cpp: A {C++} implementation of {NSGA-III}},
howpublished = {\url{http://web.ntnu.edu.tw/~tcchiang/publications/nsga3cpp/nsga3cpp.htm}},
year = 2014
}
@inproceedings{ChrSchBur2011patus,
publisher = {IEEE Computer Society},
year = 2011,
series = {IPDPS '11},
booktitle = {Proceedings of the 2011 IEEE International Parallel \&
Distributed Processing Symposium},
editor = {Frank Mueller},
author = {Matthias Christen and Olaf Schenk and Helmar Burkhart},
title = {{PATUS:} A Code Generation and Autotuning Framework for
Parallel Iterative Stencil Computations on Modern
Microarchitectures},
pages = {676--687},
doi = {10.1109/IPDPS.2011.70}
}
@techreport{ChrVan2018,
author = {Jan Christiaens and Greet Vanden Berghe},
title = {Slack Induction by String Removals for Vehicle Routing Problems},
institution = {Department of Computing Science, KU Leuven, Gent, Belgium},
year = 2018,
number = {7-05-2018}
}
@techreport{Christofides1976,
title = {Worst-case analysis of a new heuristic for the travelling salesman problem},
author = { Christofides, Nicos },
year = 1976,
number = 388,
institution = {Graduate School of Industrial Administration, Carnegie-Mellon University, Pittsburgh, PA}
}
@incollection{ChuLop2021gecco,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2021},
address = { New York, NY},
year = 2021,
publisher = {ACM Press},
editor = { Chicano, Francisco and Krzysztof Krawiec },
author = { Tinkle Chugh and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Maximising Hypervolume and Minimising $\epsilon$-Indicators
using Bayesian Optimisation over Sets},
doi = {10.1145/3449726.3463178},
keywords = {multi-objective, surrogate models, epsilon, hypervolume},
supplement = {https://doi.org/10.5281/zenodo.4675569},
pages = {1326--1334}
}
@incollection{ChuNuaJanPho06,
address = { New York, NY},
publisher = {ACM Press},
year = 2006,
editor = {M. Cattolico and others},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2006},
author = {S. Chusanapiputt and D. Nualhong and S. Jantarang
and S. Phoomvuthisarn},
title = {Selective self-adaptive approach to ant system for
solving unit commitment problem},
pages = {1729--1736}
}
@phdthesis{Chugh2017phd,
author = { Tinkle Chugh },
title = {Handling expensive multiobjective optimization problems with
evolutionary algorithms},
school = {University of Jyv{\"a}skyl{\"a}},
year = 2017
}
@inproceedings{Chugh2020scalar,
year = 2020,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2020 Congress on Evolutionary Computation (CEC 2020)},
key = {IEEE CEC},
author = { Tinkle Chugh },
title = {Scalarizing Functions in Bayesian Multiobjective
Optimization},
pages = {1--8},
doi = {10.1109/CEC48606.2020.9185706}
}
@incollection{CinFerLopAl2021evocop,
address = { Cham, Switzerland},
publisher = {Springer},
volume = 12692,
series = {Lecture Notes in Computer Science},
year = 2021,
booktitle = {Proceedings of EvoCOP 2021 -- 21th European Conference on Evolutionary Computation in Combinatorial Optimization },
editor = { Christine Zarges and Verel, S{\'e}bastien },
author = { Christian Cintrano and Javier Ferrer and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Alba, Enrique },
title = {Hybridization of Racing Methods with Evolutionary Operators
for Simulation Optimization of Traffic Lights Programs},
abstract = {In many real-world optimization problems, like the traffic
light scheduling problem tackled here, the evaluation of
candidate solutions requires the simulation of a process
under various scenarios. Thus, good solutions should not only
achieve good objective function values, but they must be
robust (low variance) across all different scenarios.
Previous work has revealed the effectiveness of IRACE for
this task. However, the operators used by IRACE to generate
new solutions were designed for configuring algorithmic
parameters, that have various data types (categorical,
numerical, etc.). Meanwhile, evolutionary algorithms have
powerful operators for numerical optimization, which could
help to sample new solutions from the best ones found in the
search. Therefore, in this work, we propose a hybridization
of the elitist iterated racing mechanism of IRACE with
evolutionary operators from differential evo- lution and
genetic algorithms. We consider a realistic case study
derived from the traffic network of Malaga (Spain) with 275
traffic lights that should be scheduled optimally. After a
meticulous study, we discovered that the hybrid algorithm
comprising IRACE plus differential evolution offers
statistically better results than conventional algorithms and
also improves travel times and reduces pollution.},
keywords = {Hybrid algorithms, Evolutionary algorithms, Simulation
optimization, Uncertainty, Traffic light planning},
pages = {17--33},
doi = {10.1007/978-3-030-72904-2_2},
annote = {Extended version published in Evolutionary Computation journal~\cite{CinFerLopAlb2022irace}.}
}
@inproceedings{CirJohMcGZha2001,
author = {Jill Cirasella and David S. Johnson and Lyle A. McGeoch and Weixiong Zhang},
title = {The Asymmetric Traveling Salesman Problem: Algorithms,
Instance Generators, and Tests},
booktitle = {Algorithm Engineering and Experimentation, Third
International Workshop, {ALENEX} 2001, Washington, DC, USA,
January 5-6, 2001, Revised Papers},
pages = {32--59},
series = {Lecture Notes in Computer Science},
volume = 2153,
publisher = {Springer},
address = { Berlin, Germany},
year = 2001,
doi = {10.1007/3-540-44808-X_3},
editor = {Adam L. Buchsbaum and Jack Snoeyink}
}
@inproceedings{ClaKar1992electronic,
author = {Jon Claerbout and Martin Karrenbach},
year = 1992,
title = {Electronic documents give reproducible research a new
meaning},
booktitle = {SEG Technical Program Expanded Abstracts 1992},
publisher = {Society of Exploration Geophysicists},
pages = {601--604},
doi = {10.1190/1.1822162},
annote = {Proposed a reproducibility taxonomy, defined reproducibility
and taxonomy}
}
@misc{CleKen2011spso,
author = { Clerc, Maurice and J. Kennedy },
title = {Standard {PSO} 2011},
howpublished = {Particle Swarm Central},
year = 2011,
url = {http://www.particleswarm.info/}
}
@unpublished{Clerc2012spso,
title = {Standard {Particle} {Swarm} {Optimisation}},
author = { Clerc, Maurice },
url = {https://hal.archives-ouvertes.fr/hal-00764996},
numpages = 15,
year = 2012,
month = sep,
hal_id = {hal-00764996},
hal_version = {v1},
keywords = {particle swarm optimisation},
abstract = {Since 2006, three successive standard PSO versions have been
put on line on the Particle Swarm Central
(\url{http://particleswarm.info}), namely SPSO 2006, 2007,
and 2011. The basic principles of all three versions can be
informally described the same way, and in general, this
statement holds for almost all PSO variants. However, the
exact formulae are slightly different, because they took
advantage of latest theoretical analysis available at the
time they were designed.},
note = {hal-00764996}
}
@incollection{Coe2015multi,
title = {Multi-objective Evolutionary Algorithms in Real-World
Applications: Some Recent Results and Current Challenges},
author = { Carlos A. {Coello Coello} },
booktitle = {Advances in Evolutionary and Deterministic Methods for
Design, Optimization and Control in Engineering and Sciences},
pages = {3--18},
year = 2015,
doi = {10.1007/978-3-319-11541-2_1},
publisher = {Springer}
}
@book{CoeLamVVe2007:book,
author = { Carlos A. {Coello Coello} and Gary B. Lamont and David A. {Van Veldhuizen} },
title = {Evolutionary Algorithms for Solving Multi-Objective Problems},
year = 2007,
publisher = {Springer},
address = { New York, NY},
edition = {2nd},
doi = {10.1007/978-0-387-36797-2}
}
@incollection{CoeSie2004igd,
address = { Heidelberg, Germany},
series = {Lecture Notes in Artificial Intelligence},
volume = 2972,
booktitle = {Proceedings of MICAI},
publisher = {Springer},
year = 2004,
editor = {Monroy, Ra{\'u}l and Arroyo-Figueroa, Gustavo and Sucar, Luis
Enrique and Sossa, Humberto},
author = { Carlos A. {Coello Coello} and Reyes-Sierra, Margarita},
title = {A Study of the Parallelization of a Coevolutionary
Multi-objective Evolutionary Algorithm},
pages = {688--697},
keywords = {IGD},
annote = {Introduces Inverted Generational Distance (IGD)}
}
@inproceedings{Coello2000cec,
month = jul,
address = {Piscataway, NJ},
publisher = {IEEE Press},
year = 2000,
booktitle = {Proceedings of the 2000 Congress on Evolutionary Computation (CEC'00)},
key = {IEEE CEC},
author = { Carlos A. {Coello Coello} },
title = {Handling Preferences in Evolutionary Multiobjective
Optimization: A Survey},
pages = {30--37}
}
@incollection{Coello2017results,
volume = 10687,
series = {Lecture Notes in Computer Science},
year = 2017,
address = { Cham, Switzerland},
publisher = {Springer International Publishing},
booktitle = {Theory and Practice of Natural Computing - 6th International Conference,
{TPNC} 2017},
editor = {Carlos Mart{\'i}n{-}Vide and Roman Neruda and Miguel A. Vega{-}Rodr{\'i}guez},
author = { Carlos A. {Coello Coello} },
title = {Recent Results and Open Problems in Evolutionary Multiobjective Optimization},
pages = {3--21}
}
@book{Cohen1995ai,
author = {Paul R. Cohen},
title = {Empirical Methods for Artificial Intelligence},
publisher = {MIT Press},
address = {Cambridge, MA},
year = 1995
}
@incollection{Cohen82,
author = { G. Cohen },
title = {Optimal Control of Water Supply Networks},
booktitle = {Optimization and Control of Dynamic Operational
Research Models},
pages = {251--276},
publisher = {North-Holland Publishing Company},
year = 1982,
editor = { S. G. Tzafestas },
volume = 4,
chapter = 8,
address = {Amsterdam}
}
@inproceedings{ColDorMan92:ecal,
publisher = {MIT Press, Cambridge, MA},
editor = {F. J. Varela and P. Bourgine},
year = 1992,
booktitle = {Proceedings of the First European Conference on
Artificial Life},
author = { Alberto Colorni and Marco Dorigo and Vittorio Maniezzo },
title = {Distributed Optimization by Ant Colonies},
pages = {134--142}
}
@incollection{ColMonGauSli07,
volume = 4926,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
year = 2008,
doi = {10.1007/978-3-540-79305-2},
shorteditor = {Monmarch{\'e}, Nicolas and others},
editor = {Monmarch{\'e}, Nicolas and Talbi, El-Ghazali and Collet, Pierre and Marc Schoenauer and Lutton, Evelyne},
booktitle = {Artificial Evolution},
author = {Sonia Colas and Nicolas Monmarch{\'e} and Pierre
Gaucher and Mohamed Slimane},
pages = {87--99},
title = {Artificial Ants for the Optimization of Virtual
Keyboard Arrangement for Disabled People}
}
@book{ConSchVic2009,
author = {Andrew R. Conn and Katya Scheinberg and Luis N. Vicente},
title = {Introduction to Derivative-Free Optimization},
publisher = {Society for Industrial and Applied Mathematics, Philadelphia, PA, USA},
year = 2009,
series = {MPS--SIAM Series on Optimization}
}
@misc{ConcordeSolver,
author = { David Applegate and Robert E. Bixby and Va{\v{s}}ek Chv{\'a}tal and William J. Cook },
title = {Concorde {TSP} Solver},
howpublished = {\url{http://www.math.uwaterloo.ca/tsp/concorde.html}},
note = {Version visited last on 15 April 2014},
year = 2014
}
@book{Conover99:pns,
author = { W. J. Conover },
title = {Practical Nonparametric Statistics},
publisher = {John Wiley \& Sons},
address = { New York, NY},
year = 1999,
edition = {3rd}
}
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author = {Cook, Stephen A.},
title = {The Complexity of Theorem-proving Procedures},
booktitle = {Proceedings of the Third Annual ACM Symposium on Theory of
Computing},
series = {STOC '71},
year = 1971,
location = {Shaker Heights, Ohio, USA},
pages = {151--158},
numpages = 8,
doi = {10.1145/800157.805047},
acmid = 805047,
publisher = {ACM}
}
@book{Cook2012,
author = { William J. Cook },
title = {In Pursuit of the Traveling Salesman},
publisher = {Princeton University Press, Princeton, NJ},
year = 2012
}
@incollection{Cook2019,
year = 2019,
editor = {Bernhard Steffen and Gerhard Woeginger},
address = { Cham, Switzerland},
publisher = {Springer},
volume = 10000,
series = {Lecture Notes in Computer Science},
booktitle = {Computing and Software Science: State of the Art and Perspectives},
title = {Computing in Combinatorial Optimization},
author = { William J. Cook },
pages = {27--47},
doi = {10.1007/978-3-319-91908-9_3}
}
@incollection{CorKno2001pesa2,
publisher = {Morgan Kaufmann Publishers, San Francisco, CA},
editor = {Erik D. Goodman},
year = 2001,
booktitle = {Proceedings of the 3rd Annual Conference on Genetic and
Evolutionary Computation, GECCO 2001},
author = { David Corne and Jerram, Nick R. and Joshua D. Knowles and Oates,
Martin J.},
title = {{PESA-II}: Region-Based Selection in Evolutionary
Multiobjective Optimization},
pages = {283--290},
numpages = 8,
doi = {10.5555/2955239.2955289}
}
@inproceedings{CorKno2003cec,
year = 2003,
address = {Piscataway, NJ},
publisher = {IEEE Press},
month = dec,
booktitle = {Proceedings of the 2003 Congress on Evolutionary Computation (CEC'03)},
key = {IEEE CEC},
author = { David Corne and Joshua D. Knowles },
title = {Some Multiobjective Optimizers are Better than Others},
pages = {2506--2512}
}
@incollection{CorKno2003nfl,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 2632,
series = {Lecture Notes in Computer Science},
editor = { Carlos M. Fonseca and Peter J. Fleming and Eckart Zitzler and Kalyanmoy Deb and Lothar Thiele },
year = 2003,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2003},
author = { David Corne and Joshua D. Knowles },
title = {No free lunch and free leftovers theorems for multiobjective
optimisation problems},
pages = {327--341},
doi = {10.1007/3-540-36970-8_23}
}
@incollection{CorKnoOat2000ppsn,
anote = {IC.29},
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Marc Schoenauer and others},
aeditor = { Marc Schoenauer and Kalyanmoy Deb and G{\"u}nther Rudolph and Xin Yao and E. Lutton and Juan-Juli{\'a}n Merelo and Hans-Paul Schwefel },
year = 2000,
volume = 1917,
series = {Lecture Notes in Computer Science},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {VI}},
author = { David Corne and Joshua D. Knowles and M. J. Oates},
title = {The {Pareto} Envelope-Based Selection Algorithm for
Multiobjective Optimization},
pages = {839--848}
}
@book{CorLeiRiv2009,
title = {Introduction to algorithms},
author = {Cormen, Thomas H. and Leiserson, Charles E. and Rivest, Ronald L. and Stein, Clifford},
year = 2009,
publisher = {MIT Press},
address = {Cambridge, MA}
}
@incollection{CorRey2011gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2011,
editor = {Natalio Krasnogor and Pier Luca Lanzi},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011},
author = { David Corne and Reynolds, Alan},
title = {Evaluating optimization algorithms: bounds on the performance
of optimizers on unseen problems},
pages = {707--710},
doi = {10.1145/2001858.2002073},
supplement = {http://is.gd/evalopt}
}
@inproceedings{CorViaHerMor2000bwas,
month = sep # { 7--9},
year = 2000,
date = {2000-09-07/2000-09-09},
organization = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and Martin Middendorf and Thomas St{\"u}tzle },
booktitle = {Abstract proceedings of ANTS 2000 -- From Ant
Colonies to Artificial Ants: Second International
Workshop on Ant Algorithms},
author = { Oscar Cord{\'o}n and I. Fern{\'a}ndez de Viana and Francisco Herrera and L. Moreno},
title = {A New {ACO} Model Integrating Evolutionary Computation
Concepts: The Best-Worst Ant System},
pages = {22--29}
}
@incollection{CowKenSou2000hyper,
publisher = {Springer},
volume = 2079,
series = {Lecture Notes in Computer Science},
year = 2000,
editor = {Edmund K. Burke and Wilhelm Erben},
booktitle = {PATAT 2000: Proceedings of the 3rd International Conference
of the Practice and Theory of Automated Timetabling},
author = {Peter I. Cowling and Graham Kendall and Eric Soubeiga},
title = {A Hyperheuristic Approach to Scheduling a Sales Summit},
pages = {176--190},
doi = {10.1007/3-540-44629-X_11},
annote = {First mention of the term hyper-heuristic.}
}
@book{Crawley2012rbook,
author = {M. J. Crawley},
title = {The \proglang{R} Book},
publisher = {Wiley},
year = 2012,
edition = {2nd}
}
@techreport{CroGloThoTra1963,
author = {W. B. Crowston and F. Glover and G. L. Thompson and
J. D. Trawick},
title = {Probabilistic and Parametric Learning Combinations of Local
Job Shop Scheduling Rules},
institution = {GSIA, Carnegie-Mellon University, Pittsburgh, PA, USA},
year = 1963,
number = {No.\ 117},
type = {ONR Research Memorandum}
}
@techreport{Cul92,
author = { Joseph C. Culberson },
title = {Iterated Greedy Graph Coloring and the Difficulty
Landscape},
institution = {Department of Computing Science, The University of
Alberta, Edmonton, Alberta, Canada},
year = 1992,
number = {92-07}
}
@inproceedings{CulBeaPap95,
author = { Joseph C. Culberson and A. Beacham and D. Papp},
title = {Hiding our Colors},
booktitle = {Proceedings of the CP'95 Workshop on Studying and Solving
Really Hard Problems},
pages = {31--42},
year = 1995,
address = {Cassis, France},
month = sep
}
@incollection{CulLuo1996,
series = {{DIMACS} Series on Discrete Mathematics and Theoretical Computer Science},
volume = 26,
year = 1996,
address = { Providence, RI},
publisher = {American Mathematical Society},
booktitle = {Cliques, Coloring, and Satisfiability: Second {DIMACS}
Implementation Challenge},
editor = {David S. Johnson and Michael A. Trick },
author = { Joseph C. Culberson and F. Luo},
title = {Exploring the $k$-colorable Landscape with Iterated Greedy},
pages = {245--284}
}
@book{Cumming2012,
author = {Jeff Cumming},
title = {Understanding the New Statistics -- Effect Sizes, Confidence Intervals, and Meta-analysis},
publisher = {Taylor \& Francis},
year = 2012
}
@incollection{DanDeC2014,
year = 2014,
publisher = {SciTePress},
booktitle = {{ICORES} 2014 - Proceedings of the 3rd International Conference on
Operations Research and Enterprise Systems},
editor = {Bego{\~{n}}a Vitoriano and Eric Pinson and Fernando Valente},
author = {Nguyen {Dang Thi Thanh} and Patrick {De Causmaecker}},
title = {Motivations for the Development of a Multi-objective
Algorithm Configurator},
pages = {328--333}
}
@incollection{DanDec2016neighborhood,
address = { Cham, Switzerland},
series = {Lecture Notes in Computer Science},
volume = 10079,
booktitle = {Learning and Intelligent Optimization, 10th International Conference, LION 10},
publisher = {Springer},
year = 2016,
editor = {Paola Festa and Meinolf Sellmann and Joaquin Vanschoren },
title = {Characterization of Neighborhood Behaviours in a
Multi-neighborhood Local Search Algorithm},
author = {Nguyen {Dang Thi Thanh} and Patrick {De Causmaecker}},
pages = {234--239}
}
@incollection{DanDec2019analysis,
address = { Cham, Switzerland},
series = {Lecture Notes in Computer Science},
volume = 11968,
booktitle = {Learning and Intelligent Optimization, 13th International Conference, LION 13},
publisher = {Springer},
year = 2019,
editor = {Nikolaos F. Matsatsinis and Yannis Marinakis and Panos M. Pardalos },
author = {Nguyen Dang and Patrick {De Causmaecker}},
title = {Analysis of Algorithm Components and Parameters: Some Case
Studies},
pages = {288--303},
abstract = {Modern high-performing algorithms are usually highly
parameterised, and can be configured either manually or by an
automatic algorithm configurator. The algorithm performance
dataset obtained after the configuration step can be used to
gain insights into how different algorithm parameters
influence algorithm performance. This can be done by a number
of analysis methods that exploit the idea of learning
prediction models from an algorithm performance dataset and
then using them for the data analysis on the importance of
variables. In this paper, we demonstrate the complementary
usage of three methods along this line, namely forward
selection, fANOVA and ablation analysis with surrogates on
three case studies, each of which represents some special
situations that the analyses can fall into. By these
examples, we illustrate how to interpret analysis results and
discuss the advantage of combining different analysis
methods.},
doi = {10.1007/978-3-030-05348-2_25}
}
@incollection{DanDoe2019gecco,
isbn = {978-1-4503-6111-8},
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
author = {Nguyen Dang and Carola Doerr },
title = {Hyper-parameter tuning for the ({1 + (\(\lambda\), \(\lambda\))}) {GA}},
pages = {889--897},
doi = {10.1145/3321707.3321725},
keywords = {irace; theory}
}
@incollection{DanPerCauStu2017:gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2017,
editor = { Peter A. N. Bosman },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017},
author = {Nguyen {Dang Thi Thanh} and P{\'e}rez C{\'a}ceres, Leslie and Patrick {De Causmaecker} and Thomas St{\"u}tzle },
title = {Configuring {\rpackage{irace}} Using Surrogate Configuration Benchmarks},
pages = {243--250},
keywords = {irace},
doi = {10.1145/3071178.3071238}
}
@inproceedings{DanPoz2018,
year = 2018,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2018 Congress on Evolutionary Computation (CEC 2018)},
key = {IEEE CEC},
title = {A Meta-Learning Algorithm Selection Approach for the Quadratic Assignment Problem},
author = {Dantas, Augusto Lopez and Pozo, Aurora Trinidad Ramirez},
pages = {1--8}
}
@incollection{Dandy03,
author = { Graeme C. Dandy and Matthew S. Gibbs },
editor = {Paul Bizier and Paul DeBarry},
title = {Optimizing System Operations and Water Quality},
publisher = {ASCE},
year = 2003,
booktitle = {Proceedings of World Water and Environmental
Resources Congress},
address = {Philadelphia, USA},
doi = {10.1061/40685(2003)127},
note = {on CD-ROM}
}
@phdthesis{Dang2018PhD,
title = {Data analytics for algorithm design},
school = {KU Leuven, Belgium},
author = {Nguyen {Dang Thi Thanh}},
year = 2018,
annote = {Supervised by Patrick {De Causmaecker}}
}
@incollection{DaoVerOchTom2012:gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2012,
editor = {Terence Soule and Jason H. Moore},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2012},
author = {Fabio Daolio and Verel, S{\'e}bastien and Gabriela Ochoa and Marco Tomassini},
title = {Local Optima Networks and the Performance of Iterated Local
Search},
pages = {369--376}
}
@inproceedings{DauBalBak2020different,
year = 2020,
editor = {Hugo Larochelle and Marc'Aurelio Ranzato and Raia Hadsell and
Maria{-}Florina Balcan and Hsuan{-}Tien Lin},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS
33)},
author = {Daulton, Samuel and Balandat, Maximilian and Bakshy, Eytan},
title = {Differentiable Expected Hypervolume Improvement for Parallel
Multi-Objective {Bayesian} Optimization},
pages = {9851--9864},
epub = {https://proceedings.neurips.cc/paper/2020/file/6fec24eac8f18ed793f5eaad3dd7977c-Paper.pdf}
}
@inproceedings{DeSchaetzen98,
author = { Werner de Schaetzen and Dragan A. Savic and Godfrey A. Walters },
title = {A genetic algorithm approach to pump scheduling in
water supply},
booktitle = {Hydroinformatics '98},
pages = {897--899},
year = 1998,
editor = { V. Babovic and L. C. Larsen },
address = {Rotterdam, Balkema}
}
@inproceedings{DeaBod1988aaai,
year = 1988,
booktitle = {Proceedings of the 7th National Conference on Artificial
Intelligence, AAAI-88},
url = {http://www.aaai.org/Conferences/AAAI/aaai88.php},
publisher = {{AAAI} Press\slash {MIT} Press, Menlo Park, CA},
editor = {Howard E. Shrobe and Tom M. Mitchell and Reid G. Smith},
author = {Thomas Dean and Mark S. Boddy},
title = {An Analysis of Time-Dependent Planning},
pages = {49--54},
keywords = {anytime, performance profiles}
}
@book{DeanVoss99:DAE,
author = { Angela Dean and Daniel Voss },
title = {Design and Analysis of Experiments},
publisher = {Springer},
address = { London, UK },
doi = {10.1007/b97673},
year = 1999
}
@incollection{Deb2008introduction,
editor = { J{\"u}rgen Branke and Kalyanmoy Deb and Kaisa Miettinen and Roman S{\l}owi{\'n}ski },
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 5252,
year = 2008,
booktitle = {Multiobjective Optimization: Interactive and Evolutionary
Approaches},
title = {Introduction to evolutionary multiobjective optimization},
author = { Kalyanmoy Deb },
abstract = {In its current state, evolutionary multiobjective
optimization (EMO) is an established field of research and
application with more than 150 PhD theses, more than ten
dedicated texts and edited books, commercial softwares and
numerous freely downloadable codes, a biannual conference
series running successfully since 2001, special sessions and
workshops held at all major evolutionary computing
conferences, and full-time researchers from universities and
industries from all around the globe. In this chapter, we
provide a brief introduction to EMO principles, illustrate
some EMO algorithms with simulated results, and outline the
current research and application potential of EMO. For
solving multiobjective optimization problems, EMO procedures
attempt to find a set of well-distributed Pareto-optimal
points, so that an idea of the extent and shape of the
Pareto-optimal front can be obtained. Although this task was
the early motivation of EMO research, EMO principles are now
being found to be useful in various other problem solving
tasks, enabling one to treat problems naturally as they
are. One of the major current research thrusts is to combine
EMO procedures with other multiple criterion decision making
(MCDM) tools so as to develop hybrid and interactive
multiobjective optimization algorithms for finding a set of
trade-off optimal solutions and then choose a preferred
solution for implementation. This chapter provides the
background of EMO principles and their potential to launch
such collaborative studies with MCDM researchers in the
coming years.},
doi = {10.1007/978-3-540-88908-3_3},
pages = {59--96}
}
@incollection{Deb2005,
year = 2005,
address = {Boston, MA},
publisher = {Springer},
editor = { Edmund K. Burke and Graham Kendall },
booktitle = {Search Methodologies},
title = {Multi-objective optimization},
author = { Kalyanmoy Deb },
pages = {273--316},
doi = {10.1007/0-387-28356-0_10}
}
@book{Deb:MOEA,
author = { Kalyanmoy Deb },
title = {Multi-Objective Optimization Using Evolutionary
Algorithms},
year = 2001,
publisher = {Wiley},
address = {Chichester, UK}
}
@incollection{DebAg1999polymut,
doi = {10.1007/978-3-7091-6384-9},
booktitle = {Artificial Neural Nets and Genetic Algorithms (ICANNGA-99)},
key = {ICANNGA},
year = 1999,
publisher = {Springer Verlag},
editor = {Andrej Dobnikar and Nigel C. Steele and David
W. Pearson and Rudolf F. Albrecht},
author = { Kalyanmoy Deb and S. Agrawal},
title = {A Niched-Penalty Approach for Constraint Handling in Genetic
Algorithms},
pages = {235--243},
keywords = {polynomial mutation}
}
@incollection{DebAgrPra2000ppsn,
anote = {IC.29},
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Marc Schoenauer and others},
aeditor = { Marc Schoenauer and Kalyanmoy Deb and G{\"u}nther Rudolph and Xin Yao and E. Lutton and Juan-Juli{\'a}n Merelo and Hans-Paul Schwefel },
year = 2000,
volume = 1917,
series = {Lecture Notes in Computer Science},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {VI}},
title = {A fast elitist non-dominated sorting genetic algorithm
for multi-objective optimization: {NSGA-II}},
author = { Kalyanmoy Deb and S. Agarwal and A. Pratap and T. Meyarivan},
pages = {849--858}
}
@techreport{DebJain02,
author = { Kalyanmoy Deb and Sachin Jain },
title = {Multi-Speed Gearbox Design Using Multi-Objective
Evolutionary Algorithms},
institution = {KanGAL},
year = 2002,
number = 2002001,
month = feb
}
@incollection{DebMyb2016gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2016,
editor = { Tobias Friedrich and Frank Neumann and Andrew M. Sutton },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2016},
title = {Breaking the billion-variable barrier in real-world
optimization using a customized evolutionary algorithm},
author = { Kalyanmoy Deb and Myburgh, Christie},
pages = {653--660}
}
@incollection{DebSin2009emo,
address = { Heidelberg, Germany},
publisher = {Springer},
year = 2009,
series = {Lecture Notes in Computer Science},
volume = 5467,
editor = { Matthias Ehrgott and Carlos M. Fonseca and Xavier Gandibleux and Jin-Kao Hao and Marc Sevaux },
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2009},
title = {Solving Bilevel Multi-Objective Optimization Problems Using
Evolutionary Algorithms},
author = { Kalyanmoy Deb and Sinha, Ankur},
pages = {110--124}
}
@incollection{DebSun2006gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2006,
editor = {M. Cattolico and others},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2006},
author = { Kalyanmoy Deb and Sundar, J.},
title = {Reference point based multi-objective optimization using
evolutionary algorithms},
pages = {635--642},
annote = {Proposed R-NSGA-II},
doi = {10.1145/1143997.1144112}
}
@inproceedings{DebTewDixDut2007finding,
address = {Piscataway, NJ},
publisher = {IEEE Press},
year = 2007,
booktitle = {Proceedings of the 2007 Congress on Evolutionary Computation (CEC 2007)},
key = {IEEE CEC},
title = {Finding trade-off solutions close to {KKT} points using
evolutionary multi-objective optimization},
author = { Kalyanmoy Deb and Tewari, Rahul and Dixit, Mayur and Dutta, Joydeep},
pages = {2109--2116}
}
@techreport{DebThiLau2001dtlz,
author = { Kalyanmoy Deb and Lothar Thiele and Marco Laumanns and Eckart Zitzler },
institution = {Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Z{\"u}rich, Switzerland},
number = 112,
title = {Scalable Test Problems for Evolutionary
Multi-Objective Optimization},
year = 2001,
keywords = {DTLZ benchmark},
note = {Do not cite this TR! It is incorrect and it is superseeded by~\cite{DebThiLau2005dtlz}}
}
@incollection{DebThiLau2005dtlz,
address = { London, UK },
year = 2005,
month = jan,
editor = {Abraham, Ajith and Jain, Lakhmi and Goldberg, Robert},
series = {Advanced Information and Knowledge Processing},
publisher = {Springer},
booktitle = {Evolutionary Multiobjective Optimization},
author = { Kalyanmoy Deb and Lothar Thiele and Marco Laumanns and Eckart Zitzler },
title = {Scalable Test Problems for Evolutionary Multiobjective
Optimization},
pages = {105--145},
keywords = {DTLZ benchmark},
doi = {10.1007/1-84628-137-7_6}
}
@incollection{DeeKar1982,
author = {William A. {Dees, Jr.} and Patrick G. Karger},
title = {Automated Rip-up and Reroute Techniques},
booktitle = {DAC'82, Proceedings of the 19th Design Automation Workshop},
publisher = {IEEE Press},
year = 1982,
pages = {432--439}
}
@phdthesis{DenBestenPhD,
author = { Matthijs L. {den Besten} },
title = {Simple Metaheuristics for Scheduling},
school = {FB Informatik, TU Darmstadt, Germany},
year = 2004,
url = {http://tuprints.ulb.tu-darmstadt.de/516/}
}
@incollection{DenCosEsp2013igd,
isbn = {978-1-4503-1963-8},
address = { New York, NY},
publisher = {ACM Press},
year = 2013,
editor = { Christian Blum and Alba, Enrique },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2013},
title = {Many-objective optimization using differential evolution with
variable-wise mutation restriction},
author = {Denysiuk, Roman and Costa, Lino and Esp{\'i}rito Santo,
Isabel},
pages = {591--598}
}
@inproceedings{DenDonScoLiLiFei2009imagenet,
title = {Imagenet: A large-scale hierarchical image database},
author = {Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia
and Li, Kai and Fei-Fei, Li},
booktitle = {Computer Vision and Pattern Recognition, 2009. CVPR
2009. IEEE Conference on},
pages = {248--255},
year = 2009,
organization = {IEEE}
}
@inproceedings{DesRit2018cec,
year = 2018,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2018 Congress on Evolutionary Computation (CEC 2018)},
key = {IEEE CEC},
author = { Marcelo {De Souza} and Marcus Ritt},
title = {An Automatically Designed Recombination Heuristic for the
Test-Assignment Problem},
pages = {1--8},
doi = {10.1109/CEC.2018.8477801}
}
@incollection{DesRit2018evo,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 10782,
series = {Lecture Notes in Computer Science},
year = 2018,
booktitle = {Proceedings of EvoCOP 2018 -- 18th European Conference on Evolutionary Computation in Combinatorial Optimization },
editor = { Arnaud Liefooghe and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
author = { Marcelo {De Souza} and Marcus Ritt},
title = {Automatic Grammar-Based Design of Heuristic Algorithms for
Unconstrained Binary Quadratic Programming},
pages = {67--84},
doi = {10.1007/978-3-319-77449-7_5}
}
@misc{DesRit2018hhbqp,
author = { Marcelo {De Souza} and Marcus Ritt},
title = {Hybrid Heuristic for Unconstrained Binary Quadratic
Programming -- Source Code of {HHBQP}},
howpublished = {\url{https://github.com/souzamarcelo/hhbqp}},
year = 2018
}
@misc{DesRitLop2020acviz,
author = { Marcelo {De Souza} and Marcus Ritt and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and P{\'e}rez C{\'a}ceres, Leslie },
title = {{\softwarepackage{ACVIZ}}: A Tool for the Visual Analysis of
the Configuration of Algorithms with {\rpackage{irace}} --
Source Code},
howpublished = {\url{https://github.com/souzamarcelo/acviz}},
year = 2020
}
@misc{DesRitLopPer2020zenodo,
author = { Marcelo {De Souza} and Marcus Ritt and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and P{\'e}rez C{\'a}ceres, Leslie },
title = {{\softwarepackage{ACVIZ}}: Algorithm Configuration
Visualizations for {\rpackage{irace}}: Supplementary
material},
howpublished = {\url{http://doi.org/10.5281/zenodo.4714582}},
month = sep,
year = 2020,
publisher = {Zenodo}
}
@phdthesis{Dewez2004PhD,
author = {Sophie Dewez},
title = {On the toll setting problem},
school = {Facult\'{e} de Sciences, Universit\'{e} Libre de Bruxelles},
year = 2014,
annote = {Supervised by Dr. Martine Labb\'{e}}
}
@inproceedings{DiaYan2008succint,
title = {Succinct approximate convex {Pareto} curves},
author = {Diakonikolas, Ilias and Mihalis Yannakakis },
booktitle = {Proceedings of the nineteenth annual ACM-SIAM symposium on
Discrete algorithms},
year = 2008,
organization = {Society for Industrial and Applied Mathematics},
pages = {74--83}
}
@incollection{DieValAreRodSua2014ants,
volume = 8667,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Marco Dorigo and others},
year = 2014,
booktitle = {Swarm Intelligence, 9th International Conference, ANTS 2014},
author = {Diego D{\'i}az and Pablo Valledor and Paula Areces and Jorge Rodil and Montserrat Su{\'a}rez},
title = {An {ACO} Algorithm to Solve an Extended Cutting Stock Problem for Scrap Minimization in a Bar Mill},
pages = {13--24}
}
@incollection{DigChiSch2006,
publisher = {IOS Press},
year = 2006,
booktitle = {Proceedings of the 17th European Conference on Artificial Intelligence,
{ECAI} 2006, Riva del Garda, Italy, August29 - September 1, 2006},
editor = {Brewka, Gerhard and Coradeschi, Silvia and Perini, Anna and Traverso, Paolo},
author = {Luca {Di Gaspero} and Marco Chiarandini and Andrea Schaerf},
title = {A Study on the Short-Term Prohibition Mechanisms in Tabu Search},
pages = {83--87}
}
@incollection{DigRenUrl2013cp,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 8124,
booktitle = {Principles and Practice of Constraint Programming},
publisher = {Springer},
year = 2013,
editor = {Christian Schulte},
title = {Constraint-Based Approaches for Balancing Bike Sharing
Systems},
author = {Luca {Di Gaspero} and Andrea Rendl and Tommaso Urli },
pages = {758--773},
doi = {10.1007/978-3-642-40627-0_56},
keywords = {F-race}
}
@incollection{DigRenUrl2013hyme,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 7919,
series = {Lecture Notes in Computer Science},
editor = { Mar{\'i}a J. Blesa and Christian Blum and Paola Festa and Andrea Roli and M. Sampels },
isbn = {978-3-642-38515-5},
year = 2013,
booktitle = {Hybrid Metaheuristics},
title = {A Hybrid {ACO+CP} for Balancing Bicycle Sharing Systems},
author = {Luca {Di Gaspero} and Andrea Rendl and Tommaso Urli },
pages = {198--212},
keywords = {F-race},
doi = {10.1007/978-3-642-38516-2_16}
}
@incollection{DobNebLop2022ants,
volume = 13491,
series = {Lecture Notes in Computer Science},
address = { Cham, Switzerland},
publisher = {Springer},
editor = { Marco Dorigo and Heiko Hamann and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Jos{\'e} Garc{\'i}a-Nieto and Andries Engelbrecht and Carlo Pinciroli and Volker Strobel and Camacho-Villal\'{o}n, Christian Leonardo},
year = 2022,
booktitle = {Swarm Intelligence, 13th International Conference, ANTS 2022},
author = {Doblas, Daniel and Nebro, Antonio J. and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Jos{\'e} Garc{\'i}a-Nieto and Carlos A. {Coello Coello} },
title = {Automatic Design of Multi-objective Particle Swarm
Optimizers},
doi = {10.1007/978-3-031-20176-9_3},
pages = {28--40}
}
@incollection{DomHul2000,
year = 2000,
address = { New York, NY},
publisher = {ACM Press},
booktitle = {The 6th {ACM} {SIGKDD} International Conference on Knowledge
Discovery and Data Mining, {KDD} 2000},
epub = {http://dl.acm.org/citation.cfm?id=347090},
editor = {Raghu Ramakrishnan and Salvatore J. Stolfo and Roberto
J. Bayardo and Ismail Parsa},
key = {SIGKDD},
author = {Domingos, Pedro and Hulten, Geoff},
title = {Mining high-speed data streams},
pages = {71--80}
}
@incollection{DorDic99:nio,
address = {London, UK},
year = 1999,
publisher = {McGraw Hill},
editor = { David Corne and Marco Dorigo and Fred Glover },
booktitle = {New Ideas in Optimization},
author = { Marco Dorigo and Gianni A. {Di Caro} },
title = {The {Ant} {Colony} {Optimization} Meta-Heuristic},
pages = {11--32},
anote = {Also available as Technical Report IRIDIA/99-1,
Universit{\'e} Libre de Bruxelles, Belgium}
}
@techreport{DorGam1996:iridia,
author = { Marco Dorigo and L. M. Gambardella },
title = {Ant {Colony} {System}},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 1996,
number = {IRIDIA/96-05}
}
@techreport{DorManCol1991:tr16Revised,
author = { Marco Dorigo and Vittorio Maniezzo and Alberto Colorni },
title = {The {Ant} {System}: {An} autocatalytic optimizing process},
institution = {Dipartimento di Elettronica, Politecnico di Milano, Italy},
year = 1991,
number = {91-016 Revised}
}
@techreport{DorManCol91:tr16,
author = { Marco Dorigo and Vittorio Maniezzo and Alberto Colorni },
title = {Positive Feedback as a Search Strategy},
institution = {Dipartimento di Elettronica, Politecnico di Milano, Italy},
year = 1991,
number = {91-016}
}
@incollection{DorMonOliStu2011eorms,
doi = {10.1002/9780470400531},
year = 2011,
publisher = {John Wiley \& Sons},
editor = {J. J. Cochran},
booktitle = {Wiley Encyclopedia of Operations Research and
Management Science},
author = { Marco Dorigo and Marco A. {Montes de Oca} and Sabrina Oliveira and Thomas St{\"u}tzle },
title = {Ant Colony Optimization},
pages = {114--125},
volume = 1
}
@incollection{DorStu02:mh,
publisher = {Kluwer Academic Publishers, Norwell, MA},
year = 2002,
editor = { Fred Glover and Gary A. Kochenberger},
booktitle = {Handbook of Metaheuristics},
author = { Marco Dorigo and Thomas St{\"u}tzle },
title = {The Ant Colony Optimization Metaheuristic: Algorithms, Applications and Advances},
pages = {251--285}
}
@book{DorStu2004:book,
author = { Marco Dorigo and Thomas St{\"u}tzle },
title = {Ant Colony Optimization},
publisher = {MIT Press},
address = {Cambridge, MA},
year = 2004,
pagination = 305,
anote = {305 p}
}
@phdthesis{DorigoPhD,
author = { Marco Dorigo },
title = {Optimization, Learning and Natural Algorithms},
school = {Dipartimento di Elettronica, Politecnico di Milano, Italy},
year = 1992,
atype = {{Ph.D.} thesis},
note = {In Italian}
}
@incollection{Dre2009gecco,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2009},
address = { New York, NY},
year = 2009,
publisher = {ACM Press},
editor = { Franz Rothlauf },
author = { Johann Dreo },
title = {Using performance fronts for parameter setting of stochastic
metaheuristics},
pages = {2197--2200},
doi = {10.1145/1570256.1570301}
}
@incollection{DreDoeSem2019,
doi = {10.1145/3319619},
isbn = {978-1-4503-6748-6},
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
author = { Johann Dreo and Carola Doerr and Semet, Yann},
title = {Coupling the design of benchmark with algorithm in landscape-aware solver design},
pages = {1419--1420}
}
@incollection{DreLieVer2021paradiseo,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2021},
address = { New York, NY},
year = 2021,
publisher = {ACM Press},
editor = { Chicano, Francisco and Krzysztof Krawiec },
title = {Paradiseo: from a modular framework for evolutionary
computation to the automated design of metaheuristics: 22
years of {Paradiseo}},
doi = {10.1145/3449726.3463276},
author = { Johann Dreo and Arnaud Liefooghe and Verel, S{\'e}bastien and Marc Schoenauer and Juan-Juli{\'a}n Merelo and Quemy, Alexandre and Bouvier, Benjamin and Gmys, Jan},
pages = {1522--1530},
numpages = 9,
keywords = {metaheuristics, evolutionary computation, software framework,
automated algorithm design}
}
@incollection{DreSia02:ants,
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and Gianni A. {Di Caro} and M. Sampels },
volume = 2463,
series = {Lecture Notes in Computer Science},
year = 2002,
booktitle = {Ant Algorithms, Third International Workshop, ANTS
2002},
author = { Johann Dreo and P. Siarry},
title = {A New Ant Colony Algorithm Using the Heterarchical Concept
Aimed at Optimization of Multiminima Continuous Functions},
pages = {216--221}
}
@phdthesis{Dreo2003phd,
author = { Johann Dreo },
title = {Adaptation de la m{\'e}taheuristique des colonies de fourmis
pour l'optimisation difficile en variables continues:
Application en g{\'e}nie biologique et m{\'e}dical},
school = {\BIBdepartment{LERISS - Laboratoire d'{\'e}tude et de recherche en instrumentation, signaux et syst{\'e}mes}Universit{\'e} Paris XII Val de Marne},
year = 2003,
month = dec,
hal_id = {tel-00093143},
hal_version = {v1},
keywords = {metaheuristic ; continuous optimization ; global optimization
; imagery ; registration ; ant colony algorithm ; estimation
of distribution algorithm ; evolutionary computation ;
m{\'e}taheuristique ; optimisation continue ; optimisation
globale ; imagerie ; biom{\'e}dical ; recalage ; algorithme
de colonie de fourmis ; algorithme {\`a} estimation de
distribution ; algorithme {\'e}volutionnaire},
url = {https://tel.archives-ouvertes.fr/tel-00093143}
}
@incollection{DroJanWeg2002,
publisher = {Morgan Kaufmann Publishers},
editor = { De Jong, Kenneth A. and Poli, Riccardo and Rowe, Jonathan E.},
year = 2002,
booktitle = {Proceedings of the Seventh Workshop on Foundations of Genetic Algorithms (FOGA)},
title = {A new framework for the valuation of algorithms for black-box-optimization},
author = {Droste, Stefan and Jansen, Thomas and Ingo Wegener },
pages = {253--270}
}
@incollection{IshPanSha2020unbounded,
publisher = {IOS Press},
editor = {Giuseppe De Giacomo and Alejandro Catala and Bistra Dilkina
and Michela Milano and Senén Barro and Alberto Bugarín and
Jérôme Lang},
series = {Frontiers in Artificial Intelligence and Applications},
volume = 325,
year = 2020,
booktitle = {Proceedings of the 24th European Conference on Artificial Intelligence (ECAI)},
title = {A new framework of evolutionary multi-objective algorithms
with an unbounded external archive},
author = { Ishibuchi, Hisao and Pang, Lie Meng and Shang, Ke}
}
@inproceedings{Dru2009replicability,
author = {Chris Drummond},
title = {Replicability is not Reproducibility: Nor is it Good Science},
booktitle = {Proceedings of the Evaluation Methods for Machine Learning
Workshop at the 26th ICML},
address = {Montreal, Canada},
url = {http://www.site.uottawa.ca/~cdrummon/pubs/ICMLws09.pdf},
year = 2009
}
@incollection{DruThi2010,
volume = 6238,
year = 2010,
address = { Heidelberg, Germany},
publisher = {Springer},
editor = {Schaefer, Robert and Cotta, Carlos and Kolodziej,
Joanna and G{\"u}nther Rudolph },
series = {Lecture Notes in Computer Science},
booktitle = {Parallel Problem Solving from Nature, PPSN XI},
author = { M{\u{a}}d{\u{a}}lina M. Drugan and Dirk Thierens },
title = {Path-Guided Mutation for Stochastic {Pareto} Local Search Algorithms},
pages = {485--495}
}
@incollection{DuaSanMla2018vnd,
isbn = {978-3-319-07125-1},
publisher = {Springer International Publishing},
year = 2018,
booktitle = {Handbook of Heuristics},
editor = { Rafael Mart{\'i} and Panos M. Pardalos and Mauricio G. C. Resende },
author = { Duarte, Abraham and Jes{\'u}s S{\'a}nchez-Oro and Nenad Mladenovi{\'c} and Todosijevi{\'c}, Raca},
title = {Variable Neighborhood Descent},
pages = {341--367},
doi = {10.1007/978-3-319-07124-4_9}
}
@techreport{Dub2009:sls-ds,
author = { J{\'e}r{\'e}mie Dubois-Lacoste },
title = {Weight Setting Strategies for Two-Phase Local Search:
A Study on Biobjective Permutation Flowshop Scheduling},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2009,
number = {TR/IRIDIA/2009-024}
}
@incollection{DubHooStu2015gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2015,
editor = {Sara Silva and Anna I. Esparcia{-}Alc{\'{a}}zar },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015},
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Holger H. Hoos and Thomas St{\"u}tzle },
title = {On the Empirical Scaling Behaviour of State-of-the-art Local
Search Algorithms for the Euclidean {TSP}},
pages = {377--384},
doi = {10.1145/2739480.2754747}
}
@incollection{DubLopStu09:hm-bfsp,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 5818,
series = {Lecture Notes in Computer Science},
editor = { Mar{\'i}a J. Blesa and Christian Blum and Luca {Di Gaspero} and Andrea Roli and M. Sampels and Andrea Schaerf},
year = 2009,
booktitle = {Hybrid Metaheuristics},
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Effective Hybrid Stochastic Local Search Algorithms for
Biobjective Permutation Flowshop Scheduling},
pages = {100--114},
doi = {10.1007/978-3-642-04918-7_8}
}
@misc{DubLopStu10:journal-anytime-supp,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {{Supplementary material: Improving the Anytime Behavior of Two-Phase Local Search}},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2010-012}},
year = 2010
}
@misc{DubLopStu10:journal-bfsp-supp,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {{Supplementary material: A Hybrid TP+PLS Algorithm for Bi-objective Flow-shop Scheduling Problems}},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2010-001}},
year = 2010
}
@incollection{DubLopStu10:lion-bfsp,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 6073,
booktitle = {Learning and Intelligent Optimization, 4th International Conference, LION 4},
publisher = {Springer},
year = 2010,
editor = { Christian Blum and Roberto Battiti },
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Adaptive ``Anytime'' Two-Phase Local Search},
pages = {52--67},
doi = {10.1007/978-3-642-13800-3_5}
}
@incollection{DubLopStu2011gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2011,
editor = {Natalio Krasnogor and Pier Luca Lanzi},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011},
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Configuration of State-of-the-art Multi-Objective
Optimizers Using the {TP+PLS} Framework},
pages = {2019--2026},
doi = {10.1145/2001576.2001847}
}
@incollection{DubLopStu2012evocop,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 7245,
year = 2012,
editor = { Jin-Kao Hao and Martin Middendorf },
booktitle = {Proceedings of EvoCOP 2012 -- 12th European Conference on Evolutionary Computation in Combinatorial Optimization },
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {{Pareto} Local Search Algorithms for Anytime
Bi-objective Optimization},
pages = {206--217},
doi = {10.1007/978-3-642-29124-1_18}
}
@incollection{DubLopStu2013hm,
url = {http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-30670-9},
year = 2013,
volume = 434,
series = {Studies in Computational Intelligence},
editor = { Talbi, El-Ghazali },
publisher = {Springer Verlag},
booktitle = {Hybrid Metaheuristics},
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Combining Two Search Paradigms for Multi-objective
Optimization: {Two}-{Phase} and {Pareto} Local Search},
pages = {97--117},
doi = {10.1007/978-3-642-30671-6_3}
}
@misc{DubPagStu2017:flowshop-makespan-supp,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Federico Pagnozzi and Thomas St{\"u}tzle },
title = {Supplementary material: {An} iterated greedy algorithm with optimization of partial
solutions for the permutation flowshop problem},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2013-006}},
year = 2017
}
@inproceedings{DubStu2017cec,
year = 2017,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2017 Congress on Evolutionary Computation (CEC 2017)},
key = {IEEE CEC},
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Thomas St{\"u}tzle },
title = {Tuning of a Stigmergy-based Traffic Light Controller as a Dynamic Optimization Problem},
pages = {1--8}
}
@mastersthesis{Dubois2009,
author = { J{\'e}r{\'e}mie Dubois-Lacoste },
title = {A study of {Pareto} and Two-Phase Local Search
Algorithms for Biobjective Permutation Flowshop
Scheduling},
school = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2009
}
@mastersthesis{Dubois2010,
author = { J{\'e}r{\'e}mie Dubois-Lacoste },
title = {Effective Stochastic Local Search Algorithms For
Bi-Objective Permutation Flowshop Scheduling},
school = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
type = {Rapport d'avancement de recherches pr\'esent\'e pour
la Formation Doctorale en sciences de l'Ing\'enieur},
year = 2010
}
@phdthesis{DuboisPhD,
author = { J{\'e}r{\'e}mie Dubois-Lacoste },
title = {Anytime Local Search for Multi-Objective Combinatorial
Optimization: Design, Analysis and Automatic Configuration},
school = {IRIDIA, {\'E}cole polytechnique, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2014,
annote = {Supervised by Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }
}
@misc{Duecketal1999:patent,
author = { Gunter Dueck and Martin Maehler and Johannes Schneider and Gerhard Schrimpf and Hermann Stamm-Wilbrandt},
title = {Optimization with Ruin Recreate},
howpublished = {US Patent 6,418,398 B1},
month = jul,
year = 2002,
note = {Filed on October 1, 1999 and granted on July 9, 2002;
Assignee is IBM Corporation, Armonk, NY (US)}
}
@incollection{DumStu2003,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
year = 2003,
editor = { G{\"u}nther R. Raidl and Gottlieb, Jens},
volume = 2611,
booktitle = {Proceedings of EvoCOP 2003 -- 3rd European Conference on Evolutionary Computation in Combinatorial Optimization },
author = {Irina Dumitrescu and Thomas St{\"u}tzle },
title = {Combinations of Local Search and Exact Algorithms},
pages = {211--223},
doi = {10.1007/3-540-36605-9_20}
}
@incollection{DumStu2009,
address = { New York, NY},
series = {Annals of Information Systems},
volume = 10,
year = 2009,
publisher = {Springer},
booktitle = {Matheuristics---Hybridizing Metaheuristics and Mathematical
Programming},
editor = { Vittorio Maniezzo and Thomas St{\"u}tzle and Stefan Vo{\ss} },
author = {Irina Dumitrescu and Thomas St{\"u}tzle },
title = {Usage of Exact Algorithms to Enhance Stochastic Local Search Algorithms},
pages = {103--134}
}
@incollection{DurGarNebCoe2009emo,
address = { Heidelberg, Germany},
publisher = {Springer},
year = 2009,
series = {Lecture Notes in Computer Science},
volume = 5467,
editor = { Matthias Ehrgott and Carlos M. Fonseca and Xavier Gandibleux and Jin-Kao Hao and Marc Sevaux },
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2009},
author = { Durillo, Juan J. and Jos{\'e} Garc{\'i}a-Nieto and Nebro, Antonio J. and Carlos A. {Coello Coello} and Luna, Francisco and Alba, Enrique },
title = {Multi-Objective Particle Swarm Optimizers: An Experimental
Comparison},
pages = {495--509},
abstract = {Particle Swarm Optimization (PSO) has received increasing
attention in the optimization research community since its
first appearance in the mid-1990s. Regarding multi-objective
optimization, a considerable number of algorithms based on
Multi-Objective Particle Swarm Optimizers (MOPSOs) can be
found in the specialized literature. Unfortunately, no
experimental comparisons have been made in order to clarify
which MOPSO version shows the best performance. In this
paper, we use a benchmark composed of three well-known
problem families (ZDT, DTLZ, and WFG) with the aim of
analyzing the search capabilities of six representative
state-of-the-art MOPSOs, namely, NSPSO, SigmaMOPSO, OMOPSO,
AMOPSO, MOPSOpd, and CLMOPSO. We additionally propose a new
MOPSO algorithm, called SMPSO, characterized by including a
velocity constraint mechanism, obtaining promising results
where the rest perform inadequately.},
isbn = {978-3-642-01020-0}
}
@incollection{DurNebLunAlb2009,
address = { Heidelberg, Germany},
publisher = {Springer},
year = 2009,
series = {Lecture Notes in Computer Science},
volume = 5467,
editor = { Matthias Ehrgott and Carlos M. Fonseca and Xavier Gandibleux and Jin-Kao Hao and Marc Sevaux },
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2009},
title = {On the Effect of the Steady-State Selection Scheme in
Multi-Objective Genetic Algorithms},
author = { Durillo, Juan J. and Nebro, Antonio J. and Luna, Francisco and Alba, Enrique },
pages = {183--197}
}
@incollection{DwoKumNao2001rank,
isbn = {1-58113-348-0},
year = 2001,
address = { New York, NY},
publisher = {ACM Press},
booktitle = {Proceedings of the Tenth International World Wide Web
Conference, {WWW} 10},
editor = {Vincent Y. Shen and Nobuo Saito and Michael R. Lyu and Mary
Ellen Zurko},
title = {Rank aggregation methods for the Web},
author = {Dwork, Cynthia and Kumar, Ravi and Naor, Moni and Sivakumar,
D.},
doi = {10.1145/371920.372165},
keywords = {Kemeny ranking,multi-word queries,rank aggregation,ranking
functions,spam},
pages = {613--622}
}
@manual{EPANET2Manual,
title = {{EPANET} 2 Users Manual},
author = { L. A. Rossman },
organization = {U.S. Environmental Protection Agency},
address = {Cincinnati, USA},
year = 2000
}
@manual{EPANET94,
title = {{EPANET} User's Guide},
author = { L. A. Rossman },
organization = {Risk Reduction Engineering Laboratory, Office of
Research and Development, U.S. Environmental
Protection Agency},
address = {Cincinnati, USA},
year = 1994
}
@inproceedings{EPANET_Toolkit,
author = { L. A. Rossman },
title = {The {EPANET} {Programmer's} {Toolkit} for Analysis
of Water Distribution Systems},
booktitle = {Proceedings of the Annual Water Resources Planning
and Management Conference},
year = 1999,
address = {Reston, USA},
publisher = {ASCE},
anote = {CD-ROM}
}
@inproceedings{EbeKen1995:pso,
author = { Eberhart, Russell C. and J. Kennedy },
booktitle = {Proceedings of the Sixth International Symposium on
Micro Machine and Human Science},
title = {A New Optimizer Using Particle Swarm Theory},
year = 1995,
pages = {39--43}
}
@inproceedings{EggHutHooLey2015,
year = 2015,
publisher = {{AAAI} Press},
booktitle = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
editor = {Blai Bonet and Sven Koenig},
author = { Katharina Eggensperger and Frank Hutter and Holger H. Hoos and Kevin Leyton-Brown },
title = {Efficient Benchmarking of Hyperparameter Optimizers via
Surrogates},
pages = {1114--1120},
doi = {10.1609/aaai.v29i1.9375}
}
@incollection{Ehm2016,
author = {Werner Ehm},
title = {Reproducibility from the perspective of meta-analysis},
editor = {Harald Atmanspacher and Sabine Maasen},
booktitle = {Reproducibility -- Principles, problems, practices and prospects},
publisher = {Wiley},
year = 2016,
pages = {141--168}
}
@incollection{EhrGan08hybrid,
series = {Studies in Computational Intelligence},
volume = 114,
year = 2008,
address = { Berlin, Germany},
publisher = {Springer},
editor = { Christian Blum and Mar{\'i}a J. Blesa and Andrea Roli and M. Sampels },
booktitle = {Hybrid Metaheuristics: An emergent approach for optimization},
author = { Matthias Ehrgott and Xavier Gandibleux },
title = {Hybrid Metaheuristics for Multi-objective
Combinatorial Optimization},
doi = {10.1007/978-3-540-78295-7_8},
abstract = {Many real-world optimization problems can be
modelled as combinatorial optimization
problems. Often, these problems are characterized by
their large size and the presence of multiple,
conflicting objectives. Despite progress in solving
multi-objective combinatorial optimization problems
exactly, the large size often means that heuristics
are required for their solution in acceptable time.
Since the middle of the nineties the trend is
towards heuristics that ``pick and choose'' elements
from several of the established metaheuristic
schemes. Such hybrid approximation techniques may
even combine exact and heuristic approaches. In this
chapter we give an overview over approximation
methods in multi-objective combinatorial
optimization. We briefly summarize ``classical''
metaheuristics and focus on recent approaches, where
metaheuristics are hybridized and/or combined with
exact methods. },
pages = {221--259}
}
@book{Ehrgott00:multicriteria,
author = { Matthias Ehrgott },
title = {Multicriteria Optimization},
publisher = {Springer},
address = { Berlin, Germany},
year = 2000,
volume = 491,
series = {Lecture Notes in Economics and Mathematical Systems}
}
@book{Ehrgott2005multicrit,
author = { Matthias Ehrgott },
title = {Multicriteria Optimization},
publisher = {Springer},
address = { Berlin, Germany},
year = 2005,
edition = {2nd},
doi = {10.1007/3-540-27659-9}
}
@incollection{EibHorKow2006rl,
title = {Reinforcement learning for online control of evolutionary
algorithms},
author = { Agoston E. Eiben and Horvath, Mark and Kowalczyk, Wojtek and Schut, Martijn C.},
booktitle = {International Workshop on Engineering Self-Organising
Applications},
pages = {151--160},
year = 2006,
publisher = {Springer}
}
@inproceedings{EibJel2002critical,
year = 2002,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2002 Congress on Evolutionary Computation (CEC'02)},
key = {IEEE CEC},
author = { Agoston E. Eiben and M. Jelasity},
title = {A critical note on experimental research methodology in {EC}},
annote = {Discusses reproducibility, generalizability and separation
between training (for tuning and experimentation) and testing
instances (for comparisons).},
doi = {10.1109/cec.2002.1006991},
pages = {582--587}
}
@incollection{EibMichSChSmi07,
address = { Berlin, Germany},
publisher = {Springer},
year = 2007,
booktitle = {Parameter Setting in Evolutionary Algorithms},
editor = {F. Lobo and C. F. Lima and Zbigniew Michalewicz },
author = { Agoston E. Eiben and Zbigniew Michalewicz and Marc Schoenauer and James E. Smith},
title = {Parameter Control in Evolutionary Algorithms},
pages = {19--46}
}
@book{EibSmi2003,
title = {Introduction to Evolutionary Computing},
author = { Agoston E. Eiben and Smith, James E. },
publisher = {Springer},
year = 2003,
isbn = 3540401849
}
@book{EibSmi2007,
author = { Agoston E. Eiben and Smith, James E. },
title = {Introduction to Evolutionary Computing},
publisher = {Springer},
year = 2007,
series = {Natural Computing Series},
edition = {2nd}
}
@incollection{Ela2011:gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2011,
editor = {Natalio Krasnogor and Pier Luca Lanzi},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011},
author = {Mohammed El-Abd},
title = {Opposition-based Artificial Bee Colony Algorithm},
pages = {109--116}
}
@techreport{ElsKhaTor2017financial,
author = {Elsokkary, Nada and Khan, Faisal Shah and La Torre, Davide
and Humble, Travis S. and Gottlieb, Joel},
title = {Financial Portfolio Management using {D-Wave}'s Quantum
Optimizer: The Case of {Abu} {Dhabi} Securities Exchange},
institution = {Oak Ridge National Lab, Oak Ridge, TN, USA},
year = 2017,
url = {https://www.osti.gov/biblio/1423041}
}
@inproceedings{EmmDeuKli2011ehvi,
year = 2011,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2011 Congress on Evolutionary Computation (CEC 2011)},
key = {IEEE CEC},
author = { Emmerich, Michael T. M. and Andr{\'{e}} H. Deutz and J. W. Klinkenberg},
title = {Hypervolume-based expected improvement: Monotonicity
properties and exact computation},
pages = {2147--2154},
doi = {10.1109/CEC.2011.5949880},
annote = {Proposed Expected Hypervolume Improvement (EHVI)}
}
@incollection{EmmFon2011emo,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2011},
address = {Berlin\slash Heidelberg},
series = {Lecture Notes in Computer Science},
volume = 6576,
year = 2011,
publisher = {Springer},
editor = { Takahashi, R. H. C. and Kalyanmoy Deb and Wanner, Elizabeth F. and Salvatore Greco },
title = {Computing Hypervolume Contributions in Low
Dimensions: Asymptotically Optimal Algorithm and
Complexity Results},
doi = {10.1007/978-3-642-19893-9_9},
abstract = {Given a finite set $Y \subset \mathbb{R}^d$ of n mutually
non-dominated vectors in $d \geq 2 dimensions$, the
hypervolume contribution of a point $y \in Y$ is the
difference between the hypervolume indicator of $Y$
and the hypervolume indicator of $Y \setminus \{y\}$. In
multi-objective metaheuristics, hypervolume
contributions are computed in several selection and
bounded-size archiving procedures. This paper
presents new results on the (time) complexity of
computing all hypervolume contributions. It is
proved that for $d = 2$ and 3 the problem has time
complexity $\Theta(n logn)$, and, for $d > 3$,
the time complexity is bounded below by $\Omega(n
logn)$. Moreover, complexity bounds are derived for
computing a single hypervolume contribution. A
dimension sweep algorithm with time complexity
$\mathcal{O} (n logn)$ and space
complexity $\mathcal{O}(n)$ is
proposed for computing all hypervolume contributions
in three dimensions. It improves the complexity of
the best known algorithm for $d = 3$ by a factor of
$\sqrt{n}$ . Theoretical results
are complemented by performance tests on randomly
generated test-problems.},
author = { Emmerich, Michael T. M. and Carlos M. Fonseca },
pages = {121--135}
}
@incollection{EppDeSStu2011:adt,
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Ronen I. Brafman and F. Roberts and Alexis Tsouki{\`a}s },
volume = 6992,
series = {Lecture Notes in Artificial Intelligence},
year = 2011,
booktitle = {Algorithmic Decision Theory, Third International Conference,
{ADT} 2011},
author = { Stefan Eppe and Yves {De Smet} and Thomas St{\"u}tzle },
title = {A bi-objective optimization model to eliciting decision maker's preferences for the {PROMETHEE II} method},
pages = {56--66}
}
@inproceedings{EppLopStuDeS2011:cec,
year = 2011,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2011 Congress on Evolutionary Computation (CEC 2011)},
key = {IEEE CEC},
author = { Stefan Eppe and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle and Yves {De Smet} },
title = {An Experimental Study of Preference Model Integration into
Multi-Objective Optimization Heuristics},
pages = {2751--2758},
doi = {10.1109/CEC.2011.5949963}
}
@inproceedings{EriPeaGar2019scalable,
year = 2019,
editor = {Hanna M. Wallach and Hugo Larochelle and Alina Beygelzimer
and Florence d'Alch{\'{e}}{-}Buc and Emily B. Fox and Roman
Garnett},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS 32)},
title = {Scalable Global Optimization via Local {Bayesian}
Optimization},
author = {Eriksson, David and Pearce, Michael and Gardner, Jacob and
Turner, Ryan D. and Poloczek, Matthias},
pages = {5496--5507},
epub = {http://papers.nips.cc/paper/8788-scalable-global-optimization-via-local-bayesian-optimization.pdf},
annote = {Arxiv preprint arXiv: \url{https://arxiv.org/abs/1910.01739}}
}
@inproceedings{Ertin01,
author = { Emre Ertin and Anthony N. Dean and Mathew L. Moore and Kevin L. Priddy },
title = {Dynamic Optimization for Optimal Control of Water
Distribution Systems},
booktitle = {Applications and Science of Computational
Intelligence {IV}, Proceedings of {SPIE}},
year = 2001,
month = mar,
pages = {142--149},
editor = { Kevin L. Priddy and Paul E. Keller and Peter J. Angeline },
volume = 4390
}
@inproceedings{Esat94,
author = { V. Esat and M. Hall },
title = {Water resources system optimization using genetic algorithms},
booktitle = {Hydroinformatics'94},
pages = {225--231},
year = 1994,
editor = { A. Verwey and A. Minns and V. Babovic and C. Maksimovi{\'c} },
address = {Balkema, Rotterdam, The Netherlands},
note = {}
}
@incollection{EshSch1992,
isbn = {1-55860-263-1},
year = 1993,
publisher = {Morgan Kaufmann Publishers},
booktitle = {Foundations of Genetic Algorithms (FOGA)},
editor = { Darrell Whitley },
author = { Larry J. Eshelman and J. David Schaffer },
title = {Real-Coded Genetic Algorithms and Interval-Schemata},
pages = {187--202}
}
@inproceedings{Eshelman89crossoverbiases,
publisher = {Morgan Kaufmann Publishers, San Mateo, CA},
editor = { J. David Schaffer },
year = 1989,
booktitle = {Proceedings of the Third International Conference on Genetic Algorithms (ICGA'89)},
author = { Larry J. Eshelman and A. Caruana and J. David Schaffer },
title = {Biases in the Crossover Landscape},
pages = {86--91}
}
@incollection{EveFieSin2002full,
title = {Full Elite Sets for Multi-objective Optimisation},
author = { Everson, Richard M. and Jonathan E. Fieldsend and Singh, Sameer},
booktitle = {Adaptive Computing in Design and Manufacture {V}},
publisher = {Springer},
address = { London, UK },
year = 2002,
pages = {343--354},
doi = {10.1007/978-0-85729-345-9_29},
annote = {Extended version published as \cite{FieEveSing2003tec}}
}
@incollection{EycSno02,
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and Gianni A. {Di Caro} and M. Sampels },
volume = 2463,
series = {Lecture Notes in Computer Science},
year = 2002,
booktitle = {Ant Algorithms, Third International Workshop, ANTS
2002},
author = {C. J. Eyckelhof and M. Snoek},
title = {Ant Systems for a Dynamic {TSP}: {Ants} Caught in a
Traffic Jam},
pages = {88--99}
}
@incollection{FalLinHut2015spysmac,
address = { Cham, Switzerland},
publisher = {Springer},
editor = {Heule, Marijn and Weaver, Sean},
volume = 9340,
series = {Lecture Notes in Computer Science},
year = 2015,
booktitle = {Theory and Applications of Satisfiability Testing -- {SAT}
2015},
title = {{SpySMAC}: Automated configuration and performance analysis
of {SAT} solvers},
author = {Falkner, Stefan and Marius Thomas Lindauer and Frank Hutter },
doi = {10.1007/978-3-319-24318-4_16},
pages = {215--222}
}
@incollection{FalZapGar2021gecco,
location = {Lille, France},
address = { New York, NY},
publisher = {ACM Press},
year = 2021,
editor = { Chicano, Francisco and Krzysztof Krawiec },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021},
author = { Falc{\'{o}}n-Cardona, Jes{\'{u}}s Guillermo and Zapotecas-Mart{\'{i}}nez, Sa{\'{u}}l and Abel
Garc{\'{i}}a-N{\'{a}}jera},
title = {Pareto compliance from a practical point of view},
doi = {10.1145/3449639.3459276},
pages = {395--402}
}
@inproceedings{FarAma2002nafips,
publisher = {IEEE Service Center},
month = jun,
address = {Piscataway, New Jersey},
year = 2002,
booktitle = {Proceedings of the NAFIPS-FLINT International
Conference'2002},
key = {NAFIPS},
author = {M. Farina and P. Amato},
title = {On the Optimal Solution Definition for Many-criteria
Optimization Problems},
pages = {233--238},
doi = {10.1109/nafips.2002.1018061},
annote = {First to mention exponential number of nondominated solutions
with respect to many objectives?}
}
@incollection{FavMorPel09:sls,
volume = 5752,
series = {Lecture Notes in Computer Science},
year = 2009,
address = { Heidelberg, Germany},
publisher = {Springer},
booktitle = {Engineering Stochastic Local Search
Algorithms. Designing, Implementing and Analyzing
Effective Heuristics. SLS~2009},
editor = { Thomas St{\"u}tzle and Mauro Birattari and Holger H. Hoos },
author = { D. Favaretto and E. Moretti and Paola Pellegrini },
title = {On the explorative behavior of {\MaxMinAntSystem}},
pages = {115--119}
}
@inproceedings{FawHelHooKar2011icaps,
year = 2011,
booktitle = {Proceedings of ICAPS-PAL11},
editor = {Karpas, Erez and Jim{\'e}nez Celorrio, Sergio and Kambhampati, Subbarao},
author = { Chris Fawcett and Malte Helmert and Holger H. Hoos and Erez Karpas
and Gabriele R\"{o}ger and Jendrik Seipp},
title = {{FD-Autotune}: Domain-Specific Configuration using
Fast-Downward}
}
@inproceedings{FawHoos2013mic,
year = 2013,
booktitle = {Proceedings of MIC 2013, the 10th Metaheuristics
International Conference},
key = {MIC},
author = { Chris Fawcett and Holger H. Hoos },
title = {Analysing Differences between Algorithm
Configurations through Ablation},
pages = {123--132},
epub = {http://www.cs.ubc.ca/~hoos/Publ/FawHoo13.pdf}
}
@incollection{FerAlvDiaIglEna2014ants,
volume = 8667,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Marco Dorigo and others},
year = 2014,
booktitle = {Swarm Intelligence, 9th International Conference, ANTS 2014},
author = { Fern\'{a}ndez, Silvino and \'{A}lvarez, Segundo and Diego D{\'i}az and Miguel Iglesias and Borja Ena},
title = {Scheduling a Galvanizing Line by Ant Colony Optimization},
doi = {10.1007/978-3-319-09952-1_13},
pages = {146--157}
}
@incollection{FerAlvMalValDia2015gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2015,
editor = { Jim{\'e}nez Laredo, Juan Luis and Sara Silva and Anna I. Esparcia{-}Alc{\'{a}}zar },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2015},
author = { Fern\'{a}ndez, Silvino and \'{A}lvarez, Segundo and Malatsetxebarria, Eneko and Valledor, Pablo and Diego D{\'i}az },
title = {Performance Comparison of Ant Colony Algorithms for the
Scheduling of Steel Production Lines},
doi = {10.1145/2739482.2764658},
keywords = {irace}
}
@incollection{FerFonGas2007:gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2007,
editor = {Dirk Thierens and others},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007},
author = { Jos{\'e} C. Ferreira and Carlos M. Fonseca and Ant{\'o}nio Gaspar{-}Cunha },
title = {Methodology to select solutions from the {Pareto}-optimal set: a comparative study},
pages = {789--796}
}
@incollection{FerPudHat1994,
doi = {10.1016/b978-0-444-81892-8.50040-7},
year = 1994,
pages = {403--413},
author = {F. J. Ferri and P. Pudil and M. Hatef and J. Kittler},
title = {Comparative study of techniques for large-scale feature
selection},
editor = {Edzard S. Gelsema and Laveen S. Kanal},
series = {Machine Intelligence and Pattern Recognition},
publisher = {North-Holland},
volume = 16,
booktitle = {Pattern Recognition in Practice IV},
abstract = {The combinatorial search problem arising in feature selection
in high dimensional spaces is considered. Recently developed
techniques based on the classical sequential methods and the
(l, r) search called Floating search algorithms are compared
against the Genetic approach to feature subset search. Both
approaches have been designed with the view to give a good
compromise between efficiency and effectiveness for large
problems. The purpose of this paper is to investigate the
applicability of these techniques to high dimensional
problems of feature selection. The aim is to establish
whether the properties inferred for these techniques from
medium scale experiments involving up to a few tens of
dimensions extend to dimensionalities of one order of
magnitude higher. Further, relative merits of these
techniques vis-a-vis such high dimensional problems are
explored and the possibility of exploiting the best aspects
of these methods to create a composite feature selection
procedure with superior properties is considered.},
annote = {Describes sequential forward / backward selection}
}
@incollection{FerValDia2016gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2016,
editor = { Tobias Friedrich and Frank Neumann and Andrew M. Sutton },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2016},
author = { Fern\'{a}ndez, Silvino and Valledor, Pablo and Diego D{\'i}az and Malatsetxebarria, Eneko and Iglesias, Miguel},
title = {Criticality of Response Time in the usage of Metaheuristics
in Industry},
pages = {937--940}
}
@incollection{FeuHut2019hpo,
doi = {10.1007/978-3-030-05318-5},
epub = {http://automl.org/book},
booktitle = {Automated Machine Learning},
publisher = {Springer},
year = 2019,
editor = { Frank Hutter and Kotthoff, Lars and Joaquin Vanschoren },
author = { Matthias Feurer and Frank Hutter },
title = {Hyperparameter Optimization},
pages = {3--33}
}
@inproceedings{FeuKleEggSprBluHut2015autosklearn,
url = {http://papers.nips.cc/book/advances-in-neural-information-processing-systems-28-2015},
year = 2015,
booktitle = {Advances in Neural Information Processing Systems (NIPS
28)},
editor = {Corinna Cortes and Neil D. Lawrence and Daniel D. Lee and
Masashi Sugiyama and Roman Garnett},
author = { Matthias Feurer and Klein, Aaron and Katharina Eggensperger and Springenberg, Jost and Blum, Manuel and Frank Hutter },
title = {Efficient and robust automated machine learning},
pages = {2962--2970},
epub = {http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf}
}
@incollection{FeuKleEggSprBluHut2019autosklearn,
doi = {10.1007/978-3-030-05318-5},
epub = {http://automl.org/book},
booktitle = {Automated Machine Learning},
publisher = {Springer},
year = 2019,
editor = { Frank Hutter and Kotthoff, Lars and Joaquin Vanschoren },
author = { Matthias Feurer and Klein, Aaron and Katharina Eggensperger and Springenberg, Jost and Blum, Manuel and Frank Hutter },
title = {Auto-sklearn: Efficient and Robust Automated Machine
Learning},
pages = {113--134},
abstract = {The success of machine learning in a broad range of
applications has led to an ever-growing demand for machine
learning systems that can be used off the shelf by
non-experts. To be effective in practice, such systems need
to automatically choose a good algorithm and feature
preprocessing steps for a new dataset at hand, and also set
their respective hyperparameters. Recent work has started to
tackle this automated machine learning (AutoML) problem with
the help of efficient Bayesian optimization methods. Building
on this, we introduce a robust new AutoML system based on the
Python machine learning package scikit-learn (using 15
classifiers, 14 feature preprocessing methods, and 4 data
preprocessing methods, giving rise to a structured hypothesis
space with 110 hyperparameters). This system, which we dub
Auto-sklearn, improves on existing AutoML methods by
automatically taking into account past performance on similar
datasets, and by constructing ensembles from the models
evaluated during the optimization. Our system won six out of
ten phases of the first ChaLearn AutoML challenge, and our
comprehensive analysis on over 100 diverse datasets shows
that it substantially outperforms the previous state of the
art in AutoML. We also demonstrate the performance gains due
to each of our contributions and derive insights into the
effectiveness of the individual components of Auto-sklearn.}
}
@incollection{FiaRosScho2010comp,
volume = 6238,
year = 2010,
address = { Heidelberg, Germany},
publisher = {Springer},
editor = {Schaefer, Robert and Cotta, Carlos and Kolodziej,
Joanna and G{\"u}nther Rudolph },
series = {Lecture Notes in Computer Science},
booktitle = {Parallel Problem Solving from Nature, PPSN XI},
title = {Comparison-based adaptive strategy selection with bandits in
differential evolution},
author = { {\'A}lvaro Fialho and Ros, Raymond and Marc Schoenauer and Mich{\`e}le Sebag },
pages = {194--203}
}
@incollection{FiaSchoSeb2010fauc,
address = { New York, NY},
publisher = {ACM Press},
year = 2010,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2010},
editor = {Martin Pelikan and J{\"u}rgen Branke },
title = {Fitness-{AUC} bandit adaptive strategy selection vs.\ the
probability matching one within differential evolution: an
empirical comparison on the {BBOB-2010} noiseless testbed},
author = { {\'A}lvaro Fialho and Marc Schoenauer and Mich{\`e}le Sebag },
pages = {1535--1542}
}
@incollection{FiaSchoSeb2010toward,
address = { New York, NY},
publisher = {ACM Press},
year = 2010,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2010},
editor = {Martin Pelikan and J{\"u}rgen Branke },
title = {Toward comparison-based adaptive operator selection},
author = { {\'A}lvaro Fialho and Marc Schoenauer and Mich{\`e}le Sebag },
pages = {767--774},
annote = {Proposed F-AUC and F-SR}
}
@phdthesis{Fialho2010PhD,
title = {Adaptive operator selection for optimization},
author = { {\'A}lvaro Fialho },
year = 2010,
school = {Universit{\'e} Paris Sud-Paris XI}
}
@incollection{Fie2017gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2017,
editor = { Peter A. N. Bosman },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017},
author = { Jonathan E. Fieldsend },
title = {University staff teaching allocation: formulating and
optimising a many-objective problem},
pages = {1097--1104},
doi = {10.1145/3071178.3071230},
annote = {Example of NSGA-III deteriorating.}
}
@incollection{FieEve2013visualising,
isbn = {978-3-642-37139-4},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2013},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 7811,
year = 2013,
publisher = {Springer},
editor = { Robin C. Purshouse and Peter J. Fleming and Carlos M. Fonseca and Salvatore Greco and Jane Shaw},
title = {Visualising high-dimensional {Pareto} relationships in
two-dimensional scatterplots},
author = { Jonathan E. Fieldsend and Everson, Richard M. },
pages = {558--572},
doi = {10.1007/978-3-642-37140-0_42}
}
@incollection{Fieldsend2020data,
epub = {https://dl.acm.org/citation.cfm?id=3377930},
location = {Canc{\'u}n, Mexico},
doi = {10.1145/3377930},
isbn = {978-1-4503-7128-5},
address = { New York, NY},
publisher = {ACM Press},
year = 2020,
editor = { Carlos A. {Coello Coello} },
title = {Data structures for non-dominated sets: implementations and
empirical assessment of two decades of advances},
author = { Jonathan E. Fieldsend },
booktitle = {Proceedings of the 2020 Genetic and Evolutionary Computation
Conference},
pages = {489--497},
annote = {unbounded archives}
}
@incollection{FinVos2002,
year = 2002,
publisher = {Kluwer Academic Publishers, Boston, MA},
editor = { Stefan Vo{\ss} and David L. Woodruff },
booktitle = {Optimization Software Class Libraries},
author = {Andreas Fink and Stefan Vo{\ss} },
title = {{HotFrame}: A Heuristic Optimization Framework},
pages = {81--154}
}
@incollection{FisDhaJou2015lion,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 8994,
booktitle = {Learning and Intelligent Optimization, 9th International Conference, LION 9},
publisher = {Springer},
year = 2015,
editor = {Clarisse Dhaenens and Laetitia Jourdan and Marie-El{\'e}onore Marmion },
author = {Benjamin Fisset and Clarisse Dhaenens and Laetitia Jourdan },
title = {{MO-Mine$_\text{clust}$}: A Framework for Multi-Objective
Clustering},
pages = {293--305},
keywords = {irace}
}
@incollection{FlePurLyg2005,
address = {Berlin\slash Heidelberg},
publisher = {Springer},
volume = 3410,
series = {Lecture Notes in Computer Science},
editor = { Carlos A. {Coello Coello} and Hern{\'a}ndez Aguirre, Arturo and Eckart Zitzler },
year = 2005,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2005},
title = {Many-objective optimization: An engineering design
perspective},
author = { Peter J. Fleming and Robin C. Purshouse and Lygoe, Robert J.},
pages = {14--32}
}
@incollection{PurJalFle2011pref,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2011},
address = {Berlin\slash Heidelberg},
series = {Lecture Notes in Computer Science},
volume = 6576,
year = 2011,
publisher = {Springer},
editor = { Takahashi, R. H. C. and Kalyanmoy Deb and Wanner, Elizabeth F. and Salvatore Greco },
title = {Preference-Driven Co-Evolutionary Algorithms Show Promise for
Many-Objective optimisation},
author = { Robin C. Purshouse and Jalb{\u{a}}, Cezar and Peter J. Fleming },
pages = {136--150}
}
@incollection{Fleischer2003emo,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 2632,
series = {Lecture Notes in Computer Science},
editor = { Carlos M. Fonseca and Peter J. Fleming and Eckart Zitzler and Kalyanmoy Deb and Lothar Thiele },
year = 2003,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2003},
author = {M. Fleischer},
title = {The Measure of {Pareto} Optima. Applications to
Multi-objective Metaheuristics},
pages = {519--533}
}
@book{Fletcher1987,
author = {Fletcher, R.},
publisher = {John Wiley \& Sons},
title = {Practical methods of optimization},
year = 1987,
address = { New York, NY},
annote = {BFGS}
}
@incollection{FloMon1994automatic,
address = {Cambridge, MA},
year = 1994,
publisher = {MIT Press},
booktitle = {Proceedings of the third international conference on
Simulation of adaptive behavior: From Animals to Animats 3},
editor = {Cliff, D. and Husbands, P. and Meyer, J.-A. and Wilson, S.},
title = {Automatic creation of an autonomous agent: Genetic evolution
of a neural network driven robot},
author = {Floreano, Dario and Mondada, Francesco},
annote = {LIS-CONF-1994-003},
pages = {421--430}
}
@incollection{FocLabLod2002,
publisher = {Kluwer Academic Publishers, Norwell, MA},
year = 2002,
editor = { Fred Glover and Gary A. Kochenberger},
booktitle = {Handbook of Metaheuristics},
author = { Filippo Focacci and Fran{\c{c}}ois Laburthe and Andrea Lodi },
title = {Local Search and Constraint Programming},
pages = {369--403}
}
@book{FogOweWal1966,
title = {Artificial Intelligence Through Simulated Evolution},
author = { David B. Fogel and Owens, Alvin J. and Walsh, Michael J. },
year = 1966,
publisher = {John Wiley \& Sons}
}
@book{Fogel95:EvolutionaryComputation,
author = { David B. Fogel },
title = {Evolutionary Computation. Toward a New Philosophy of
Machine Intelligence},
journal = {Evolutionary Computation},
year = 1995,
publisher = {IEEE Press}
}
@inproceedings{FonFle1993:moga,
isbn = {1-55860-299-2},
year = 1993,
publisher = {Morgan Kaufmann Publishers},
booktitle = {Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA'93)},
editor = {Stephanie Forrest},
author = { Carlos M. Fonseca and Peter J. Fleming },
title = {Genetic Algorithms for Multiobjective Optimization:
Formulation, Discussion and Generalization},
pages = {416--423},
epub = {http://eden.dei.uc.pt/~cmfonsec/fonseca-ga93-reprint.pdf},
annote = {Proposes MOGA and P-MOGA}
}
@incollection{FonFle1996:ppsn,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 1141,
editor = {H.-M. Voigt and others},
aeditor = {H.-M. Voigt and W. Ebeling and Rechenberg, Ingo and Hans-Paul Schwefel },
year = 1996,
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {IV}},
author = { Carlos M. Fonseca and Peter J. Fleming },
title = {On the Performance Assessment and Comparison of
Stochastic Multiobjective Optimizers},
pages = {584--593}
}
@incollection{FonFon2012,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 7401,
booktitle = {Artificial Evolution: 10th International Conference, Evolution Artificielle, EA, 2011},
publisher = {Springer},
year = 2012,
editor = { Jin-Kao Hao and Legrand, Pierrick and Collet, Pierre and
Monmarch{\'e}, Nicolas and Lutton, Evelyne and Schoenauer,
Marc},
author = { Viviane {Grunert da Fonseca} and Carlos M. Fonseca },
title = {The Relationship between the Covered Fraction, Completeness
and Hypervolume Indicators},
pages = {25--36}
}
@incollection{FonGruPaq2005:emo,
address = {Berlin\slash Heidelberg},
publisher = {Springer},
volume = 3410,
series = {Lecture Notes in Computer Science},
editor = { Carlos A. {Coello Coello} and Hern{\'a}ndez Aguirre, Arturo and Eckart Zitzler },
year = 2005,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2005},
author = { Carlos M. Fonseca and Viviane {Grunert da Fonseca} and Lu{\'i}s Paquete },
title = {Exploring the Performance of Stochastic Multiobjective
Optimisers with the Second-Order Attainment Function},
pages = {250--264},
doi = {10.1007/978-3-540-31880-4_18},
abstract = {The attainment function has been proposed as a measure of the
statistical performance of stochastic multiobjective
optimisers which encompasses both the quality of individual
non-dominated solutions in objective space and their spread
along the trade-off surface. It has also been related to
results from random closed-set theory, and cast as a
mean-like, first-order moment measure of the outcomes of
multiobjective optimisers. In this work, the use of more
informative, second-order moment measures for the evaluation
and comparison of multiobjective optimiser performance is
explored experimentally, with emphasis on the
interpretability of the results.}
}
@incollection{FonGueLopPaq2011emo,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2011},
address = {Berlin\slash Heidelberg},
series = {Lecture Notes in Computer Science},
volume = 6576,
year = 2011,
publisher = {Springer},
editor = { Takahashi, R. H. C. and Kalyanmoy Deb and Wanner, Elizabeth F. and Salvatore Greco },
author = { Carlos M. Fonseca and Andreia P. Guerreiro and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete },
title = {On the Computation of the Empirical Attainment Function},
doi = {10.1007/978-3-642-19893-9_8},
pages = {106--120},
abstract = {The attainment function provides a description of the
location of the distribution of a random non-dominated point
set. This function can be estimated from experimental data
via its empirical counterpart, the empirical attainment
function (EAF). However, computation of the EAF in more than
two dimensions is a non-trivial task. In this article, the
problem of computing the empirical attainment function is
formalised, and upper and lower bounds on the corresponding
number of output points are presented. In addition, efficient
algorithms for the two and three-dimensional cases are
proposed, and their time complexities are related to lower
bounds derived for each case.}
}
@inproceedings{FonPaqLop06:hypervolume,
address = {Piscataway, NJ},
publisher = {IEEE Press},
month = jul,
year = 2006,
booktitle = {Proceedings of the 2006 Congress on Evolutionary Computation (CEC 2006)},
key = {IEEE CEC},
author = { Carlos M. Fonseca and Lu{\'i}s Paquete and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {An improved dimension-\hspace{0pt}sweep
algorithm for the hypervolume indicator},
pages = {1157--1163},
doi = {10.1109/CEC.2006.1688440},
abstract = {This paper presents a recursive, dimension-sweep
algorithm for computing the hypervolume indicator of
the quality of a set of $n$ non-dominated points in
$d>2$ dimensions. It improves upon the existing HSO
(Hypervolume by Slicing Objectives) algorithm by
pruning the recursion tree to avoid repeated
dominance checks and the recalculation of partial
hypervolumes. Additionally, it incorporates a recent
result for the three-dimensional special case. The
proposed algorithm achieves $O(n^{d-2} \log n)$ time
and linear space complexity in the worst-case, but
experimental results show that the pruning
techniques used may reduce the time complexity
exponent even further.}
}
@incollection{FonTag2020repro,
author = {Fonseca Cacho, Jorge Ram{\'o}n and Taghva, Kazem},
title = {The State of Reproducible Research in Computer Science},
doi = {10.1007/978-3-030-43020-7_68},
series = {Advances in Intelligent Systems and Computing},
booktitle = {17th {International} {Conference} on {Information}
{Technology}-{New} {Generations} ({ITNG} 2020)},
abstract = {Reproducible research is the cornerstone of cumulative
science and yet is one of the most serious crisis that we
face today in all fields. This paper aims to describe the
ongoing reproducible research crisis along with
counter-arguments of whether it really is a crisis, suggest
solutions to problems limiting reproducible research along
with the tools to implement such solutions by covering the
latest publications involving reproducible research.},
language = {en},
publisher = {Springer International Publishing},
editor = {Latifi, Shahram},
year = 2020,
keywords = {Docker, Improving transparency, OCR, Open science,
Replicability, Reproducibility},
pages = {519--524}
}
@incollection{FosBickHardKok2007,
address = { New York, NY},
publisher = {ACM Press},
year = 2007,
editor = {Dirk Thierens and others},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007},
author = {Manuel F\"{o}rster and Bettina Bickel and Bernd
Hardung and Gabriella K\'{o}kai},
title = {Self-adaptive ant colony optimisation applied to
function allocation in vehicle networks},
pages = {1991--1998}
}
@incollection{FosHugObr2020,
epub = {https://dl.acm.org/citation.cfm?id=3377930},
location = {Canc{\'u}n, Mexico},
doi = {10.1145/3377930},
isbn = {978-1-4503-7128-5},
address = { New York, NY},
publisher = {ACM Press},
year = 2020,
editor = { Carlos A. {Coello Coello} },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2020},
title = {Do sophisticated evolutionary algorithms perform better than simple ones?},
author = {Foster, Michael and Hughes, Matthew and O'Brien, George and Oliveto, Pietro S. and Pyle, James and Dirk Sudholt and Williams, James},
pages = {184--192}
}
@book{FouGayKer2002,
author = {Robert Fourer and David M. Gay and Brian W. Kernighan},
title = {{AMPL}: A Modeling Language for Mathematical Programming},
publisher = {Duxbury},
year = 2002,
edition = {2nd}
}
@inproceedings{Fox1992uniting,
author = { Bennett L. Fox },
title = {Uniting probabilistic methods for optimization},
booktitle = {Proceedings of the 24th conference on Winter simulation},
pages = {500--505},
year = 1992,
organization = {ACM}
}
@incollection{Fox1995simulated,
author = { Bennett L. Fox },
title = {Simulated annealing: folklore, facts, and directions},
booktitle = {Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing},
pages = {17--48},
year = 1995,
publisher = {Springer}
}
@phdthesis{Fra2021:phd,
author = { Alberto Franzin },
title = {Empirical Analysis of Stochastic Local Search Behaviour: Connecting Structure, Components and Landscape},
school = {IRIDIA, {\'E}cole polytechnique, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2021
}
@phdthesis{Fra95:phd,
author = { C. B. Fraser },
title = {Subsequences and Supersequences of Strings},
school = {University of Glasgow},
year = 1995
}
@inproceedings{FraGyoNad2020,
url = {https://bnaic.liacs.leidenuniv.nl/wordpress/wp-content/uploads/bnaic2020proceedings.pdf},
year = 2020,
editor = {Cao, Lu and Kosters, Walter and Lijffijt, Jefrey},
booktitle = {Proceedings of the 32nd Benelux Conference on Artificial Intelligence,
BNAIC 2020, Leiden, The Netherlands, 19-20 November 2020},
author = { Alberto Franzin and Gyory, Rapha\"el and Nad\'e, Jean-Charles and
Aubert, Guillaume and Klenkle, Georges and Hughes Bersini },
title = {Phil\'{e}as: Anomaly Detection for {IoT} Monitoring},
pages = {56--70}
}
@incollection{FraHam2016bor,
address = { London, UK },
publisher = {Palgrave Macmillan},
year = 2016,
editor = {Kunc, M. and Malpass, J. and White, L.},
booktitle = {Behavioral Operational Research},
author = {Franco, L Alberto and H{\"a}m{\"a}l{\"a}inen, Raimo P. },
title = {Engaging with Behavioral Operational Research: On Methods,
Actors and Praxis},
pages = {3--25},
doi = {10.1057/978-1-137-53551-1_1}
}
@book{FraLeiRui2014,
title = {Manufacturing Scheduling Systems: An Integrated View on
Models, Methods, and Tools},
author = { Jose M. Frami{\~n}{\'a}n and Rainer Leisten and Rub{\'e}n Ruiz },
publisher = {Springer},
address = { New York, NY},
year = 2014
}
@incollection{FraStu2016:gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2016,
editor = { Tobias Friedrich and Frank Neumann and Andrew M. Sutton },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2016},
author = { Alberto Franzin and Thomas St{\"u}tzle },
title = {Exploration of Metaheuristics through Automatic Algorithm Configuration Techniques and Algorithmic Frameworks},
pages = {1341--1347}
}
@incollection{FraStu2017:EA,
address = { Heidelberg, Germany},
publisher = {Springer},
editor = {Lutton, Evelyne and Legrand, Pierrick and Parrend, Pierre and Nicolas Monmarch{\'e} and Marc Schoenauer },
volume = 10764,
series = {Lecture Notes in Computer Science},
year = 2017,
booktitle = {EA 2017: Artificial Evolution},
author = { Alberto Franzin and Thomas St{\"u}tzle },
title = {Comparison of Acceptance Criteria in Randomized Local Searches},
pages = {16--29}
}
@misc{FraStu2018-supp,
author = { Alberto Franzin and Thomas St{\"u}tzle },
title = {Revisiting Simulated Annealing: a Component-Based Analysis: {Supplementaty} Material},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2018-001}},
year = 2018
}
@inproceedings{FraStu2020:lmca,
editor = {Vlastelica, Marin and Song, Jialin and Ferber, Aaron and
Amos, Brandon and Martius, Georg and Dilkina, Bistra and Yue,
Yisong},
booktitle = {Learning Meets Combinatorial Algorithms Workshop at NeurIPS
2020},
year = 2020,
author = { Alberto Franzin and Thomas St{\"u}tzle },
title = {Towards transferring algorithm configurations across problems},
pages = {1--6}
}
@incollection{FraStu2020:tailor,
year = 2021,
volume = 12641,
address = { Cham, Switzerland},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
booktitle = {Trustworthy AI -- Integrating Learning, Optimization and
Reasoning. TAILOR 2020},
editor = {Fredrik Heintz and Michela Milano and O'Sullivan, Barry },
author = { Alberto Franzin and Thomas St{\"u}tzle },
title = {A causal framework for understanding optimisation algorithms},
pages = {140--145}
}
@misc{FraStu2021-supp,
author = { Alberto Franzin and Thomas St{\"u}tzle },
title = {A Landscape-based Analysis of Fixed Temperature and Simulated Annealing: {Supplementaty} Material},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2021-002}},
year = 2021
}
@inproceedings{FreFleGui2013nonparametric,
year = 2013,
publisher = {IEEE Press},
booktitle = {2013 IEEE International Conference on Systems, Man, and
Cybernetics},
key = {SMC},
author = {A. R. R. {Freitas} and Peter J. Fleming and Frederico
G. Guimar{\~{a}}es},
title = {A Non-parametric Harmony-Based Objective Reduction Method for
Many-Objective Optimization},
pages = {651--656},
doi = {10.1109/SMC.2013.116}
}
@incollection{FreMer1996icec,
address = {Piscataway, NJ},
publisher = {IEEE Press},
year = 1996,
editor = { Thomas B{\"a}ck and T. Fukuda and Zbigniew Michalewicz },
booktitle = {Proceedings of the 1996 IEEE International Conference on
Evolutionary Computation (ICEC'96)},
author = {B. Freisleben and P. Merz},
title = {A Genetic Local Search Algorithm for Solving
Symmetric and Asymmetric Traveling Salesman
Problems},
pages = {616--621}
}
@incollection{FriGobQuiWag2018ppsn,
volume = 11101,
year = 2018,
address = { Cham, Switzerland},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Anne Auger and Carlos M. Fonseca and Louren{\c c}o, N. and Penousal Machado and Lu{\'i}s Paquete and Darrell Whitley },
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XV}},
author = { Tobias Friedrich and G{\"o}bel, Andreas and Quinzan, Francesco and Markus Wagner },
title = {Heavy-Tailed Mutation Operators in Single-Objective
Combinatorial Optimization},
pages = {134--145},
abstract = {A core feature of evolutionary algorithms is their mutation
operator. Recently, much attention has been devoted to the
study of mutation operators with dynamic and non-uniform
mutation rates. Following up on this line of work, we propose
a new mutation operator and analyze its performance on the
(1+1) Evolutionary Algorithm (EA). Our analyses show that
this mutation operator competes with pre-existing ones, when
used by the (1+1)-EA on classes of problems for which
results on the other mutation operators are available. We
present a ``jump'' function for which the performance of the
(1+1)-EA using any static uniform mutation and any restart
strategy can be worse than the performance of the (1+1)-EA
using our mutation operator with no restarts. We show that
the (1+1)-EA using our mutation operator finds a
(1/3)-approximation ratio on any non-negative submodular
function in polynomial time. This performance matches that of
combinatorial local search algorithms specifically designed
to solve this problem.}
}
@incollection{FriKotKre2015gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2015,
editor = {Sara Silva and Anna I. Esparcia{-}Alc{\'{a}}zar },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015},
author = { Tobias Friedrich and K{\"o}tzing, Timo and Krejca, Martin S. and Andrew M. Sutton },
title = {Robustness of Ant Colony Optimization to Noise},
pages = {17--24},
numpages = 8,
doi = {10.1145/2739480.2754723},
keywords = {ant colony optimization, noisy fitness, run time analysis,
theory}
}
@incollection{FriKotWag2017:aaai,
publisher = {{AAAI} Press},
month = feb,
year = 2017,
editor = {Satinder P. Singh and Shaul Markovitch},
booktitle = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
author = { Tobias Friedrich and K{\"o}tzing, Timo and Markus Wagner },
title = {A Generic Bet-and-Run Strategy for Speeding Up Stochastic Local Search},
pages = {801--807}
}
@incollection{FriQuiWag2018mutation,
address = { New York, NY},
publisher = {ACM Press},
year = 2018,
editor = { Aguirre, Hern\'{a}n E. and Keiki Takadama},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018},
author = { Tobias Friedrich and Quinzan, Francesco and Markus Wagner },
title = {Escaping Large Deceptive Basins of Attraction with
Heavy-tailed Mutation Operators},
pages = {293--300},
numpages = {8},
doi = {10.1145/3205455.3205515},
acmid = {3205515},
keywords = {combinatorial optimization, heavy-tailed mutation,
single-objective optimization, experiments-motivated theory,
irace}
}
@inproceedings{Friendly1991stat,
title = {Statistical graphics for multivariate data},
author = {Friendly, Michael},
year = 1991,
booktitle = {SAS Conference Proceedings: SAS Users Group International 16
(SUGI 16)},
annote = {February 17-20, 1991, New Orleans, Louisiana, 297 papers}
}
@book{FudTir83,
author = {Fudenberg, D. and Tirole, J.},
year = 1983,
title = {Game Theory},
publisher = {MIT Press, Cambridge, MA}
}
@incollection{FujNan2021solving,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2021},
address = { New York, NY},
year = 2021,
publisher = {ACM Press},
editor = { Chicano, Francisco and Krzysztof Krawiec },
title = {Solving {QUBO} with {GPU} parallel {MOPSO}},
author = {Fujimoto, Noriyuki and Nanai, Kouki},
pages = {1788--1794}
}
@incollection{Fuk2004gecco,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 3103,
editor = { Kalyanmoy Deb and others},
year = 2004,
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2004, Part II},
title = {Evolving Local Search Heuristics for {SAT} Using
Genetic Programming},
author = { Fukunaga, Alex S. },
abstract = {Satisfiability testing ({SAT)} is a very active area
of research today, with numerous real-world
applications. We describe {CLASS2.0}, a genetic
programming system for semi-automatically designing
{SAT} local search heuristics. An empirical
comparison shows that that the heuristics generated
by our {GP} system outperform the state of the art
human-designed local search algorithms, as well as
previously proposed evolutionary approaches, with
respect to both runtime as well as search efficiency
(number of variable flips to solve a problem).},
pages = {483--494}
}
@book{FurLovLov2000stats,
author = {Nancy E. Furlong and Eugene A. Lovelace and Kristin
L. Lovelace},
title = {Research Methods and Statistics: An Integrated
Approach},
publisher = {Harcourt College Publishers},
year = 2000
}
@incollection{GaeCla04,
isbn = {1-932415-66-1},
year = 2005,
publisher = {CSREA Press},
booktitle = {Proceedings of the 2005 International Conference on Artificial Intelligence, ICAI 2005},
editor = {Hamid R. Arabnia and Rose Joshua},
author = {D. Gaertner and K. Clark},
title = {On Optimal Parameters for Ant Colony Optimization
Algorithms},
pages = {83--89}
}
@incollection{GagLeg2010emaa,
editor = { Thomas Bartz-Beielstein and Marco Chiarandini and Lu{\'i}s Paquete and Mike Preuss },
year = 2010,
address = {Berlin\slash Heidelberg},
publisher = {Springer},
booktitle = {Experimental Methods for the Analysis of
Optimization Algorithms},
author = { Matteo Gagliolo and Catherine Legrand},
title = {Algorithm Survival Analysis},
pages = {161--184},
doi = {10.1007/978-3-642-02538-9_7},
abstract = {Algorithm selection is typically based on models of
algorithm performance,learned during a separate
offline training sequence, which can be
prohibitively expensive. In recent work, we adopted
an online approach, in which models of the runtime
distributions of the available algorithms are
iteratively updated and used to guide the allocation
of computational resources, while solving a sequence
of problem instances. The models are estimated using
survival analysis techniques, which allow us to
reduce computation time, censoring the runtimes of
the slower algorithms. Here, we review the
statistical aspects of our online selection method,
discussing the bias induced in the runtime
distributions (RTD) models by the competition of
different algorithms on the same problem instances.}
}
@incollection{GamDor95:ml,
booktitle = {Proceedings of the Twelfth International Conference on Machine
Learning (ML-95)},
publisher = {Morgan Kaufmann Publishers, Palo Alto, CA},
year = 1995,
editor = {A. Prieditis and S. Russell},
author = { L. M. Gambardella and Marco Dorigo },
title = {Ant-{Q}: A Reinforcement Learning Approach to the
Traveling Salesman Problem},
pages = {252--260}
}
@incollection{GamDor96:icec,
address = {Piscataway, NJ},
publisher = {IEEE Press},
year = 1996,
editor = { Thomas B{\"a}ck and T. Fukuda and Zbigniew Michalewicz },
booktitle = {Proceedings of the 1996 IEEE International Conference on
Evolutionary Computation (ICEC'96)},
author = { L. M. Gambardella and Marco Dorigo },
title = {Solving Symmetric and Asymmetric {TSP}s by Ant
Colonies},
pages = {622--627},
anote = {IC.18}
}
@incollection{GamTaiAga99,
address = {London, UK},
year = 1999,
publisher = {McGraw Hill},
editor = { David Corne and Marco Dorigo and Fred Glover },
booktitle = {New Ideas in Optimization},
author = { L. M. Gambardella and {\'E}ric D. Taillard and G. Agazzi },
title = {{MACS-VRPTW}: A Multiple Ant Colony System for Vehicle
Routing Problems with Time Windows},
pages = {63--76}
}
@incollection{GanDelKin04:ants2004,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 3172,
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and L. M. Gambardella and Francesco Mondada and Thomas St{\"u}tzle and Mauro Birattari and Christian Blum },
year = 2004,
booktitle = {Ant Colony Optimization and Swarm Intelligence, 4th
International Workshop, ANTS 2004 },
author = { Xavier Gandibleux and X. Delorme and V. {T'Kindt} },
title = {An Ant Colony Optimisation Algorithm for the Set
Packing Problem},
pages = {49--60}
}
@incollection{GanMezFre1997,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Economics and Mathematical Systems},
volume = 455,
editor = {R. Caballero and Francisco Ruiz and R. Steuer},
year = 1997,
booktitle = {Advances in Multiple Objective and Goal Programming},
title = {A tabu search procedure to solve multiobjective
combinatorial optimization problem},
author = { Xavier Gandibleux and Mezdaoui, N. and Fr{\'e}ville, A.},
pages = {291--300}
}
@incollection{GanMorKat2003,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 2632,
series = {Lecture Notes in Computer Science},
editor = { Carlos M. Fonseca and Peter J. Fleming and Eckart Zitzler and Kalyanmoy Deb and Lothar Thiele },
year = 2003,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2003},
title = {Use of a genetic heritage for solving the assignment
problem with two objectives},
author = { Xavier Gandibleux and H. Morita and Katoh, N. },
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institution = {CSI, Universidade do Algarve},
keywords = {high-order EAF}
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address = { Heidelberg, Germany},
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title = {The Relationship between the Covered Fraction, Completeness
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paper.}
}
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address = { Heidelberg, Germany},
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aeditor = {S. Cagnoni and J. J. {Romero Cardalda} and D. W. Corne
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Intellectics Group, Darmstadt University of
Technology, Germany.}
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}
@misc{Hadoop,
author = {{Apache Software Foundation}},
title = {Hadoop},
url = {https://hadoop.apache.org},
year = 2008
}
@book{HaestadBook03,
author = { Thomas M. Walski and Donald V. Chase and Dragan A. Savic and Walter Grayman and Stephen Beckwith and Edmundo Koelle },
title = {Advanced Water Distribution Modeling and Management},
publisher = {Haestad Methods, Inc., Haestad Press},
year = 2003,
edition = {1st}
}
@incollection{HalOliSud2019cutoff,
isbn = {978-1-4503-6111-8},
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
author = { George T. Hall and Oliveto, Pietro S. and Dirk Sudholt },
title = {On the impact of the cutoff time on the performance of
algorithm configurators},
pages = {907--915},
doi = {10.1145/3321707.3321879},
keywords = {theory, automatic configuration, capping}
}
@incollection{HalOliSud2020fast,
volume = 12269,
year = 2020,
address = { Cham, Switzerland},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Thomas B{\"a}ck and Mike Preuss and Deutz, Andr{\'e} and Wang, Hao and Carola Doerr and Emmerich, Michael T. M. and Heike Trautmann },
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XVI}},
author = { George T. Hall and Oliveto, Pietro S. and Dirk Sudholt },
title = {Fast Perturbative Algorithm Configurators},
pages = {19--32},
doi = {10.1007/978-3-030-58112-1_2}
}
@incollection{HalOliSud2020gecco,
epub = {https://dl.acm.org/citation.cfm?id=3377930},
location = {Canc{\'u}n, Mexico},
isbn = {978-1-4503-7128-5},
address = { New York, NY},
publisher = {ACM Press},
year = 2020,
editor = { Carlos A. {Coello Coello} },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2020},
author = {George T. Hall and Oliveto, Pietro S. and Dirk Sudholt },
title = {Analysis of the performance of algorithm configurators for
search heuristics with global mutation operators},
pages = {823--831},
doi = {10.1145/3377930.3390218}
}
@incollection{HamElk2003gmeans,
publisher = {MIT Press},
editor = {S. Thrun and L. Saul and B. Sch\"{o}lkopf},
booktitle = {Advances in Neural Information Processing Systems (NIPS 16)},
year = 2003,
title = {Learning the k in k-means},
author = {Hamerly, Greg and Elkan, Charles},
epub = {https://proceedings.neurips.cc/paper/2003/file/234833147b97bb6aed53a8f4f1c7a7d8-Paper.pdf}
}
@incollection{HamStu2017,
address = { Heidelberg, Germany},
publisher = {Springer},
doi = {10.1007/978-3-319-55453-2},
volume = 10197,
series = {Lecture Notes in Computer Science},
year = 2017,
booktitle = {Proceedings of EvoCOP 2017 -- 17th European Conference on Evolutionary Computation in Combinatorial Optimization },
editor = { Bin Hu and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
author = { Hayfa Hammami and Thomas St{\"u}tzle },
title = {A Computational Study of Neighborhood Operators for Job-Shop
Scheduling Problems with Regular Objectives},
pages = {1--17}
}
@incollection{Han1997mots,
publisher = {Springer Verlag},
editor = {J. Climaco},
year = 1997,
booktitle = {Proceedings of the 13th International Conference on
Multiple Criteria Decision Making (MCDM'97)},
title = {Tabu search for multiobjective optimization: {MOTS}},
author = { Michael Pilegaard Hansen },
pages = {574--586}
}
@misc{HanAkiBau2019pycma,
author = { Nikolaus Hansen and Youhei Akimoto and Petr Baudis},
title = {{CMA-ES/pycma} on {Github}},
howpublished = {Zenodo},
month = feb,
year = 2019,
doi = {10.5281/zenodo.2559634}
}
@techreport{HanAugFin2009bbob_setup,
author = { Nikolaus Hansen and Anne Auger and Finck, S. and Ros, R.},
title = {Real-Parameter Black-Box Optimization Benchmarking 2009:
Experimental setup},
institution = {INRIA, France},
year = 2009,
number = {RR-6828},
supplement = {http://coco.gforge.inria.fr/bbob2012-downloads}
}
@incollection{HanAugRosFin2010comparing,
address = { New York, NY},
publisher = {ACM Press},
year = 2010,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2010},
editor = {Martin Pelikan and J{\"u}rgen Branke },
title = {Comparing Results of 31 Algorithms from the Black-Box
Optimization Benchmarking {BBOB-2009}},
author = { Nikolaus Hansen and Anne Auger and Ros, Raymond and Finck,
Steffen and Po{\v{s}}{\'i}k, Petr },
pages = {1689--1696},
doi = {10.1145/1830761.1830790},
abstract = {This paper presents results of the BBOB-2009 benchmarking of
31 search algorithms on 24 noiseless functions in a black-box
optimization scenario in continuous domain. The runtime of
the algorithms, measured in number of function evaluations,
is investigated and a connection between a single convergence
graph and the runtime distribution is uncovered. Performance
is investigated for different dimensions up to 40-D, for
different target precision values, and in different subgroups
of functions. Searching in larger dimension and multi-modal
functions appears to be more difficult. The choice of the
best algorithm also depends remarkably on the available
budget of function evaluations.},
keywords = {benchmarking, black-box optimization}
}
@techreport{HanFinRosAug2009bbob,
author = { Nikolaus Hansen and Finck, Steffen and Ros, Raymond and Anne Auger },
title = {Real-Parameter Black-Box Optimization Benchmarking 2009:
Noiseless Functions Definitions},
institution = {INRIA, France},
year = 2009,
number = {RR-6829},
note = {Updated February 2010},
annote = {\url{http://coco.gforge.inria.fr/bbob2012-downloads}},
epub = {https://hal.inria.fr/inria-00362633/document}
}
@techreport{HanJas1998,
author = { Michael Pilegaard Hansen and Andrzej Jaszkiewicz },
title = {Evaluating the quality of approximations to the non-dominated
set},
institution = {Institute of Mathematical Modelling, Technical University of
Denmark},
year = 1998,
number = {IMM-REP-1998-7},
address = {Lyngby, Denmark},
annote = {Proposed R2 indicator}
}
@incollection{HanKno2008mpsn,
address = {Berlin\slash Heidelberg},
publisher = {Springer},
series = {Natural Computing Series},
editor = { Joshua D. Knowles and David Corne and Kalyanmoy Deb and Chair, Deva Raj},
year = 2008,
booktitle = {Multiobjective Problem Solving from Nature},
author = { Julia Handl and Joshua D. Knowles },
title = {Modes of Problem Solving with Multiple Objectives:
Implications for Interpreting the {Pareto} Set and
for Decision Making},
doi = {10.1007/978-3-540-72964-8_7},
pages = {131--151}
}
@incollection{HanMla02:mh,
publisher = {Kluwer Academic Publishers, Norwell, MA},
year = 2002,
editor = { Fred Glover and Gary A. Kochenberger},
booktitle = {Handbook of Metaheuristics},
author = { Pierre Hansen and Nenad Mladenovi{\'c} },
title = {Variable Neighborhood Search},
pages = {145--184}
}
@incollection{HanMlaBriPer2010:handbook,
address = { New York, NY},
publisher = {Springer},
edition = {2nd},
series = {International Series in Operations Research \& Management
Science},
volume = 146,
booktitle = {Handbook of Metaheuristics},
year = 2010,
editor = { Michel Gendreau and Jean-Yves Potvin },
title = {Variable {Neighborhood} {Search}},
author = { Pierre Hansen and Nenad Mladenovi{\'c} and Jack Brimberg and Jos{\'e} A. Moreno P{\'e}rez},
pages = {61--86}
}
@incollection{HanOst1996cma,
address = {Piscataway, NJ},
publisher = {IEEE Press},
year = 1996,
editor = { Thomas B{\"a}ck and T. Fukuda and Zbigniew Michalewicz },
booktitle = {Proceedings of the 1996 IEEE International Conference on
Evolutionary Computation (ICEC'96)},
author = { Nikolaus Hansen and Ostermeier, Andreas},
title = {Adapting Arbitrary Normal Mutation Distributions in Evolution
Strategies: The Covariance Matrix Adaptation},
pages = {312--317},
annote = {Proposed CMA-ES},
shorttitle = {Adapting Arbitrary Normal Mutation Distributions in Evolution
Strategies},
doi = {10.1109/ICEC.1996.542381},
abstract = {A new formulation for coordinate system independent
adaptation of arbitrary normal mutation distributions with
zero mean is presented. This enables the evolution strategy
(ES) to adapt the correct scaling of a given problem and also
ensures invariance with respect to any rotation of the
fitness function (or the coordinate system). Especially
rotation invariance, here resulting directly from the
coordinate system independent adaptation of the mutation
distribution, is an essential feature of the ES with regard
to its general applicability to complex fitness
functions. Compared to previous work on this subject, the
introduced formulation facilitates an interpretation of the
resulting mutation distribution, making sensible manipulation
by the user possible (if desired). Furthermore it enables a
more effective control of the overall mutation variance
(expected step length)},
keywords = {Evolution strategies, Evolutionary algorithms,
self-adaptation, stochastic processes, Covariance matrix,
matrix algebra, derandomised adaptation, mutation
distribution, rotation invariance, electronic switching
systems}
}
@incollection{Hanne2001emo,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2001},
address = {Berlin\slash Heidelberg},
series = {Lecture Notes in Computer Science},
volume = 1993,
year = 2001,
publisher = {Springer},
editor = { Eckart Zitzler and Kalyanmoy Deb and Lothar Thiele and Carlos A. {Coello Coello} and David Corne },
title = {Global Multiobjective Optimization with Evolutionary
Algorithms: Selection Mechanisms and Mutation Control},
author = {Hanne, Thomas},
pages = {197--212}
}
@phdthesis{Hansen1998PhD,
author = { Michael Pilegaard Hansen },
title = {Metaheuristics for multiple objective combinatorial
optimization},
school = {Institute of Mathematical Modelling, Technical
University of Denmark},
month = mar,
year = 1998
}
@incollection{Hansen2006cma,
title = {The {CMA} evolution strategy: a comparing review},
author = { Nikolaus Hansen },
booktitle = {Towards a new evolutionary computation},
pages = {75--102},
year = 2006,
publisher = {Springer}
}
@incollection{Hansen2009bpopcma,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2009},
address = { New York, NY},
year = 2009,
publisher = {ACM Press},
editor = { Franz Rothlauf },
author = { Nikolaus Hansen },
title = {Benchmarking a {BI}-population {CMA-ES} on the {BBOB}-2009
function testbed},
pages = {2389--2396},
keywords = {bipop-cma-es}
}
@inproceedings{HaoCaiHua2006,
publisher = {IEEE Press},
year = 2006,
booktitle = {Proceedings of the International Conference on
Machine Learning and Cybernetics},
key = {ICMLC},
author = {Zhifeng Hao and Ruichu Cai and Han Huang},
title = {An Adaptive Parameter Control Strategy for {ACO}},
pages = {203--206}
}
@incollection{HaoHuaQinCai2007,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 4490,
booktitle = {Computational Science -- ICCS 2007, 7th International
Conference, Proceedings, Part IV},
publisher = {Springer},
year = 2007,
editor = {Yong Shi and G. Dick van Albada and Jack Dongarra and Peter
M. A. Sloot},
author = {Zhifeng Hao and Han Huang and Yong Qin and Ruichu
Cai},
title = {An {ACO} Algorithm with Adaptive Volatility Rate of
Pheromone Trail},
pages = {1167--1170}
}
@inproceedings{HaoPan1998,
year = 1998,
booktitle = {Fifth International Symposium on Artificial Intelligence and Mathematics,
{AIM} 1998, Fort Lauderdale, Florida, USA, January 4-6, 1998},
editor = {Martin C. Golumbic and others},
author = { Jin-Kao Hao and Pannier, J{\^{e}}rome},
title = {Simulated Annealing and Tabu Search for Constraint Solving},
pages = {1--15}
}
@incollection{HarGin1995,
publisher = {Morgan Kaufmann Publishers},
editor = {Chris S. Mellish},
year = 1995,
booktitle = {Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI-95)},
author = {William D. Harvey and Matthew L. Ginsberg},
title = {Limited Discrepancy Search},
pages = {607--615}
}
@incollection{HarMigSto2023keep,
location = {Lisbon, Portugal},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2023},
annote = {ISBN: 979-8-4007-0120-7},
address = { New York, NY},
year = 2023,
publisher = {ACM Press},
editor = {Silva, Sara and Lu{\'i}s Paquete },
author = { Emma Hart and Miguel, Ian and Stone, Christopher and Renau,
Quentin},
title = {Towards optimisers that `{Keep} {Learning}'},
pages = {1636--1638},
doi = {10.1145/3583133.3596344}
}
@incollection{HassGueSil2016deep,
epub = {http://www.aaai.org/Library/AAAI/aaai16contents.php},
year = 2016,
publisher = {{AAAI} Press},
booktitle = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
editor = {Dale Schuurmans and Michael P. Wellman},
title = {Deep Reinforcement Learning with Double {Q}-Learning},
author = {van Hasselt, Hado and Guez, Arthur and Silver, David}
}
@inproceedings{HeiIge2009aicml,
publisher = {ACM Press},
address = { New York, NY},
editor = {Andrea Pohoreckyj Danyluk and L{\'{e}}on Bottou and Michael
L. Littman},
booktitle = {Proceedings of the 26th International Conference on Machine Learning, {ICML} 2009},
year = 2009,
title = {Hoeffding and {Bernstein} races for selecting policies in
evolutionary direct policy search},
author = { Heidrich-Meisner, Verena and Christian Igel },
keywords = {automated algorithm configuration, CMA-ES, racing},
pages = {401--408},
doi = {10.1145/1553374.1553426}
}
@misc{Hel2018lkh,
author = { Keld Helsgaun },
title = {Source Code of the {Lin}-{Kernighan}-{Helsgaun} Traveling
Salesman Heuristic},
howpublished = {\url{http://webhotel4.ruc.dk/~keld/research/LKH/}},
year = 2018
}
@incollection{Hel2018ppsn,
volume = 11101,
year = 2018,
address = { Cham, Switzerland},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Anne Auger and Carlos M. Fonseca and Louren{\c c}o, N. and Penousal Machado and Lu{\'i}s Paquete and Darrell Whitley },
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XV}},
author = { Keld Helsgaun },
title = {Efficient Recombination in the {Lin}-{Kernighan}-{Helsgaun}
Traveling Salesman Heuristic},
pages = {95--107},
doi = {10.1007/978-3-319-99253-2_8}
}
@book{Hen1999:mit,
author = {van Hentenryck, Pascal },
title = {The {OPL} optimization programming language},
publisher = {MIT Press},
year = 1999,
address = {Cambridge, MA}
}
@inproceedings{HenIzz2015interplanetary,
publisher = {IJCAI/AAAI Press, Menlo Park, CA},
editor = {Qiang Yang and Michael Wooldridge},
year = 2015,
booktitle = {Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI-15)},
author = {Hennes, Daniel and Dario Izzo },
title = {Interplanetary trajectory planning with {Monte} {Carlo} tree
search},
pages = {769--775}
}
@inproceedings{HenIzzLan2016fast,
year = 2016,
booktitle = {Computational Intelligence (SSCI), 2016 IEEE Symposium Series
on},
editor = {Chen, Xuewen and Stafylopatis, Andreas},
author = {Hennes, Daniel and Dario Izzo and Landau, Damon},
title = {Fast approximators for optimal low-thrust hops between main
belt asteroids},
pages = {1--7},
doi = {10.1109/SSCI.2016.7850107}
}
@incollection{HenJacJoh2003,
doi = {10.1007/b101874},
address = { Boston, MA},
publisher = {Springer},
year = 2003,
editor = { Fred Glover and Gary A. Kochenberger},
booktitle = {Handbook of Metaheuristics},
author = { Darrall Henderson and Sheldon H. Jacobson and Alan W. Johnson },
title = {The Theory and Practice of Simulated Annealing},
pages = {287--319}
}
@book{HenMich05:mit,
author = {van Hentenryck, Pascal and Laurent D. Michel },
title = {Constraint-based Local Search},
publisher = {MIT Press},
year = 2005,
address = {Cambridge, MA}
}
@inproceedings{HenMich07synthesis,
publisher = {{AAAI} Press\slash {MIT} Press, Menlo Park, CA},
year = 2007,
editor = {Robert C. Holte and Adele Howe},
booktitle = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
author = {van Hentenryck, Pascal and Laurent D. Michel },
title = {Synthesis of constraint-based local search
algorithms from high-level models},
pages = {273--278}
}
@inproceedings{HerGraObe1999svmrank,
key = {ICANN},
booktitle = {ICANN'99: Proceedings of the 9th International Conference on
Artificial Neural Networks},
year = 1999,
title = {Support vector learning for ordinal regression},
author = {R. Herbrich and T. Graepel and K. Obermayer},
keywords = {support vector machine;metric regression;support vector
learning;ordinal regression;information retrieval;risk
functional;machine learning;pattern classification;},
abstract = {We investigate the problem of predicting variables of ordinal
scale. This task is referred to as ordinal regression and is
complementary to the standard machine learning tasks of
classification and metric regression. In contrast to
statistical models we present a distribution independent
formulation of the problem together with uniform bounds of
the risk functional. The approach presented is based on a
mapping from objects to scalar utility values. Similar to
support vector methods we derive a new learning algorithm for
the task of ordinal regression based on large margin rank
boundaries. We give experimental results for an information
retrieval task: learning the order of documents with respect
to an initial query. Experimental results indicate that the
presented algorithm outperforms more naive approaches to
ordinal regression such as support vector classification and
support vector regression in the case of more than two
ranks.},
doi = {10.1049/cp:19991091},
pages = {97--102},
annote = {Proposed the pairwise transform for learning-to-rank}
}
@misc{HerLozMol2010test,
author = { Francisco Herrera and Manuel Lozano and Daniel Molina },
title = {Test suite for the special issue of {Soft}
{Computing} on scalability of evolutionary
algorithms and other metaheuristics for large scale
continuous optimization problems},
year = 2010,
howpublished = {\url{http://sci2s.ugr.es/eamhco/}},
keywords = {SOCO benchmark}
}
@incollection{HerSch2022archive,
location = {Boston, Massachusetts},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2022},
address = { New York, NY},
year = 2022,
publisher = {ACM Press},
editor = { Jonathan E. Fieldsend and Markus Wagner },
author = { Carlos Hern{\'a}ndez and Oliver Sch{\"u}tze },
title = {A bounded archive based for bi-objective problems based on
distance and e-dominance to avoid cyclic behavior},
pages = {583--591},
doi = {10.1145/3512290.3528840}
}
@book{Heyman2003,
title = {Stochastic models in operations research: stochastic
optimization},
author = {Heyman, Daniel P and Sobel, Matthew J},
volume = 2,
year = 2003,
publisher = {Courier Corporation}
}
@book{Hol75,
author = { J. Holland },
title = {Adaptation in Natural and Artificial Systems},
publisher = {University of Michigan Press},
year = 1975
}
@book{HolWol73:nonparam_stats,
author = {Myle Hollander and Douglas A. Wolfe},
title = {Nonparametric statistical inference},
publisher = {John Wiley \& Sons},
address = { New York, NY},
year = 1973,
note = {Second edition (1999)}
}
@incollection{Hoo2004discover,
address = { New York, NY},
publisher = {ACM Press},
year = 2004,
editor = {Won Kim and Ronny Kohavi and Johannes Gehrke and William
DuMouchel},
booktitle = {Proceedings of the tenth ACM SIGKDD international conference
on Knowledge discovery and data mining, {KDD'04}},
author = {Hooker, Giles},
title = {Discovering Additive Structure in Black Box Functions},
pages = {575--580},
doi = {10.1145/1014052.1014122},
abstract = {Many automated learning procedures lack interpretability,
operating effectively as a black box: providing a prediction
tool but no explanation of the underlying dynamics that drive
it. A common approach to interpretation is to plot the
dependence of a learned function on one or two predictors. We
present a method that seeks not to display the behavior of a
function, but to evaluate the importance of non-additive
interactions within any set of variables. Should the function
be close to a sum of low dimensional components, these
components can be viewed and even modeled
parametrically. Alternatively, the work here provides an
indication of where intrinsically high-dimensional behavior
takes place.The calculations used in this paper correspond
closely with the functional ANOVA decomposition; a
well-developed construction in Statistics. In particular, the
proposed score of interaction importance measures the loss
associated with the projection of the prediction function
onto a space of additive models. The algorithm runs in linear
time and we present displays of the output as a graphical
model of the function for interpretation purposes.},
numpages = 6,
keywords = {diagnostics, functional ANOVA, feature selection,
interpretation, visualization, additive models, draphical
models}
}
@incollection{HooHutLey2021acsat,
author = { Holger H. Hoos and Frank Hutter and Kevin Leyton-Brown },
title = {Automated Configuration and Selection of {SAT} Solvers},
booktitle = {Handbook of Satisfiability},
publisher = {IOS Press},
year = 2021,
pages = {481--507},
month = feb,
doi = {10.3233/faia200995}
}
@book{HooStu04:sls-elsevier,
author = { Holger H. Hoos and Thomas St{\"u}tzle },
title = {Stochastic Local Search: Foundations and Applications},
publisher = {Elsevier},
address = {Amsterdam, The Netherlands},
year = 2004,
anote = {superseed by~\cite{{HooStu05sls-mk}}}
}
@book{HooStu05sls-mk,
author = { Holger H. Hoos and Thomas St{\"u}tzle },
title = {Stochastic Local Search---Foundations and Applications},
publisher = {Morgan Kaufmann Publishers},
address = { San Francisco, CA},
year = 2005
}
@inproceedings{HooStu1998uai,
author = { Holger H. Hoos and Thomas St{\"u}tzle },
title = {Evaluating {Las} {Vegas} Algorithms --- Pitfalls and
Remedies},
booktitle = {Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence},
editor = {Gregory F. Cooper and Seraf{\'i}n Moral},
year = 1998,
publisher = {Morgan Kaufmann Publishers, San Francisco, CA},
pages = {238--245}
}
@inproceedings{Hoos2011mic,
author = { Holger H. Hoos },
title = {Programming by Optimisation: Towards a new Paradigm
for Developing High-Performance Software},
booktitle = {MIC 2011, the 9th Metaheuristics International
Conference},
year = 2011,
note = {{Plenary talk}},
url = {http://mic2011.diegm.uniud.it/uploads/plenaries/Hoos-MIC2011.pdf}
}
@incollection{Hoos2012autsea,
year = 2012,
address = { Berlin, Germany},
publisher = {Springer},
booktitle = {Autonomous Search},
editor = { Youssef Hamadi and E. Monfroy and F. Saubion},
author = { Holger H. Hoos },
title = {Automated Algorithm Configuration and Parameter
Tuning},
pages = {37--71},
doi = {10.1007/978-3-642-21434-9_3}
}
@incollection{HorNeu2008,
address = { New York, NY},
publisher = {ACM Press},
year = 2008,
editor = {Conor Ryan},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2008},
author = {Horoba, Christian and Frank Neumann },
title = {Benefits and drawbacks for the use of epsilon-dominance in
evolutionary multi-objective optimization},
pages = {641--648},
annote = {Proposed $\epsilon$-box}
}
@inproceedings{HorNafGol1994npga,
month = jun,
year = 1994,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 1994 World Congress on Computational Intelligence (WCCI 1994)},
key = {WCCI},
author = {Horn, J. and Nafpliotis, N. and David E. Goldberg },
title = {A niched {Pareto} genetic algorithm for multiobjective
optimization},
pages = {82--87},
doi = {10.1109/ICEC.1994.350037}
}
@inproceedings{HosEec2008cole,
series = {CGO '08},
address = { New York, NY},
publisher = {ACM Press},
year = 2008,
booktitle = {Proceedings of the 6th Annual IEEE/ACM International
Symposium on Code Generation and Optimization},
editor = {Soffa, Mary Lou and Duesterwald, Evelyn},
author = {Kenneth Hoste and Lieven Eeckhout},
title = {Cole: Compiler Optimization Level Exploration},
pages = {165--174},
doi = {10.1145/1356058.1356080}
}
@incollection{HuaYaHaoCai2006,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 4115,
booktitle = {International Conference on Computational Science (3)},
publisher = {Springer},
year = 2006,
editor = {De-Shuang Huang and Kang Li and George W. Irwin},
author = {Han Huang and Xiaowei Yang and Zhifeng Hao and
Ruichu Cai},
title = {A Novel {ACO} Algorithm with Adaptive Parameter},
pages = {12--21}
}
@inproceedings{HuaYanTse04:ics,
author = { Kuo-Si Huang and Chang-Biau Yang and Kuo-tsung Tseng },
title = {Fast algorithms for finding the common subsequences
of multiple sequences},
booktitle = {Proceedings of the International Computer Symposium},
pages = {1006--1011},
year = 2004,
publisher = {IEEE Press}
}
@inproceedings{Hughes2003msops,
year = 2003,
address = {Piscataway, NJ},
publisher = {IEEE Press},
month = dec,
booktitle = {Proceedings of the 2003 Congress on Evolutionary Computation (CEC'03)},
key = {IEEE CEC},
title = {Multiple single objective {Pareto} sampling},
author = { Hughes, Evan J. },
pages = {2678--2684}
}
@inproceedings{Hughes2007msops,
address = {Piscataway, NJ},
publisher = {IEEE Press},
year = 2007,
booktitle = {Proceedings of the 2007 Congress on Evolutionary Computation (CEC 2007)},
key = {IEEE CEC},
title = {{MSOPS-II}: A general-purpose many-objective optimiser},
author = { Hughes, Evan J. },
pages = {3944--3951}
}
@incollection{Hughes2011models,
address = { New York, NY},
publisher = {ACM Press},
year = 2011,
editor = {Natalio Krasnogor and Pier Luca Lanzi},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011},
title = {Many-objective directed evolutionary line search},
author = { Hughes, Evan J. },
pages = {761--768}
}
@inproceedings{HunLop2019turing,
isbn = {978-1-5262-0820-0},
organization = {Alan Turing Institute},
month = nov # { 21--22},
year = 2019,
date = {2019-11-21/2019-11-22},
address = {London, UK},
editor = {Iv{\'a}n Palomares},
booktitle = {International Alan Turing Conference on Decision Support and
Recommender systems},
author = {Maura Hunt and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Modeling a Decision-Maker in Goal Programming by means of
Computational Rationality},
pages = {17--20},
abstract = {This paper extends a simulation of cognitive mechanisms in
the context of multi-criteria decision-making by using ideas
from computational rationality. Specifically, this paper
improves the simulation of a human decision-maker (DM) by
considering how resource constraints impact their evaluation
process in an interactive Goal Programming problem. Our
analysis confirms and emphasizes a previous simulation study
by showing key areas that could be effected by cognitive
mechanisms. While the results are promising, the effects
should be validated by future experiments with human DMs.},
epub = {https://dsrs.blogs.bristol.ac.uk/files/2020/01/DSRS-Turing_19.pdf#page=24}
}
@incollection{HusStu2009hm,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 5818,
series = {Lecture Notes in Computer Science},
editor = { Mar{\'i}a J. Blesa and Christian Blum and Luca {Di Gaspero} and Andrea Roli and M. Sampels and Andrea Schaerf},
year = 2009,
booktitle = {Hybrid Metaheuristics},
author = {Mohamed Saifullah Hussin and Thomas St{\"u}tzle },
title = {Hierarchical Iterated Local Search for the Quadratic
Assignment Problem},
pages = {115--129},
doi = {10.1007/978-3-642-04918-7_9}
}
@inproceedings{HutBabHooHu2007fmcad,
address = {Austin, Texas, USA},
year = 2007,
publisher = {IEEE Computer Society, Washington, DC, USA},
booktitle = {{FMCAD'07}: Proceedings of the 7th International Conference
Formal Methods in Computer Aided Design},
editor = {Jason Baumgartner and Mary Sheeran},
author = { Frank Hutter and Domagoj Babi{\'c} and Holger H. Hoos and Alan J. Hu},
title = {Boosting Verification by Automatic Tuning of
Decision Procedures},
pages = {27--34}
}
@incollection{HutHooLey2009gecco,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2009},
address = { New York, NY},
year = 2009,
publisher = {ACM Press},
editor = { Franz Rothlauf },
author = { Frank Hutter and Holger H. Hoos and Kevin Leyton-Brown and Kevin P. Murphy},
title = {An experimental investigation of model-based
parameter optimisation: {SPO} and beyond},
pages = {271--278},
doi = {10.1145/1569901.1569940}
}
@incollection{HutHooLey2010:cpaior,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 6140,
booktitle = {Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2010},
publisher = {Springer},
year = 2010,
editor = { Andrea Lodi and Michela Milano and Paolo Toth },
author = { Frank Hutter and Holger H. Hoos and Kevin Leyton-Brown },
title = {Automated Configuration of Mixed Integer Programming Solvers},
pages = {186--202},
keywords = {MIP, ParamILS},
doi = {10.1007/978-3-642-13520-0_23}
}
@incollection{HutHooLey2011lion,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 6683,
booktitle = {Learning and Intelligent Optimization, 5th International Conference, LION 5},
publisher = {Springer},
year = 2011,
editor = { Carlos A. {Coello Coello} },
author = { Frank Hutter and Holger H. Hoos and Kevin Leyton-Brown },
title = {Sequential Model-Based Optimization for General
Algorithm Configuration},
pages = {507--523},
keywords = {SMAC,ROAR},
doi = {10.1007/978-3-642-25566-3_40}
}
@incollection{HutHooLey2012lion,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 7219,
booktitle = {Learning and Intelligent Optimization, 6th International Conference, LION 6},
publisher = {Springer},
year = 2012,
editor = { Youssef Hamadi and Marc Schoenauer },
author = { Frank Hutter and Holger H. Hoos and Kevin Leyton-Brown },
title = {Parallel Algorithm Configuration},
pages = {55--70}
}
@incollection{HutHooLey2013lion,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 7997,
booktitle = {Learning and Intelligent Optimization, 7th International Conference, LION 7},
publisher = {Springer},
year = 2013,
editor = { Panos M. Pardalos and G. Nicosia},
author = { Frank Hutter and Holger H. Hoos and Kevin Leyton-Brown },
title = {Identifying Key Algorithm Parameters and Instance Features
using Forward Selection},
pages = {364--381},
doi = {10.1007/978-3-642-44973-4_40},
abstract = {Most state-of-the-art algorithms for large-scale optimization
problems expose free parameters, giving rise to combinatorial
spaces of possible configurations. Typically, these spaces
are hard for humans to understand. In this work, we study a
model-based approach for identifying a small set of both
algorithm parameters and instance features that suffices for
predicting empirical algorithm performance well. Our
empirical analyses on a wide variety of hard combinatorial
problem benchmarks spanning SAT, MIP, and TSP show that--for
parameter configurations sampled uniformly at random--very
good performance predictions can typically be obtained based
on just two key parameters, and that similarly, few instance
features and algorithm parameters suffice to predict the most
salient algorithm performance characteristics in the combined
configuration/feature space. We also use these models to
identify settings of these key parameters that are predicted
to achieve the best overall performance, both on average
across instances and in an instance-specific way. This serves
as a further way of evaluating model quality and also
provides a tool for further understanding the parameter
space. We provide software for carrying out this analysis on
arbitrary problem domains and hope that it will help
algorithm developers gain insights into the key parameters of
their algorithms, the key features of their instances, and
their interactions.},
keywords = {parameter importance}
}
@inproceedings{HutHooLey2014icml,
publisher = {{PMLR}},
year = 2014,
volume = 32,
booktitle = {Proceedings of the 31st International Conference on Machine Learning, {ICML} 2014},
editor = {Xing, Eric P. and Jebara, Tony},
author = { Frank Hutter and Holger H. Hoos and Kevin Leyton-Brown },
title = {An Efficient Approach for Assessing Hyperparameter
Importance},
pages = {754--762},
url = {https://proceedings.mlr.press/v32/hutter14.html},
keywords = {fANOVA, parameter importance}
}
@incollection{HutHooLeyMur2010lion,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 6073,
booktitle = {Learning and Intelligent Optimization, 4th International Conference, LION 4},
publisher = {Springer},
year = 2010,
editor = { Christian Blum and Roberto Battiti },
author = { Frank Hutter and Holger H. Hoos and Kevin Leyton-Brown and Kevin Murphy},
title = {Time-Bounded Sequential Parameter Optimization},
pages = {281--298},
doi = {10.1007/978-3-642-13800-3_30}
}
@inproceedings{HutHooStu07aaai,
publisher = {{AAAI} Press\slash {MIT} Press, Menlo Park, CA},
year = 2007,
editor = {Robert C. Holte and Adele Howe},
booktitle = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
author = { Frank Hutter and Holger H. Hoos and Thomas St{\"u}tzle },
title = {Automatic Algorithm Configuration Based on Local Search},
pages = {1152--1157}
}
@incollection{HutLopFaw2014lion,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 8426,
booktitle = {Learning and Intelligent Optimization, 8th International Conference, LION 8},
publisher = {Springer},
year = 2014,
editor = { Panos M. Pardalos and Mauricio G. C. Resende and Chrysafis Vogiatzis and Jose
L. Walteros},
author = { Frank Hutter and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Chris Fawcett and Marius Thomas Lindauer and Holger H. Hoos and Kevin Leyton-Brown and Thomas St{\"u}tzle },
title = {{AClib}: A Benchmark Library for Algorithm Configuration},
pages = {36--40},
doi = {10.1007/978-3-319-09584-4_4}
}
@misc{Hutter2007satbench,
author = { Frank Hutter },
title = {{SAT} benchmarks used in automated algorithm configuration},
howpublished = {\url{http://www.cs.ubc.ca/labs/beta/Projects/AAC/SAT-benchmarks.html}},
year = 2007
}
@phdthesis{HutterPhD,
author = { Frank Hutter },
title = {Automated Configuration of Algorithms for Solving
Hard Computational Problems},
school = {University of British Columbia, Department of
Computer Science},
address = {Vancouver, Canada},
year = 2009,
month = oct
}
@misc{IJCAI2021checklist,
author = {Zhiyuan Liu and Jian Tang},
title = {IJCAI 2021 Reproducibility Guidelines, 35th International
Joint Conference on Artificial Intelligence},
year = 2021,
howpublished = {\url{https://ijcai-21.org/wp-content/uploads/2020/12/20201226-IJCAI-Reproducibility.pdf}}
}
@techreport{INRIA-RR-7871,
author = { J{\'e}r{\'e}mie Humeau and Arnaud Liefooghe and Talbi, El-Ghazali and Verel, S{\'e}bastien },
title = {{ParadisEO-MO}: From Fitness Landscape Analysis to Efficient
Local Search Algorithms},
institution = {INRIA, France},
year = 2012,
type = {Rapport de recherche},
number = {RR-7871},
epub = {http://hal.inria.fr/hal-00665421/PDF/RR-7871.pdf}
}
@techreport{IRIDIA-2003-037,
author = { Mauro Birattari },
title = {The {\rpackage{race}} Package for~\proglang{R}: {Racing}
Methods for the Selection of the Best},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2003,
number = {TR/IRIDIA/2003-037}
}
@techreport{IRIDIA-2004-001,
author = { Mauro Birattari },
title = {On the Estimation of the Expected Performance of a Metaheuristic on a Class of Instances. How Many Instances, How Many Runs?},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2004,
number = {TR/IRIDIA/2004-001}
}
@techreport{IRIDIA-2007-019,
author = { Krzysztof Socha and Marco Dorigo },
title = {Ant Colony Optimization for Mixed-Variable Optimization
Problems},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2007,
number = {TR/IRIDIA/2007-019},
month = oct
}
@techreport{IRIDIA-2009-015,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2009,
number = {TR/IRIDIA/2009-015},
month = may,
note = {Published as a book chapter~\cite{LopPaqStu09emaa}}
}
@techreport{IRIDIA-2009-019,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An Analysis of Algorithmic Components for
Multiobjective Ant Colony Optimization: A Case Study
on the Biobjective {TSP}},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2009-019},
year = 2009,
month = jun,
note = {Published in the proceedings of Evolution Artificielle, 2009~\cite{LopStu09ea}}
}
@techreport{IRIDIA-2009-020,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Effective Hybrid Stochastic Local Search Algorithms
for Biobjective Permutation Flowshop Scheduling},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2009-020},
year = 2009,
month = jun,
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2009-020.pdf},
note = {Published in the proceedings of Hybrid Metaheuristics 2009~\cite{DubLopStu09:hm-bfsp}}
}
@techreport{IRIDIA-2009-026,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Adaptive ``Anytime'' Two-Phase Local Search},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2010,
number = {TR/IRIDIA/2009-026},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2009-026.pdf},
note = {Published in the proceedings of LION 4~\cite{DubLopStu10:lion-bfsp}}
}
@techreport{IRIDIA-2010-002,
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Paola Pellegrini and Michael Maur and Marco A. {Montes de Oca} and Mauro Birattari and Marco Dorigo },
title = {Parameter Adaptation in Ant Colony Optimization},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2010-002},
year = 2010,
month = jan,
note = {Published as a book chapter~\cite{StuLopPel2011autsea}}
}
@techreport{IRIDIA-2010-019,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {A Hybrid {TP+PLS} Algorithm for Bi-objective
Flow-Shop Scheduling Problems},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2010,
number = {TR/IRIDIA/2010-019},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2010-019.pdf},
note = {Published in Computers \& Operations Research~\cite{DubLopStu2011cor}}
}
@techreport{IRIDIA-2010-020,
author = {M. S. Hussin and Thomas St{\"u}tzle },
title = {Tabu Search vs. Simulated Annealing for Solving
Large Quadratic Assignment Instances},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2010,
number = {TR/IRIDIA/2010-020}
}
@techreport{IRIDIA-2010-022,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Improving the Anytime Behavior of Two-Phase Local
Search},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2010,
number = {TR/IRIDIA/2010-022},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2010-022.pdf},
note = {Published in Annals of Mathematics and Artificial Intelligence~\cite{DubLopStu2011amai}}
}
@techreport{IRIDIA-2011-001,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Joshua D. Knowles and Marco Laumanns },
title = {On Sequential Online Archiving of Objective Vectors},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2011,
number = {TR/IRIDIA/2011-001},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2011-001.pdf},
note = {This is a revised version of the paper published in EMO 2011~\cite{LopKnoLau2011emo}}
}
@techreport{IRIDIA-2011-002,
author = { Mauro Birattari and Marco Chiarandini and Marco Saerens and Thomas St{\"u}tzle },
year = 2011,
title = {Learning graphical models for parameter tuning},
number = {TR/IRIDIA/2011-002},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2011-002.pdf}
}
@techreport{IRIDIA-2011-003,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {The Automatic Design of Multi-Objective Ant Colony
Optimization Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2011,
number = {TR/IRIDIA/2011-003},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2011-003.pdf},
note = {Published in IEEE Transactions on Evolutionary
Computation~\cite{LopStu2012tec}}
}
@techreport{IRIDIA-2011-010,
author = {Liao, Tianjun and Daniel Molina and Marco A. {Montes de Oca} and Thomas St{\"u}tzle },
title = {A Note on the Effects of Enforcing Bound Constraints on
Algorithm Comparisons using the {IEEE CEC'05} Benchmark
Function Suite},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2011,
number = {TR/IRIDIA/2011-010},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2011-010.pdf},
note = {Published in Evolutionary Computation~\cite{LiaMolMonStu2014}}
}
@techreport{IRIDIA-2011-022,
author = {Liao, Tianjun and Daniel Molina and Marco A. {Montes de Oca} and Thomas St{\"u}tzle },
title = {Computational Results for an Automatically Tuned {IPOP-CMA-ES} on the {CEC'05} Benchmark Set},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2011,
number = {TR/IRIDIA/2011-022}
}
@techreport{IRIDIA-2012-012,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatically Improving the Anytime Behaviour of
Optimisation Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2012,
number = {TR/IRIDIA/2012-012},
month = may,
note = {Published in European Journal of Operational Research~\cite{LopStu2013ejor}}
}
@techreport{IRIDIA-2012-019,
author = { Andreea Radulescu and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatically Improving the Anytime Behaviour of
Multiobjective Evolutionary Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2012,
number = {TR/IRIDIA/2012-019},
note = {Published in the proceedings of EMO 2013~\cite{RadLopStu2013emo}}
}
@techreport{IRIDIA-2013-002,
author = {Liao, Tianjun and Thomas St{\"u}tzle and Marco A. {Montes de Oca} and Marco Dorigo },
title = {A Unified Ant Colony Optimization Algorithm for Continuous Optimization},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2013,
number = {TR/IRIDIA/2013-002}
}
@techreport{IRIDIA-2013-015,
author = { Franco Mascia and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and Thomas St{\"u}tzle },
year = 2013,
title = {Grammar-based generation of stochastic local search
heuristics through automatic algorithm configuration
tools},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2013-015}
}
@techreport{IRIDIA-2014-009,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Arnaud Liefooghe and Verel, S{\'e}bastien },
title = {Local Optimal Sets and Bounded Archiving on
Multi-objective {NK}-Landscapes with Correlated
Objectives},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2014,
number = {TR/IRIDIA/2014-009}
}
@techreport{IRIDIA-2014-014,
author = { Vito Trianni and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Advantages of Multi-Objective Optimisation in Evolutionary
Robotics: Survey and Case Studies},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2014,
number = {TR/IRIDIA/2014-014},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2014-014.pdf}
}
@techreport{IRIDIA-2017-005,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {A Large-Scale Experimental Evaluation of High-Performing
Multi- and Many-Objective Evolutionary Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2017,
number = {TR/IRIDIA/2017-005},
month = nov
}
@techreport{IRIDIA-2017-006,
author = { Alberto Franzin and P{\'e}rez C{\'a}ceres, Leslie and Thomas St{\"u}tzle },
title = {Effect of Transformations of Numerical Parameters
in Automatic Algorithm Configuration},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2017-006},
year = 2017,
month = mar,
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2017-006.pdf}
}
@techreport{IRIDIA-2017-011,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Configuration of Multi-objective Optimizers and
Multi-objective Configuration},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2017,
number = {TR/IRIDIA/2017-011},
month = nov,
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2017-011.pdf},
note = {Published as a book chapter~\cite{BezLopStu2020chapter}}
}
@techreport{IRIDIA-2017-012,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Marie-El{\'e}onore Kessaci and Thomas St{\"u}tzle },
title = {Automatic Design of Hybrid Metaheuristics from Algorithmic
Components},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2017,
number = {TR/IRIDIA/2017-012},
month = dec,
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2017-012.pdf}
}
@techreport{IRIDIA-2018-001,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatically Designing State-of-the-Art Multi- and
Many-Objective Evolutionary Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2018,
number = {TR/IRIDIA/2018-001},
month = jan,
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2018-001.pdf},
note = {Published in Evolutionary Computation journal~\cite{BezLopStu2019ec}}
}
@techreport{IRIDIA-2018-010,
author = { Alberto Franzin and Thomas St{\"u}tzle },
title = {Revisiting Simulated Annealing: a Component-Based Analysis},
year = 2018,
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2018-010},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2018-010.pdf}
}
@techreport{IRIDIA-2021-002,
author = {Camacho-Villal\'{o}n, Christian Leonardo and Thomas St{\"u}tzle and Marco Dorigo },
title = {{PSO-X}: A Component-Based Framework for the Automatic Design
of Particle Swarm Optimization Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2021,
number = {TR/IRIDIA/2021-002},
annote = {Published as \cite{CamStuDor2021psox}},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2021-002.pdf}
}
@techreport{IRIDIA-2021-005,
author = { Alberto Franzin and Thomas St{\"u}tzle },
title = {A Landscape-based Analysis of Fixed Temperature and Simulated Annealing},
year = 2021,
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2021-005},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2021-005.pdf}
}
@techreport{IRIDIA-2021-006,
author = {Camacho-Villal\'{o}n, Christian Leonardo and Thomas St{\"u}tzle and Marco Dorigo },
title = {Cuckoo Search {$\equiv (\mu + \lambda$)}-Evolution Strategy --
A Rigorous Analysis of an Algorithm That Has Been Misleading
the Research Community for More Than 10 Years and Nobody Seems to Have Noticed},
year = 2021,
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2021-006},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2021-006.pdf}
}
@incollection{Ige2005mosvm,
address = {Berlin\slash Heidelberg},
publisher = {Springer},
volume = 3410,
series = {Lecture Notes in Computer Science},
editor = { Carlos A. {Coello Coello} and Hern{\'a}ndez Aguirre, Arturo and Eckart Zitzler },
year = 2005,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2005},
author = { Christian Igel },
title = {Multi-objective Model Selection for Support Vector Machines},
annote = {Early work on multi-objective hyper-parameter optimization
(AutoML)},
pages = {534--546},
doi = {10.1007/978-3-540-31880-4_37}
}
@inproceedings{IkeKitShi2001cec,
address = {Piscataway, NJ},
publisher = {IEEE Press},
year = 2001,
booktitle = {Proceedings of the 2001 Congress on Evolutionary Computation (CEC'01)},
key = {IEEE CEC},
author = {Ikeda, Kokolo and Hajime Kita and Shigenobu Kobayashi},
title = {Failure of {Pareto}-based {MOEA}s: {Does} non-dominated really
mean near to optimal?},
pages = {957--962},
keywords = {dominance resistance}
}
@book{IllPenSto2008,
title = {Statistical Analysis and Modelling of Spatial Point Patterns},
author = {Illian, Janine and Penttinen, Antti and Stoyan, Helga and
Stoyan, Dietrich},
publisher = {Wiley},
year = 2008
}
@incollection{IreMerMid2001,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2001},
address = {Berlin\slash Heidelberg},
series = {Lecture Notes in Computer Science},
volume = 1993,
year = 2001,
publisher = {Springer},
editor = { Eckart Zitzler and Kalyanmoy Deb and Lothar Thiele and Carlos A. {Coello Coello} and David Corne },
author = { S. Iredi and D. Merkle and Martin Middendorf },
title = {Bi-Criterion Optimization with Multi Colony Ant
Algorithms},
pages = {359--372},
keywords = {BicriterionAnt}
}
@misc{IridiaSupp2012-011,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {{Automatically Improving the Anytime Behaviour of Optimisation Algorithms: Supplementary material}},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2012-011/}},
year = 2012
}
@incollection{IruLop2021gecco,
location = {Lille, France},
address = { New York, NY},
publisher = {ACM Press},
year = 2021,
editor = { Chicano, Francisco and Krzysztof Krawiec },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021},
author = { Irurozki, Ekhine and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Unbalanced Mallows Models for Optimizing Expensive Black-Box
Permutation Problems},
pages = {225--233},
doi = {10.1145/3449639.3459366},
supplement = {https://doi.org/10.5281/zenodo.4500974},
abstract = {Expensive black-box combinatorial optimization problems arise
in practice when the objective function is evaluated by means
of a simulator or a real-world experiment. Since each fitness
evaluation is expensive in terms of time or resources, only a
limited number of evaluations is possible, typically several
orders of magnitude smaller than in non-expensive
problems. In this scenario, classical optimization methods
such as mixed-integer programming and local search are not
useful. In the continuous case, Bayesian optimization, in
particular using Gaussian processes, has proven very
effective under these conditions. Much less research is
available in the combinatorial case. In this paper, we
propose and analyze UMM, an estimation-of-distribution (EDA)
algorithm based on a Mallows probabilistic model and
unbalanced rank aggregation (uBorda). Experimental results on
black-box versions of LOP and PFSP show that UMM is able to
match, and sometimes surpass, the solutions obtained by CEGO,
a Bayesian optimization algorithm for combinatorial
optimization. Moreover, the computational complexity of UMM
increases linearly with both the number of function
evaluations and the permutation size.},
keywords = {UMM, Permutation, Expensive, Black-box}
}
@incollection{IshMasNoj2015gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2015,
editor = {Sara Silva and Anna I. Esparcia{-}Alc{\'{a}}zar },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015},
author = { Ishibuchi, Hisao and Masuda, Hiroyuki and Nojima, Yusuke},
title = {A Study on Performance Evaluation Ability of a Modified
Inverted Generational Distance Indicator},
pages = {695--702}
}
@incollection{IshMasTanNoj2015igd,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2015 Part {I}},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 9018,
year = 2015,
publisher = {Springer},
editor = { Ant{\'o}nio Gaspar{-}Cunha and Carlos Henggeler Antunes and Carlos A. {Coello Coello} },
author = { Ishibuchi, Hisao and Masuda, Hiroyuki and Tanigaki, Yuki and
Nojima, Yusuke},
title = {Modified Distance Calculation in Generational Distance and
Inverted Generational Distance},
pages = {110--125},
annote = {Proposed IGD+},
keywords = {Performance metrics, multi-objective, IGD, IGD+}
}
@inproceedings{IshTsuNoj2008,
address = {Piscataway, NJ},
publisher = {IEEE Press},
year = 2008,
booktitle = {Proceedings of the 2008 Congress on Evolutionary Computation (CEC 2008)},
key = {IEEE CEC},
author = { Ishibuchi, Hisao and Tsukamoto, N. and Nojima, Y.},
title = {Evolutionary many-objective optimization: {A} short review},
doi = {10.1109/CEC.2008.4631121},
pages = {2419--2426}
}
@incollection{IzzGetHenSim2015evolving,
address = { New York, NY},
publisher = {ACM Press},
year = 2015,
editor = {Sara Silva and Anna I. Esparcia{-}Alc{\'{a}}zar },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015},
author = { Dario Izzo and Getzner, Ingmar and Hennes, Daniel and Sim{\~o}es, Lu{\'i}s F. },
title = {Evolving solutions to {TSP} variants for active space debris
removal},
pages = {1207--1214}
}
@incollection{IzzSimMar2013tour,
isbn = {978-1-4503-1963-8},
address = { New York, NY},
publisher = {ACM Press},
year = 2013,
editor = { Christian Blum and Alba, Enrique },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2013},
author = { Dario Izzo and Sim{\~o}es, Lu{\'i}s F. and M{\"a}rtens, Marcus and de
Croon, Guido C.H.E. and Heritier, Aurelie and Yam, Chit Hong},
title = {Search for a Grand Tour of the {Jupiter} {Galilean} Moons},
pages = {1301--1308},
doi = {10.1145/2463372.2463524}
}
@incollection{JacJouTal2014evapp,
year = 2014,
volume = 8602,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
booktitle = {Applications of Evolutionary Computation},
editor = { Anna I. Esparcia{-}Alc{\'{a}}zar and Antonio M. Mora},
author = {Sophie Jacquin and Laetitia Jourdan and Talbi, El-Ghazali },
title = {Dynamic Programming Based Metaheuristic for Energy Planning Problems},
pages = {165--176},
doi = {10.1007/978-3-662-45523-4_14},
keywords = {irace}
}
@incollection{JaiCoeCha2008,
address = { New York, NY},
publisher = {ACM Press},
year = 2008,
editor = {Conor Ryan},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2008},
title = {Objective reduction using a feature selection technique},
author = { L{\'o}pez Jaimes, Antonio and Carlos A. {Coello Coello} and Chakraborty, Debrup},
pages = {673--680}
}
@incollection{JaiCoeUri2009onlinered,
address = { Heidelberg, Germany},
publisher = {Springer},
year = 2009,
series = {Lecture Notes in Computer Science},
volume = 5467,
editor = { Matthias Ehrgott and Carlos M. Fonseca and Xavier Gandibleux and Jin-Kao Hao and Marc Sevaux },
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2009},
title = {Online Objective Reduction to Deal with Many-Objective
Problems},
author = { L{\'o}pez Jaimes, Antonio and Carlos A. {Coello Coello} and Ur{\'i}as Barrientos, Jes{\'u}s E.},
pages = {423--437},
abstract = {In this paper, we propose and analyze two schemes to
integrate an objective reduction technique into a
multi-objective evolutionary algorithm (moea) in order to
cope with many-objective problems. One scheme reduces
periodically the number objectives during the search until
the required objective subset size has been reached and,
towards the end of the search, the original objective set is
used again. The second approach is a more conservative scheme
that alternately uses the reduced and the entire set of
objectives to carry out the search. Besides improving
computational efficiency by removing some objectives, the
experimental results showed that both objective reduction
schemes also considerably improve the convergence of a moea
in many-objective problems.}
}
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publisher = {{JMLR}.org},
volume = 51,
series = {{JMLR} Workshop and Conference Proceedings},
year = 2016,
booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence
and Statistics, {AISTATS} 2016, Cadiz, Spain, May 9-11, 2016},
editor = {Arthur Gretton and Christian C. Robert},
author = {Jamieson, Kevin G. and Talwalkar, Ameet},
title = {Non-stochastic Best Arm Identification and Hyperparameter Optimization},
pages = {240--248},
url = {http://proceedings.mlr.press/v51/jamieson16.html}
}
@incollection{JasBran2008,
editor = { J{\"u}rgen Branke and Kalyanmoy Deb and Kaisa Miettinen and Roman S{\l}owi{\'n}ski },
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 5252,
year = 2008,
booktitle = {Multiobjective Optimization: Interactive and Evolutionary
Approaches},
author = { Andrzej Jaszkiewicz and J{\"u}rgen Branke },
title = {Interactive Multiobjective Evolutionary Algorithms},
pages = {179--193},
doi = {10.1007/978-3-540-88908-3_7}
}
@incollection{JasIshZha2012,
series = {Studies in Computational Intelligence},
publisher = {Springer},
year = 2011,
volume = 379,
editor = {Neri, Ferrante and Carlos Cotta and Pablo Moscato },
booktitle = {Handbook of Memetic Algorithms},
title = {Multiobjective memetic algorithms},
author = { Andrzej Jaszkiewicz and Ishibuchi, Hisao and Zhang, Qingfu },
pages = {201--217}
}
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title = {Manual for {SMAC}},
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year = 2015,
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@incollection{JesLieDerPaq2020gecco,
epub = {https://dl.acm.org/citation.cfm?id=3377930},
location = {Canc{\'u}n, Mexico},
doi = {10.1145/3377930},
isbn = {978-1-4503-7128-5},
address = { New York, NY},
publisher = {ACM Press},
year = 2020,
editor = { Carlos A. {Coello Coello} },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2020},
author = {Jesus, Alexandre D. and Arnaud Liefooghe and Bilel Derbel and Lu{\'i}s Paquete },
pages = {850---858},
title = {Algorithm Selection of Anytime Algorithms}
}
@misc{JoH2015:policy,
key = {{Journal of Heuristics}},
title = {{Journal of Heuristics. Policies on Heuristic Search Research}},
howpublished = {\url{http://www.springer.com/journal/10732}},
year = 2015,
note = {Version visited last on June 10, 2015}
}
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title = {Experimental Analysis of Heuristics for the {ATSP}},
pages = {445--487}
}
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editor = {G. Gutin and A. Punnen},
year = 2002,
publisher = {Kluwer Academic Publishers, Dordrecht, The Netherlands},
booktitle = {The Traveling Salesman Problem and its Variations},
author = {David S. Johnson and Lyle A. McGeoch },
title = {Experimental Analysis of Heuristics for the {STSP}},
pages = {369--443}
}
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address = { Chichester, UK},
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author = {David S. Johnson and Lyle A. McGeoch },
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Optimization},
pages = {215--310}
}
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@misc{Johnson2001,
author = {David S. Johnson and Lyle A. McGeoch and Rego, C. and Fred Glover },
title = {8th {DIMACS} Implementation Challenge: The Traveling Salesman Problem},
year = 2001,
howpublished = {\url{http://dimacs.rutgers.edu/archive/Challenges/TSP}},
keywords = {TSP Challenge, RUE, RCE, generators}
}
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editor = {Michael H. Goldwasser and David S. Johnson and Catherine C. McGeoch },
year = 2002,
address = { Providence, RI},
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Computer Science},
booktitle = {Data Structures, Near Neighbor Searches, and Methodology:
Fifth and Sixth {DIMACS} Implementation Challenges},
author = {David S. Johnson},
title = {A Theoretician's Guide to the Experimental Analysis of
Algorithms},
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}
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author = { De Jong, Kenneth A. },
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address = {Cambridge, MA},
year = 2006
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to Grammar Induction},
pages = {776--783}
}
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address = { Pittsburgh, PA},
booktitle = {Proceedings of the Sixth International Conference on Genetic Algorithms (ICGA'95)},
publisher = {Morgan Kaufmann Publishers, San Francisco, CA},
year = 1995,
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editor = {Coelho, H. and Studer, R. and Wooldridge, M.},
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title = {{ISAC}: Instance-Specific Algorithm Configuration},
pages = {751--756}
}
@inproceedings{KajIkeHaj2009cec,
address = {Piscataway, NJ},
publisher = {IEEE Press},
year = 2009,
booktitle = {Proceedings of the 2009 Congress on Evolutionary Computation (CEC 2009)},
key = {IEEE CEC},
author = {H. Kaji and Ikeda, Kokolo and Hajime Kita},
title = {Avoidance of constraint violation for experiment-based
evolutionary multi-objective optimization},
pages = {2756--2763},
keywords = {Safe Optimization, evolutionary computation, constraint
violation, experiment-based evolutionary multiobjective
optimization, evolutionary algorithm, risky-constraint
violation, Constraint optimization, Diesel engines,
Calibration, Evolutionary computation, Electric breakdown,
Optimization methods, Uncertainty, Computational fluid
dynamics},
doi = {10.1109/CEC.2009.4983288}
}
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address = { New York, NY},
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year = 2014,
editor = {Christian Igel and Dirk V. Arnold},
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author = {Karafotias, Giorgos and Agoston E. Eiben and Mark Hoogendoorn},
title = {Generic parameter control with reinforcement learning},
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year = 2015,
volume = 9028,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
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editor = {Antonio M. Mora and Squillero, Giovanni},
title = {Evaluating reward definitions for parameter control},
author = {Karafotias, Giorgos and Hoogendoorn, Mark and Agoston E. Eiben },
pages = {667--680},
doi = {10.1007/978-3-319-16549-3_54}
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title = {Almost optimal exploration in multi-armed bandits},
author = {Karnin, Zohar and Koren, Tomer and Somekh, Oren},
pages = {1238--1246},
annote = {Sequential Halving, Successive Halving}
}
@incollection{KarParStu2018lion,
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editor = { Roberto Battiti and Mauro Brunato and Ilias Kotsireas and Panos M. Pardalos },
title = {Algorithm Configuration: Learning policies for the quick
termination of poor performers},
author = {Karapetyan, Daniel and Andrew J. Parkes and Thomas St{\"u}tzle },
pages = {220--224},
doi = {10.1007/978-3-030-05348-2_20}
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publisher = {Springer},
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editor = {Di Chio, Cecillia and others},
title = {A generic approach to parameter control},
author = {Karafotias, Giorgos and Smit, Selmar K. and Agoston E. Eiben },
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doi = {10.1007/978-3-642-29178-4_37}
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title = {Reducibility among combinatorial problems},
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and Max H. Garzon and Vasant Honavar and Mark
J. Jakiela and Robert E. Smith},
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author = {Liangjun Ke and Zuren Feng and Zongben Xu and Ke Shang and
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Evolutionary Computation, GECCO 2001},
title = {An adaptive genetic algorithm},
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doi = {10.1109/CEC.2007.4425062}
}
@incollection{KelPol2008ae,
address = { Heidelberg, Germany},
publisher = {Springer},
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year = 2017,
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@inproceedings{KenEbe1997binpso,
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year = 2001
}
@book{Kendall1948,
author = {Maurice G. Kendall},
title = {Rank correlation methods},
publisher = {Griffin},
address = {London},
year = 1948
}
@inproceedings{KerTra2016cec,
year = 2016,
isbn = {978-1-5090-0623-6},
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2016 Congress on Evolutionary Computation (CEC 2016)},
key = {IEEE CEC},
author = { Pascal Kerschke and Heike Trautmann },
title = {The \proglang{R}-package {FLACCO} for exploratory landscape
analysis with applications to multi-objective optimization
problems},
pages = {5262--5269},
doi = {10.1109/CEC.2016.7748359}
}
@incollection{KerWanPre2016ppsn,
isbn = {978-3-319-45822-9},
year = 2016,
volume = 9921,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Julia Handl and Emma Hart and Lewis, P. R. and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Gabriela Ochoa and Ben Paechter },
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XIV}},
author = { Pascal Kerschke and Wang, Hao and Mike Preuss and Christian
Grimme and Andr{\'{e}} H. Deutz and Heike Trautmann and Emmerich, Michael T. M. },
title = {Towards Analyzing Multimodality of Continuous Multiobjective
Landscapes},
pages = {962--972},
doi = {10.1007/978-3-319-45823-6_90}
}
@misc{Keras,
author = {Chollet, Fran\c{c}ois and others},
title = {Keras},
howpublished = {\url{https://keras.io}},
year = 2015
}
@incollection{Kerrisk05:POSIX-threads,
author = {M. Kerrisk},
title = {pthreads - {POSIX} Threads},
booktitle = {Linux Programmer's Manual},
publisher = {\url{https://man7.org/linux/man-pages/man7/pthreads.7.html}},
year = 2021,
type = {{Section}},
chapter = 7,
note = {(Last accessed Feb 22 2023)}
}
@incollection{KhaYaoDeb2003,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 2632,
series = {Lecture Notes in Computer Science},
editor = { Carlos M. Fonseca and Peter J. Fleming and Eckart Zitzler and Kalyanmoy Deb and Lothar Thiele },
year = 2003,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2003},
author = {Khare, V. and Xin Yao and Kalyanmoy Deb },
title = {Performance Scaling of Multi-objective Evolutionary Algorithms},
pages = {376--390}
}
@incollection{KhiAlbSol08,
volume = 5217,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and Mauro Birattari and Christian Blum and Clerc, Maurice and Thomas St{\"u}tzle and A. F. T. Winfield},
year = 2008,
booktitle = {Ant Colony Optimization and Swarm Intelligence, 6th
International Conference, ANTS 2008},
author = {M. Khichane and P. Albert and Christine Solnon },
title = {Integration of {ACO} in a Constraint Programming
Language},
pages = {84--95}
}
@incollection{KhiAlbSol09,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 5851,
booktitle = {Learning and Intelligent Optimization, Third International Conference, LION 3},
publisher = {Springer},
year = 2009,
editor = { Thomas St{\"u}tzle },
author = {M. Khichane and P. Albert and Christine Solnon },
title = {An {ACO}-Based Reactive Framework for Ant Colony
Optimization: First Experiments on Constraint
Satisfaction Problems},
doi = {10.1007/978-3-642-11169-3_9},
pages = {119--133}
}
@inproceedings{KhuXuHooLey2009:satenstein,
publisher = {AAAI Press, Menlo Park, CA},
editor = {Craig Boutilier},
year = 2009,
booktitle = {Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI-09)},
author = { KhudaBukhsh, A. R. and Lin Xu and Holger H. Hoos and Kevin Leyton-Brown },
title = {{SATenstein}: Automatically Building Local Search
{SAT} Solvers from Components},
pages = {517--524},
epub = {http://ijcai.org/papers09/Papers/IJCAI09-093.pdf}
}
@incollection{KimAllLop2020safe,
year = 2021,
volume = 12641,
address = { Cham, Switzerland},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
booktitle = {Trustworthy AI -- Integrating Learning, Optimization and
Reasoning. TAILOR 2020},
editor = {Fredrik Heintz and Michela Milano and O'Sullivan, Barry },
author = { Kim, Youngmin and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Safe Learning and Optimization Techniques: Towards a Survey
of the State of the Art},
pages = {123--139},
doi = {10.1007/978-3-030-73959-1_12},
abstract = {Safe learning and optimization deals with learning and
optimization problems that avoid, as much as possible, the
evaluation of non-safe input points, which are solutions,
policies, or strategies that cause an irrecoverable loss
(e.g., breakage of a machine or equipment, or life
threat). Although a comprehensive survey of safe
reinforcement learning algorithms was published in 2015, a
number of new algorithms have been proposed thereafter, and
related works in active learning and in optimization were not
considered. This paper reviews those algorithms from a number
of domains including reinforcement learning, Gaussian process
regression and classification, evolutionary computing, and
active learning. We provide the fundamental concepts on which
the reviewed algorithms are based and a characterization of
the individual algorithms. We conclude by explaining how the
algorithms are connected and suggestions for future
research.}
}
@incollection{KimAllLop2022easafe,
location = {Boston, Massachusetts},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2022},
address = { New York, NY},
year = 2022,
publisher = {ACM Press},
editor = { Jonathan E. Fieldsend and Markus Wagner },
author = { Kim, Youngmin and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Are Evolutionary Algorithms Safe Optimizers?},
doi = {10.1145/3512290.3528818},
abstract = {We consider a type of constrained optimization problem, where
the violation of a constraint leads to an irrevocable loss,
such as breakage of a valuable experimental resource/platform
or loss of human life. Such problems are referred to as safe
optimization problems (SafeOPs). While SafeOPs have received
attention in the machine learning community in recent years,
there was little interest in the evolutionary computation
(EC) community despite some early attempts between 2009 and
2011. Moreover, there is a lack of acceptable guidelines on
how to benchmark different algorithms for SafeOPs, an area
where the EC community has significant experience in. Driven
by the need for more eficient algorithms and benchmark
guidelines for SafeOPs, the objective of this paper is to
reignite the interest of the EC community in this problem
class. To achieve this we (i) provide a formal definition of
SafeOPs and contrast it to other types of optimization
problems that the EC community is familiar with, (ii)
investigate the impact of key SafeOP parameters on the
performance of selected safe optimization algorithms, (iii)
benchmark EC against state-of-the-art safe optimization
algorithms from the machine learning community, and (iv)
provide an open-source Python framework to replicate and
extend our work.},
pages = {814--822},
numpages = 9,
keywords = {Bayesian optimization, constrained optimization,
benchmarking, safety constraints, safe optimization}
}
@inproceedings{KimParKim2021learning,
editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P. S. Liang
and J. Wortman Vaughan},
booktitle = {Advances in Neural Information Processing Systems 34 (NeurIPS
2021)},
year = 2021,
author = {Kim, Minsu and Park, Jinkyoo and Kim, Joungho},
title = {Learning Collaborative Policies to Solve {NP-hard} Routing
Problems},
epub = {https://papers.nips.cc/paper_files/paper/2021/hash/564127c03caab942e503ee6f810f54fd-Abstract.html},
keywords = {Deep RL, TSP, prize collecting, PCTSP, CVRP, routing,
attention model}
}
@inproceedings{KinBa2015adam,
editor = { Bengio, Yoshua and Yann {LeCun}},
booktitle = {3rd International Conference on Learning Representations,
{ICLR} 2015, San Diego, CA, USA, May 7-9, 2015, Conference
Track Proceedings},
year = 2015,
author = {Diederik P. Kingma and Jimmy Ba},
title = {Adam: {A} Method for Stochastic Optimization}
}
@inproceedings{Kno2005:isda,
year = 2005,
booktitle = {Proceedings of the 5th International Conference on
Intelligent Systems Design and Applications},
editor = {Abraham, Ajith and Paprzycki, Marcin},
author = { Joshua D. Knowles },
title = {A summary-attainment-surface plotting method for visualizing
the performance of stochastic multiobjective optimizers},
pages = {552--557},
supplement = {https://www.cs.bham.ac.uk/~jdk/plot_attainments/},
doi = {10.1109/ISDA.2005.15},
abstract = {When evaluating the performance of a stochastic optimizer it
is sometimes desirable to express performance in terms of the
quality attained in a certain fraction of sample runs. For
example, the sample median quality is the best estimator of
what one would expect to achieve in 50\% of runs, and
similarly for other quantiles. In multiobjective
optimization, the notion still applies but the outcome of a
run is measured not as a scalar (i.e. the cost of the best
solution), but as an attainment surface in $k$-dimensional
space (where $k$ is the number of objectives). In this paper
we report an algorithm that can be conveniently used to plot
summary attainment surfaces in any number of dimensions
(though it is particularly suited for three). A summary
attainment surface is defined as the union of all tightest
goals that have been attained (independently) in precisely
$s$ of the runs of a sample of $n$ runs, for any $s \in
1\ldots n$, and for any $k$. We also discuss the
computational complexity of the algorithm and give some
examples of its use. C code for the algorithm is available
from the author.}
}
@inproceedings{KnoCor1999cec,
address = {Piscataway, NJ},
publisher = {IEEE Press},
year = 1999,
booktitle = {Proceedings of the 1999 Congress on Evolutionary Computation
(CEC 1999)},
key = {IEEE CEC},
author = { Joshua D. Knowles and David Corne },
pages = {98--105},
title = {The {Pareto} Archived Evolution Strategy: A New
Baseline Algorithm for Multiobjective Optimisation},
annote = {first mention of Adaptive Grid Archiving}
}
@inproceedings{KnoCor2000mpaes,
month = jul,
address = {Piscataway, NJ},
publisher = {IEEE Press},
year = 2000,
booktitle = {Proceedings of the 2000 Congress on Evolutionary Computation (CEC'00)},
key = {IEEE CEC},
title = {{M-PAES}: A memetic algorithm for multiobjective
optimization},
author = { Joshua D. Knowles and David Corne },
pages = {325--332}
}
@inproceedings{KnoCor2002cec,
year = 2002,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2002 Congress on Evolutionary Computation (CEC'02)},
key = {IEEE CEC},
author = { Joshua D. Knowles and David Corne },
title = {On Metrics for Comparing Non-Dominated Sets},
pages = {711--716}
}
@incollection{KnoCor2003emo,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 2632,
series = {Lecture Notes in Computer Science},
editor = { Carlos M. Fonseca and Peter J. Fleming and Eckart Zitzler and Kalyanmoy Deb and Lothar Thiele },
year = 2003,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2003},
author = { Joshua D. Knowles and David Corne },
title = {Instance Generators and Test Suites for the
Multiobjective Quadratic Assignment Problem},
pages = {295--310}
}
@incollection{KnoCor2004lnems,
year = 2004,
address = {Berlin\slash Heidelberg},
publisher = {Springer},
volume = 535,
series = {Lecture Notes in Economics and Mathematical Systems},
editor = { Xavier Gandibleux and Marc Sevaux and Kenneth S{\"o}rensen and V. {T'Kindt} },
booktitle = {Metaheuristics for Multiobjective Optimisation},
author = { Joshua D. Knowles and David Corne },
title = {Bounded {Pareto} Archiving: {Theory} and Practice},
pages = {39--64},
doi = {10.1007/978-3-642-17144-4_2}
}
@incollection{KnoCor2005mem,
address = {Berlin\slash Heidelberg},
publisher = {Springer},
series = {Studies in Fuzziness and Soft Computing},
volume = 166,
year = 2005,
editor = {Hart W. E. and Smith J. E. and Krasnogor N.},
booktitle = {Recent Advances in Memetic Algorithms},
title = {Memetic algorithms for multiobjective optimization: issues,
methods and prospects},
author = { Joshua D. Knowles and David Corne },
pages = {313--352},
doi = {10.1007/3-540-32363-5_14}
}
@incollection{KnoCor2007emo,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 4403,
fulleditor = {Obayashi, Shigeru and Kalyanmoy Deb and Poloni, Carlo and Hiroyasu, Tomoyuki and Murata, Tadahiko},
editor = {S. Obayashi and others},
year = 2007,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2007},
author = { Joshua D. Knowles and David Corne },
title = {Quantifying the Effects of Objective Space Dimension in
Evolutionary Multiobjective Optimization},
pages = {757--771},
abstract = {The scalability of EMO algorithms is an issue of significant
concern for both algorithm developers and users. A key aspect
of the issue is scalability to objective space dimension,
other things being equal. Here, we make some observations
about the efficiency of search in discrete spaces as a
function of the number of objectives, considering both
uncorrelated and correlated objective values. Efficiency is
expressed in terms of a cardinality-based
(scaling-independent) performance indicator. Considering
random sampling of the search space, we measure, empirically,
the fraction of the true PF covered after p iterations, as
the number of objectives grows, and for different
correlations. A general analytical expression for the
expected performance of random search is derived, and is
shown to agree with the empirical results. We postulate that
for even moderately large numbers of objectives, random
search will be competitive with an EMO algorithm and show
that this is the case empirically: on a function where each
objective is relatively easy for an EA to optimize (an
NK-landscape with K=2), random search compares favourably to
a well-known EMO algorithm when objective space dimension is
ten, for a range of inter-objective correlation values. The
analytical methods presented here may be useful for
benchmarking of other EMO algorithms.}
}
@incollection{KnoCorDeb2008,
address = {Berlin\slash Heidelberg},
publisher = {Springer},
series = {Natural Computing Series},
editor = { Joshua D. Knowles and David Corne and Kalyanmoy Deb and Chair, Deva Raj},
year = 2008,
booktitle = {Multiobjective Problem Solving from Nature},
title = {Introduction: {Problem} solving, {EC} and {EMO}},
author = { Joshua D. Knowles and David Corne and Kalyanmoy Deb },
pages = {1--28},
doi = {10.1007/978-3-540-72964-8_1}
}
@inproceedings{KnoCorFle2003,
year = 2003,
address = {Piscataway, NJ},
publisher = {IEEE Press},
month = dec,
booktitle = {Proceedings of the 2003 Congress on Evolutionary Computation (CEC'03)},
key = {IEEE CEC},
title = {Bounded archiving using the {Lebesgue} measure},
author = { Joshua D. Knowles and David Corne and Fleischer, Mark},
pages = {2490--2497}
}
@incollection{KnoCorRey09emo,
address = { Heidelberg, Germany},
publisher = {Springer},
year = 2009,
series = {Lecture Notes in Computer Science},
volume = 5467,
editor = { Matthias Ehrgott and Carlos M. Fonseca and Xavier Gandibleux and Jin-Kao Hao and Marc Sevaux },
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2009},
author = { Joshua D. Knowles and David Corne and Alan P. Reynolds},
title = {Noisy Multiobjective Optimization on a Budget of 250 Evaluations},
pages = {36--50}
}
@techreport{KnoThiZit06:tutorial,
author = { Joshua D. Knowles and Lothar Thiele and Eckart Zitzler },
title = {A tutorial on the performance assessment of stochastic
multiobjective optimizers},
institution = {Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Z{\"u}rich, Switzerland},
year = 2006,
type = {TIK-Report},
number = 214,
month = feb,
note = {Revised version},
epub = {https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/23822/2/KTZ2006a.pdf}
}
@incollection{KnoWatCor2001reducing,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2001},
address = {Berlin\slash Heidelberg},
series = {Lecture Notes in Computer Science},
volume = 1993,
year = 2001,
publisher = {Springer},
editor = { Eckart Zitzler and Kalyanmoy Deb and Lothar Thiele and Carlos A. {Coello Coello} and David Corne },
author = { Joshua D. Knowles and Watson, Richard A. and David Corne },
title = {Reducing Local Optima in Single-Objective Problems by
Multi-objectivization},
pages = {269--283},
doi = {10.1007/3-540-44719-9_19},
annote = {Proposed multi-objectivization}
}
@phdthesis{Knowles2002PhD,
author = { Joshua D. Knowles },
title = {Local-Search and Hybrid Evolutionary Algorithms for
{Pareto} Optimization},
school = {University of Reading, UK},
annote = {(Examiners: Prof. K. Deb and Prof. K. Warwick)},
year = 2002
}
@book{KolFri2009,
title = {Probabilistic graphical models: principles and techniques},
author = {Koller, Daphne and Friedman, Nir},
year = {2009},
publisher = {MIT Press}
}
@inproceedings{KopYos2007visualization,
title = {Visualization of {Pareto}-sets in evolutionary
multi-objective optimization},
author = {Koppen, Mario and Yoshida, Kaori},
booktitle = {7th International Conference on Hybrid Intelligent Systems
(HIS 2007)},
pages = {156--161},
year = 2007,
organization = {IEEE}
}
@inproceedings{KorSilOblKos07:cec,
address = {Piscataway, NJ},
publisher = {IEEE Press},
year = 2007,
booktitle = {Proceedings of the 2007 Congress on Evolutionary Computation (CEC 2007)},
key = {IEEE CEC},
author = { P. Koro{\v s}ec and Jurij {\v S}ilc and K. Oblak and F. Kosel},
title = {The differential ant-stigmergy algorithm: an
experimental evaluation and a real-world
application},
pages = {157--164}
}
@incollection{KorSilRob04:ants2004,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 3172,
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and L. M. Gambardella and Francesco Mondada and Thomas St{\"u}tzle and Mauro Birattari and Christian Blum },
year = 2004,
booktitle = {Ant Colony Optimization and Swarm Intelligence, 4th
International Workshop, ANTS 2004 },
author = { P. Koro{\v s}ec and Jurij {\v S}ilc and B. Robi{\v c}},
title = {Mesh-Partitioning with the Multiple Ant-Colony
Algorithm},
pages = {430--431}
}
@incollection{KorStuExn06,
address = { Heidelberg, Germany},
publisher = {Springer},
year = 2006,
volume = 4150,
series = {Lecture Notes in Computer Science},
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and L. M. Gambardella and Mauro Birattari and
Martinoli, A. and Poli, R. and Thomas St{\"u}tzle },
booktitle = {Ant Colony Optimization and Swarm Intelligence, 5th
International Workshop, ANTS 2006},
author = { Oliver Korb and Thomas St{\"u}tzle and Thomas E. Exner },
title = {{PLANTS}: {Application} of ant colony optimization
to structure-based drug design},
pages = {247--258},
doi = {10.1007/11839088_22}
}
@incollection{KorWall1997behavioral,
author = { Pekka Korhonen and Wallenius, Jyrki },
title = {Behavioral Issues in {MCDM}: {Neglected} Research Questions},
booktitle = {Multicriteria Analysis},
publisher = {Springer},
year = 1997,
editor = { Jo{\~ao} Cl{\'i}maco },
pages = {412--422},
address = {Berlin\slash Heidelberg},
isbn = {978-3-642-60667-0},
shorttitle = {Behavioral Issues in {MCDM}},
doi = {10.1007/978-3-642-60667-0_39},
abstract = {Behavior decision theorists have studied human decision
making in great detail. Since the late 1960's, Einhorn,
Edwards, Kahneman, Roy, Trevsky, and others have developed
new thoeries to explain choice and decision behavior. Thus
far this behavior research has had little impact on multiple
criteria decision making (MCDM). Only a handful of
MCDM-research have critically examined the behavioral
underpinnings of our field. To improve the success of
decision tools in practice, MCDM-research should pay more
attention to the behavioral realities of decision making. In
this paper, we discuss various behavioral issues relevent for
MCDM based on our personal observations and experiments with
human subjects. The spirit of our paper is to pose questions
rather than provide definite answers.},
language = {en},
keywords = {Aspiration Level, Decision Tool, Nondominated Solution,
Prefer Solution, Prospect Theory}
}
@incollection{KosVerDoe2021,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2021},
address = { New York, NY},
year = 2021,
publisher = {ACM Press},
editor = { Chicano, Francisco and Krzysztof Krawiec },
author = {Kostovska, Ana and Diederick Vermetten and Carola Doerr and D\v{z}eroski, Sa\v{s}o and Panov, Pan\v{c}e and Tome Eftimov },
title = {{OPTION}: optimization algorithm benchmarking ontology},
pages = {239--240}
}
@incollection{KosVerDze2023:evoapp,
volume = 13989,
series = {Lecture Notes in Computer Science},
address = {Switzerland},
publisher = {Springer Nature},
booktitle = {EvoApplications 2023: Applications of Evolutionary Computation},
year = 2023,
editor = {Correia, Jo\~{a}o and Smith, Stephen and Qaddoura, Raneem},
title = {Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms},
author = {Kostovska, Ana and Diederick Vermetten and D{\v{z}}eroski, Sa{\v{s}}o and Panov, Pan{\v{c}}e and Tome Eftimov and Carola Doerr },
pages = {253--268}
}
@book{KouYu1997:robustopt,
author = { P. Kouvelis and G. Yu },
title = {Robust discrete optimization and its applications},
publisher = {Kluwer Academic Publishers, Dordrecht, The Netherlands},
series = {Nonconvex optimization and its applications},
year = 1997
}
@incollection{KovSkr08,
adoi = {10.1007/978-3-540-87536-9},
booktitle = {ICANN'08: Proceedings of the 18th International Conference on
Artificial Neural Networks, Part I},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 5163,
year = 2008,
publisher = {Springer},
editor = {Kurkova-Pohlova, Vera and Koutnik, Jan},
author = {O. Kov\'{a}\v{r}{\'i}k and M. Skrbek},
title = {Ant Colony Optimization with Castes},
pages = {435--442}
}
@incollection{KozEchLei2011,
address = {Berlin\slash Heidelberg},
series = {Studies in Computational Intelligence},
volume = 356,
booktitle = {Computational Optimization, Methods and Algorithms},
publisher = {Springer},
year = 2011,
editor = {Slawomir Koziel and Xin-She Yang},
author = {Slawomir Koziel and David Echeverr{\'i}a Ciaurri and Leifur Leifsson},
title = {Surrogate-Based Methods},
pages = {33--59}
}
@book{Koza1992gp,
author = {J. Koza},
title = {Genetic Programming: On the Programming of Computers
By the Means of Natural Selection},
publisher = {MIT Press},
address = {Cambridge, MA},
year = 1992
}
@incollection{KraGlaHan2016unbounded,
address = { New York, NY},
publisher = {ACM Press},
year = 2016,
editor = { Tobias Friedrich and Frank Neumann and Andrew M. Sutton },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2016},
title = {Unbounded population {MO-CMA-ES} for the bi-objective {BBOB}
test suite},
author = {Krause, Oswin and T. Glasmachers and Nikolaus Hansen and Christian Igel },
pages = {1177--1184},
keywords = {archiving}
}
@incollection{KraGloGoe2007,
address = { New York, NY},
publisher = {ACM Press},
year = 2007,
editor = {Dirk Thierens and others},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007},
author = {Kramer, Oliver and Gloger, Bartek and Goebels, Andreas},
title = {An Experimental Analysis of Evolution Strategies and
Particle Swarm Optimisers Using Design of Experiments},
pages = {674--681}
}
@inproceedings{KraHeiMil++2013,
author = { Krajzewicz, Daniel and Marek Heinrich and Michela Milano and Paolo Bellavista and Thomas St{\"u}tzle and J{\'e}r{\^o}me H{\"a}rri and Thrasyvoulos Spyropoulos and Robbin Blokpoel and Stefan Hausberger and Martin Fellendorf},
title = {{COLOMBO}: Investigating the Potential of {V2X} for Traffic Management Purposes assuming low penetration Rates},
booktitle = {Proceedings of ITS Europe 2013},
year = 2013,
address = {Dublin, Ireland}
}
@incollection{KraLeiBloMilStu2016,
author = { Krajzewicz, Daniel and Andreas Leich and Robbin Blokpoel and Michela Milano and Thomas St{\"u}tzle },
title = {{COLOMBO}: Exploiting Vehicular Communications at Low Equipment Rates for Traffic Management Purposes},
booktitle = {Advanced Microsystems for Automotive Applications 2015: Smart Systems for Green and Automated Driving},
publisher = {Springer International Publishing},
year = 2016,
editor = {Tim Schulze and Beate M{\"u}ller and Gereon Meyer},
pages = {117--130},
address = {Cham, Switzerland}
}
@incollection{KraPru1978,
author = {Jakob Krarup and Peter Mark Pruzan},
title = {Computer-aided Layout Design},
booktitle = {Mathematical Programming in Use},
publisher = {Springer},
address = {Berlin\slash Heidelberg},
year = 1978,
editor = {M. L. Balinski and C. Lemarechal},
volume = 9,
series = {Mathematical Programming Studies},
pages = {75--94}
}
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author = { Johannes Krettek and Jan Braun and Frank Hoffmann and Torsten Bertram },
title = {Interactive Incorporation of User Preferences in Multiobjective Evolutionary Algorithms},
pages = {379--388}
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editor = { Eyke H{\"u}llermeier and Rudolf Kruse and Frank Hoffmann },
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Conference, {IPMU2010}},
author = { Johannes Krettek and Jan Braun and Frank Hoffmann and Torsten Bertram },
title = {Preference Modeling and Model Management for Interactive Multi-objective Evolutionary Optimization},
pages = {574--583}
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pages = {443--450}
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publisher = {ACM Press},
year = 2010,
editor = { Michael Rappa and Paul Jones and Juliana Freire and Soumen Chakrabarti },
booktitle = {Proceedings of the 19th International Conference on World Wide Web, WWW 2010},
author = { Ravi Kumar and Sergei Vassilvitskii },
title = {Generalized Distances between Rankings}
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address = {Berlin\slash Heidelberg},
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publisher = {Springer},
editor = { Hans-Paul Schwefel and R. M{\"a}nner},
year = 1991,
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {I}},
title = {A variant of evolution strategies for vector optimization},
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annote = {Proposed KUR benchmark}
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key = {IEEE CEC},
author = {Benjamin Lacroix and Daniel Molina and Francisco Herrera },
title = {Dynamically updated region based memetic algorithm
for the 2013 {CEC} Special Session and Competition
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author = {Larra{\~n}aga, Pedro and Jos{\'e} A. Lozano },
title = {Estimation of Distribution Algorithms: A New Tool for
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publisher = {Kluwer Academic Publishers},
address = { Boston, MA},
year = 2002
}
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publisher = {Morgan Kaufmann Publishers, San Francisco, CA},
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year = 2002,
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2002},
title = {Archiving with guaranteed convergence and diversity in
multi-objective optimization},
author = { Marco Laumanns and Lothar Thiele and Eckart Zitzler and Kalyanmoy Deb },
pages = {439--447}
}
@unpublished{LauZen2010prep,
author = { Marco Laumanns and Zenklusen, Rico},
title = {Stochastic convergence of random search methods to fixed size
{Pareto} front approximations},
note = {(submitted)},
month = nov,
year = 2010,
annote = {Published as~\cite{LauZen2011ejor}. Keep this reference for historical reasons.}
}
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year = 2000,
booktitle = {Proceedings of the 2000 Congress on Evolutionary Computation (CEC'00)},
key = {IEEE CEC},
author = { Marco Laumanns and Eckart Zitzler and Lothar Thiele },
title = {A unified model for multi-objective evolutionary algorithms
with elitism},
pages = {46--53}
}
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author = {E. L. Lawler and J. K. Lenstra and A. H. G. {Rinnooy Kan} and
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doi = {10.1007/978-3-642-30665-5_9},
title = {Ant Colony Based Algorithms for Dynamic Optimization
Problems},
publisher = {Springer},
address = {Berlin\slash Heidelberg},
pages = {189--210},
language = {English}
}
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(CEC 1999)},
key = {IEEE CEC},
author = { Leguizam\'{o}n, Guillermo and Zbigniew Michalewicz },
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pages = {1459--1464}
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publisher = {RAND},
year = 2003
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@incollection{LesDumStu04:ants,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 3172,
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and L. M. Gambardella and Francesco Mondada and Thomas St{\"u}tzle and Mauro Birattari and Christian Blum },
year = 2004,
booktitle = {Ant Colony Optimization and Swarm Intelligence, 4th
International Workshop, ANTS 2004 },
author = {L. Lessing and Irina Dumitrescu and Thomas St{\"u}tzle },
title = {A Comparison Between {ACO} Algorithms for the Set
Covering Problem},
pages = {1--12}
}
@book{Lewis2016,
author = { Lewis, Rhyd M. R. },
title = {A Guide to Graph Colouring: Algorithms and Applications},
year = 2016,
publisher = {Springer},
address = { Cham, Switzerland},
annote = {Supplementary material available at \cite{Lewis2016sup}},
doi = {10.1007/978-3-319-25730-3}
}
@misc{Lewis2016sup,
author = { Lewis, Rhyd M. R. },
title = {Suite of Graph Colouring Algorithms -- Supplementary Material
to the Book ``{A} Guide to Graph Colouring: Algorithms and
Applications''},
howpublished = {\url{http://rhydlewis.eu/resources/gCol.zip}},
year = 2016
}
@inproceedings{LeyNudAnd2003ijcai,
booktitle = {Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI-03)},
epub = {http://ijcai.org/proceedings/2003},
year = 2003,
publisher = {Morgan Kaufmann Publishers},
editor = {Georg Gottlob and Toby Walsh},
author = { Kevin Leyton-Brown and Nudelman, Eugene and Andrew, Galen and
McFadden, Jim and Shoham, Yoav},
title = {A Portfolio Approach to Algorithm Selection},
pages = {1542--1543},
annote = {First example of modern algorithm selection for
optimisation?}
}
@incollection{LeyNudSho2002,
year = 2002,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
booktitle = {Principles and Practice of Constraint Programming, CP 2002},
editor = {van Hentenryck, Pascal },
title = {Learning the Empirical Hardness of Optimization Problems: The
Case of Combinatorial Auctions},
author = { Kevin Leyton-Brown and Nudelman, Eugene and Shoham, Yoav},
pages = {556--572}
}
@incollection{LeyPeaSho2000acmec,
editor = {Anant Jhingran and others},
address = { New York, NY},
publisher = {ACM Press},
year = 2000,
booktitle = {ACM Conference on Electronic Commerce (EC-00)},
author = { Kevin Leyton-Brown and M. Pearson and Y. Shoham},
title = {Towards a Universal Test Suite for Combinatorial Auction
Algorithms},
pages = {66--76},
doi = {10.1145/352871.352879},
annote = {CPLEX-regions200 benchmark set,
\url{http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/results.html}}
}
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year = 2014,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2014 Congress on Evolutionary Computation (CEC 2014)},
key = {IEEE CEC},
title = {An Improved Two Archive Algorithm for Many-Objective
Optimization},
author = {Li, Bingdong and Li, Jinlong and Tang, Ke and Xin Yao },
pages = {2869--2876}
}
@inproceedings{LiWanYuZhaLi08,
author = {Z. Li and Y. Wang and J. Yu and Y. Zhang and X. Li},
title = {A Novel Cloud-Based Fuzzy Self-Adaptive Ant Colony
System},
booktitle = {ICNC'08: Proceedings of the 2008 Fourth
International Conference on Natural Computation},
volume = 7,
year = 2008,
pages = {460--465},
publisher = {IEEE Computer Society},
address = {Washington, DC}
}
@incollection{LiYanLiu2015pci,
address = { New York, NY},
publisher = {ACM Press},
year = 2015,
editor = {Sara Silva and Anna I. Esparcia{-}Alc{\'{a}}zar },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015},
title = {A performance comparison indicator for {Pareto} front
approximations in many-objective optimization},
author = { Li, Miqing and Yang, Shengxiang and Liu, Xiaohui},
pages = {703--710},
annote = {Proposed PCI indicator}
}
@incollection{LiYanLiuShe2013many,
isbn = {978-3-642-37139-4},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2013},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 7811,
year = 2013,
publisher = {Springer},
editor = { Robin C. Purshouse and Peter J. Fleming and Carlos M. Fonseca and Salvatore Greco and Jane Shaw},
title = {A Comparative Study on Evolutionary Algorithms for
Many-Objective Optimization},
author = { Li, Miqing and Yang, Shengxiang and Liu, Xiaohui and Shen,
Ruimin},
pages = {261--275}
}
@incollection{LiYao2019emo,
isbn = {978-3-030-12597-4},
year = 2019,
address = { Cham, Switzerland},
publisher = {Springer International Publishing},
volume = 11411,
series = {Lecture Notes in Computer Science},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2019},
editor = { Kalyanmoy Deb and Erik D. Goodman and Carlos A. {Coello Coello} and Kathrin
Klamroth and Kaisa Miettinen and Sanaz Mostaghim and Patrick
Reed},
author = { Li, Miqing and Xin Yao },
title = {An Empirical Investigation of the Optimality and Monotonicity
Properties of Multiobjective Archiving Methods},
pages = {15--26},
doi = {10.1007/978-3-030-12598-1_2}
}
@incollection{LiYevBas2017,
editor = {Heike Trautmann and G{\"{u}}nter Rudolph and Kathrin Klamroth
and Oliver Sch{\"{u}}tze and Margaret M. Wiecek and Yaochu
Jin and Christian Grimme},
year = 2017,
volume = 10173,
series = {Lecture Notes in Computer Science},
address = { Cham, Switzerland},
publisher = {Springer International Publishing},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2017},
author = {Li, Longmei and Yevseyeva, Iryna and Basto-Fernandes,
Vitor and Heike Trautmann and Jing, Ning and Emmerich, Michael T. M. },
title = {Building and using an ontology of preference-based
multiobjective evolutionary algorithms},
pages = {406--421}
}
@incollection{LiaMonDogStuDor2011gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2011,
editor = {Natalio Krasnogor and Pier Luca Lanzi},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011},
author = {Liao, Tianjun and Marco A. {Montes de Oca} and Do\v{g}an Ayd{\i}n and Thomas St{\"u}tzle and Marco Dorigo },
title = {An Incremental Ant Colony Algorithm with Local Search for Continuous Optimization},
pages = {125--132}
}
@incollection{LiaMonStu2011gecco,
year = 2011,
address = { New York, NY},
publisher = {ACM Press},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2011},
editor = {Natalio Krasnogor and Pier Luca Lanzi},
author = {Liao, Tianjun and Marco A. {Montes de Oca} and Thomas St{\"u}tzle },
title = {Tuning Parameters across Mixed Dimensional
Instances: A Performance Scalability Study of
{Sep-G-CMA-ES}},
annote = {Workshop on Scaling Behaviours of Landscapes,
Parameters and Algorithms},
pages = {703--706}
}
@inproceedings{LiaStu2013cec,
year = 2013,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2013 Congress on Evolutionary Computation (CEC 2013)},
key = {IEEE CEC},
author = {Liao, Tianjun and Thomas St{\"u}tzle },
title = {Benchmark results for a simple hybrid algorithm on
the {CEC} 2013 benchmark set for real-parameter
optimization},
pages = {1938--1944}
}
@phdthesis{Liao2013,
author = {Liao, Tianjun },
title = {Population-based Heuristic Algorithms for Continuous and Mixed Discrete-Continuous Optimization Problem},
school = {IRIDIA, {\'E}cole polytechnique, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2013
}
@incollection{LieDerVerAguTan2017evocop,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 10197,
series = {Lecture Notes in Computer Science},
year = 2017,
booktitle = {Proceedings of EvoCOP 2017 -- 17th European Conference on Evolutionary Computation in Combinatorial Optimization },
editor = { Bin Hu and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
author = { Arnaud Liefooghe and Bilel Derbel and Verel, S{\'e}bastien and Aguirre, Hern\'{a}n E. and Tanaka, Kiyoshi },
title = {Towards Landscape-Aware Automatic Algorithm Configuration:
Preliminary Experiments on Neutral and Rugged Landscapes},
pages = {215--232},
doi = {10.1007/978-3-319-55453-2_15}
}
@incollection{LieDerVerLop2018ppsn,
volume = 11102,
year = 2018,
address = { Cham, Switzerland},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Anne Auger and Carlos M. Fonseca and Louren{\c c}o, N. and Penousal Machado and Lu{\'i}s Paquete and Darrell Whitley },
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XV}},
author = { Arnaud Liefooghe and Bilel Derbel and Verel, S{\'e}bastien and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Aguirre, Hern\'{a}n E. and Tanaka, Kiyoshi },
title = {On {Pareto} Local Optimal Solutions Networks},
pages = {232--244},
doi = {10.1007/978-3-319-99259-4_19}
}
@incollection{LieLop2023many,
location = {Lisbon, Portugal},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2023},
annote = {ISBN: 9798400701191},
address = { New York, NY},
year = 2023,
publisher = {ACM Press},
editor = {Silva, Sara and Lu{\'i}s Paquete },
author = { Arnaud Liefooghe and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Many-objective (Combinatorial) Optimization is Easy},
pages = {704--712},
doi = {10.1145/3583131.3590475},
abstract = {It is a common held assumption that problems with many
objectives are harder to optimize than problems with two or
three objectives. In this paper, we challenge this assumption
and provide empirical evidence that increasing the number of
objectives tends to reduce the difficulty of the landscape
being optimized. Of course, increasing the number of
objectives brings about other challenges, such as an increase
in the computational effort of many operations, or the memory
requirements for storing non-dominated solutions. More
precisely, we consider a broad range of multi- and
many-objective combinatorial benchmark problems, and we
measure how the number of objectives impacts the dominance
relation among solutions, the connectedness of the Pareto
set, and the landscape multimodality in terms of local
optimal solutions and sets. Our analysis shows the limit
behavior of various landscape features when adding more
objectives to a problem. Our conclusions do not contradict
previous observations about the inability of
Pareto-optimality to drive search, but we explain these
observations from a different perspective. Our findings have
important implications for the design and analysis of
many-objective optimization algorithms.}
}
@incollection{LieLopPaqVer2018gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2018,
editor = { Aguirre, Hern\'{a}n E. and Keiki Takadama},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018},
author = { Arnaud Liefooghe and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Verel, S{\'e}bastien },
title = {Dominance, Epsilon, and Hypervolume Local Optimal Sets in
Multi-objective Optimization, and How to Tell the Difference},
pages = {324--331},
doi = {10.1145/3205455.3205572}
}
@incollection{LieMesHum2009sls,
volume = 5752,
series = {Lecture Notes in Computer Science},
year = 2009,
address = { Heidelberg, Germany},
publisher = {Springer},
booktitle = {Engineering Stochastic Local Search
Algorithms. Designing, Implementing and Analyzing
Effective Heuristics. SLS~2009},
editor = { Thomas St{\"u}tzle and Mauro Birattari and Holger H. Hoos },
title = {A Study on Dominance-based Local Search Approaches
for Multiobjective Combinatorial Optimization},
author = { Arnaud Liefooghe and Salma Mesmoudi and J{\'e}r{\'e}mie Humeau and Laetitia Jourdan and Talbi, El-Ghazali },
pages = {120--124}
}
@incollection{LiePaqSim2011,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 6622,
year = 2011,
editor = { Peter Merz and Jin-Kao Hao },
booktitle = {Proceedings of EvoCOP 2011 -- 11th European Conference on Evolutionary Computation in Combinatorial Optimization },
author = { Arnaud Liefooghe and Lu{\'i}s Paquete and Sim{\={o}}es, Marco and Jos{\'e} Rui Figueira },
title = {Connectedness and Local Search for Bicriteria Knapsack Problems},
pages = {48--59},
doi = {10.1007/978-3-642-20364-0_5}
}
@incollection{LieVerAguTan2013ea,
address = { Heidelberg, Germany},
publisher = {Springer},
year = 2013,
volume = 8752,
fulleditor = {Pierrick Legrand and Marc{-}Michel Corsini and Jin-Kao Hao and Nicolas Monmarch{\'{e}} and Evelyne Lutton and Marc
Schoenauer},
editor = {Pierrick Legrand and others},
series = {Lecture Notes in Computer Science},
booktitle = {Artificial Evolution: 11th International Conference, Evolution Artificielle, EA, 2013},
title = {What Makes an Instance Difficult for Black-box 0--1
Evolutionary Multiobjective Optimizers?},
author = { Arnaud Liefooghe and Bilel Derbel and Verel, S{\'e}bastien and Aguirre, Hern\'{a}n E. and Tanaka, Kiyoshi },
pages = {3--15},
doi = {10.1007/978-3-319-11683-9_1}
}
@incollection{LieVerLac2021gecco,
location = {Lille, France},
address = { New York, NY},
publisher = {ACM Press},
year = 2021,
editor = { Chicano, Francisco and Krzysztof Krawiec },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021},
author = { Arnaud Liefooghe and Verel, S{\'e}bastien and Benjamin Lacroix and Alexandru{-}Ciprian Zavoianu and McCall, John },
doi = {10.1145/3449639.3459353},
pages = {421--429},
title = {Landscape features and automated algorithm selection for
multi-objective interpolated continuous optimisation
problems}
}
@incollection{LieVerPaqHao2015bubqp,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2015 Part {I}},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 9018,
year = 2015,
publisher = {Springer},
editor = { Ant{\'o}nio Gaspar{-}Cunha and Carlos Henggeler Antunes and Carlos A. {Coello Coello} },
author = { Arnaud Liefooghe and Verel, S{\'e}bastien and Lu{\'i}s Paquete and Jin-Kao Hao },
title = {Experiments on Local Search for Bi-objective Unconstrained
Binary Quadratic Programming},
pages = {171--186},
abstract = {This article reports an experimental analysis on stochastic
local search for approximating the Pareto set of bi-objective
unconstrained binary quadratic programming problems. First,
we investigate two scalarizing strategies that iteratively
identify a high-quality solution for a sequence of
sub-problems. Each sub-problem is based on a static or
adaptive definition of weighted-sum aggregation coefficients,
and is addressed by means of a state-of-the-art
single-objective tabu search procedure. Next, we design a
Pareto local search that iteratively improves a set of
solutions based on a neighborhood structure and on the Pareto
dominance relation. At last, we hybridize both classes of
algorithms by combining a scalarizing and a Pareto local
search in a sequential way. A comprehensive experimental
analysis reveals the high performance of the proposed
approaches, which substantially improve upon previous
best-known solutions. Moreover, the obtained results show the
superiority of the hybrid algorithm over non-hybrid ones in
terms of solution quality, while requiring a competitive
computational cost. In addition, a number of structural
properties of the problem instances allow us to explain the
main difficulties that the different classes of local search
algorithms have to face.}
}
@book{Lilja2000measuring,
author = {Lilja, David J.},
title = {Measuring Computer Performance: A Practitioner's Guide},
doi = {10.1017/CBO9780511612398},
publisher = {Cambridge University Press},
year = 2000,
abstract = {Measuring Computer Performance sets out the fundamental
techniques used in analyzing and understanding the
performance of computer systems. Throughout the book, the
emphasis is on practical methods of measurement, simulation,
and analytical modeling. The author discusses performance
metrics and provides detailed coverage of the strategies used
in benchmark programmes. He gives intuitive explanations of
the key statistical tools needed to interpret measured
performance data. He also describes the general 'design of
experiments' technique, and shows how the maximum amount of
information can be obtained for the minimum effort. The book
closes with a chapter on the technique of queueing
analysis. Appendices listing common probability distributions
and statistical tables are included, along with a glossary of
important technical terms. This practically-oriented book
will be of great interest to anyone who wants a detailed, yet
intuitive, understanding of computer systems performance
analysis.}
}
@inproceedings{LinHooHutSch2015aaai,
year = 2015,
publisher = {{AAAI} Press},
booktitle = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
editor = {Blai Bonet and Sven Koenig},
title = {{AutoFolio}: Algorithm Configuration for Algorithm Selection},
author = { Marius Thomas Lindauer and Holger H. Hoos and Frank Hutter and Schaub, Torsten}
}
@inproceedings{LinLuo07,
author = {W. Ling and H. Luo},
title = {An Adaptive Parameter Control Strategy for Ant
Colony Optimization},
booktitle = {CIS'07: Proceedings of the 2007 International
Conference on Computational Intelligence and
Security},
year = 2007,
pages = {142--146},
publisher = {IEEE Computer Society},
address = {Washington, DC}
}
@misc{LocalSolver,
author = {{Innovation 24}},
title = {{LocalSolver}},
howpublished = {\url{http://www.localsolver.com}},
note = {Last visited, August 15, 2016},
year = 2016
}
@incollection{LodTra2013,
year = 2013,
editor = {Topaluglu, Huseyin},
publisher = {{INFORMS}},
booktitle = {Theory Driven by Influential Applications},
author = { Andrea Lodi and Tramontani, Andrea},
title = {Performance Variability in Mixed-Integer Programming},
pages = {1--12}
}
@misc{LodiEtAl2004sup,
author = { Andrea Lodi and Silvano Martello and Vigo, Daniele },
title = {Two- and Three-Dimensional Bin Packing -- Source Code of
{TSpack}},
howpublished = {\url{https://site.unibo.it/operations-research/en/research/library-of-codes-and-instances-1/tspack-tar.gz/@@download/file/TSpack.tar.gz}},
year = 2004
}
@incollection{LohNow2013faster,
address = { Berlin, Germany},
publisher = {Springer},
doi = {10.1007/978-3-642-40935-6},
year = 2013,
volume = 8139,
series = {Lecture Notes in Computer Science},
booktitle = {Proceedings of Algorithmic Learning Theory},
editor = {Sanjay Jain and R{\'{e}}mi Munos and Frank Stephan and Thomas
Zeugmann},
title = {Faster {Hoeffding} Racing: {Bernstein} Races via Jackknife
Estimates},
author = {Loh, Po-Ling and Nowozin, Sebastian},
pages = {203--217}
}
@incollection{Lop07:HPC_ACO,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
editor = {Paola Alberigo and Giovanni Erbacci and Francesca
Garofalo and Silvia Monfardini},
booktitle = {Science and Sumpercomputing in Europe},
title = {High Performance Ant Colony Optimisation of the Pump
Scheduling Problem},
publisher = {CINECA},
year = 2007,
pages = {371--375},
isbn = {978-88-86037-21-1}
}
@techreport{LopBlu08:tsptw,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum },
title = {Beam-{ACO} Based on Stochastic Sampling: {A} Case Study on
the {TSP} with Time Windows},
institution = {Department LSI, Universitat Polit{\`e}cnica de Catalunya},
year = 2008,
number = {LSI-08-28},
note = {Extended version published in Computers \& Operations Research~\cite{LopBlu2010cor}}
}
@incollection{LopBlu09:evocop,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 5482,
year = 2009,
editor = { Carlos Cotta and P. Cowling},
booktitle = {Proceedings of EvoCOP 2009 -- 9th European Conference on Evolutionary Computation in Combinatorial Optimization },
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum and Dhananjay Thiruvady and Andreas T. Ernst and Bernd Meyer },
title = {Beam-{ACO} based on stochastic sampling for makespan
optimization concerning the {TSP} with time windows},
pages = {97--108},
doi = {10.1007/978-3-642-01009-5_9}
}
@incollection{LopBlu09:lion,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 5851,
booktitle = {Learning and Intelligent Optimization, Third International Conference, LION 3},
publisher = {Springer},
year = 2009,
editor = { Thomas St{\"u}tzle },
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum },
title = {Beam-{ACO} Based on Stochastic Sampling: {A} Case
Study on the {TSP} with Time Windows},
pages = {59--73},
doi = {10.1007/978-3-642-11169-3_5}
}
@incollection{LopChiGil2022evo,
fulleditor = { Jim{\'e}nez Laredo, Juan Luis and Hidalgo Perez, J. Ignacio and Oluwatoyin Babaagba, Kehinde},
address = {Switzerland},
series = {Lecture Notes in Computer Science},
volume = 13224,
booktitle = {EvoApplications 2022: Applications of Evolutionary Computation},
publisher = {Springer Nature},
year = 2022,
editor = { Jim{\'e}nez Laredo, Juan Luis and others},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Chicano, Francisco and Rodrigo Gil-Merino},
title = {The Asteroid Routing Problem: A Benchmark for Expensive
Black-Box Permutation Optimization},
pages = {124--140},
abstract = {Inspired by the recent 11th Global Trajectory Optimisation
Competition, this paper presents the asteroid routing problem
(ARP) as a realistic benchmark of algorithms for expensive
bound-constrained black-box optimization in permutation
space. Given a set of asteroids' orbits and a departure
epoch, the goal of the ARP is to find the optimal sequence
for visiting the asteroids, starting from Earth's orbit, in
order to minimize both the cost, measured as the sum of the
magnitude of velocity changes required to complete the trip,
and the time, measured as the time elapsed from the departure
epoch until visiting the last asteroid. We provide
open-source code for generating instances of arbitrary sizes
and evaluating solutions to the problem. As a preliminary
analysis, we compare the results of two methods for expensive
black-box optimization in permutation spaces, namely,
Combinatorial Efficient Global Optimization (CEGO), a
Bayesian optimizer based on Gaussian processes, and
Unbalanced Mallows Model (UMM), an estimation-of-distribution
algorithm based on probabilistic Mallows models. We
investigate the best permutation representation for each
algorithm, either rank-based or order-based. Moreover, we
analyze the effect of providing a good initial solution,
generated by a greedy nearest neighbor heuristic, on the
performance of the algorithms. The results suggest directions
for improvements in the algorithms being compared.},
keywords = {Spacecraft Trajectory Optimization, Unbalanced Mallows Model,
Combinatorial Efficient Global Optimization, Estimation of
Distribution Algorithms, Bayesian Optimization},
supplement = {https://doi.org/10.5281/zenodo.5725837},
doi = {10.1007/978-3-031-02462-7_9},
epub = {https://arxiv.org/abs/2203.15708}
}
@misc{LopDubPerStuBir2016irace-supp,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and P{\'e}rez C{\'a}ceres, Leslie and Thomas St{\"u}tzle and Mauro Birattari },
title = {The {\rpackage{irace}} Package: Iterated Racing for Automatic Algorithm
Configuration (Supplementary Material)},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2016-003}},
year = 2016
}
@techreport{LopDubStu2011irace,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and Thomas St{\"u}tzle and Mauro Birattari },
title = {The {\rpackage{irace}} package, Iterated Race for Automatic
Algorithm Configuration},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2011,
number = {TR/IRIDIA/2011-004},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2011-004.pdf},
note = {Published in Operations Research Perspectives~\cite{LopDubPerStuBir2016irace}}
}
@incollection{LopKno2015emo,
editor = { Ant{\'o}nio Gaspar{-}Cunha and Carlos Henggeler Antunes and Carlos A. {Coello Coello} },
volume = 9019,
year = 2015,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2015 Part {II}},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Joshua D. Knowles },
title = {Machine Decision Makers as a Laboratory for Interactive {EMO}},
pages = {295--309},
abstract = {A key challenge, perhaps the central challenge, of
multi-objective optimization is how to deal with candidate
solutions that are ultimately evaluated by the hidden or
unknown preferences of a human decision maker (DM) who
understands and cares about the optimization problem.
Alternative ways of addressing this challenge exist but
perhaps the favoured one currently is the interactive
approach (proposed in various forms). Here, an evolutionary
multi-objective optimization algorithm (EMOA) is controlled
by a series of interactions with the DM so that preferences
can be elicited and the direction of search controlled. MCDM
has a key role to play in designing and evaluating these
approaches, particularly in testing them with real DMs, but
so far quantitative assessment of interactive EMOAs has been
limited. In this paper, we propose a conceptual framework
for this problem of quantitative assessment, based on the
definition of machine decision makers (machine DMs), made
somewhat realistic by the incorporation of various
non-idealities. The machine DM proposed here draws from
earlier models of DM biases and inconsistencies in the MCDM
literature. As a practical illustration of our approach, we
use the proposed machine DM to study the performance of an
interactive EMOA, and discuss how this framework could help
in the evaluation and development of better interactive
EMOAs.},
doi = {10.1007/978-3-319-15892-1_20}
}
@incollection{LopKnoLau2011emo,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2011},
address = {Berlin\slash Heidelberg},
series = {Lecture Notes in Computer Science},
volume = 6576,
year = 2011,
publisher = {Springer},
editor = { Takahashi, R. H. C. and Kalyanmoy Deb and Wanner, Elizabeth F. and Salvatore Greco },
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Joshua D. Knowles and Marco Laumanns },
title = {On Sequential Online Archiving of Objective Vectors},
pages = {46--60},
doi = {10.1007/978-3-642-19893-9_4},
abstract = {In this paper, we examine the problem of maintaining
an approximation of the set of nondominated points
visited during a multiobjective optimization, a
problem commonly known as archiving. Most of the
currently available archiving algorithms are
reviewed, and what is known about their convergence
and approximation properties is summarized. The main
scenario considered is the restricted case where the
archive must be updated online as points are
generated one by one, and at most a fixed number of
points are to be stored in the archive at any one
time. In this scenario, the better-monotonicity of
an archiving algorithm is proposed as a weaker, but
more practical, property than negative efficiency
preservation. This paper shows that
hypervolume-based archivers and a recently proposed
multi-level grid archiver have this property. On the
other hand, the archiving methods used by SPEA2 and
NSGA-II do not, and they may better-deteriorate with
time. The better-monotonicity property has meaning
on any input sequence of points. We also classify
archivers according to limit properties,
i.e. convergence and approximation properties of the
archiver in the limit of infinite (input) samples
from a finite space with strictly positive
generation probabilities for all points. This paper
establishes a number of research questions, and
provides the initial framework and analysis for
answering them.},
annote = {Revised version available at \url{http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2011-001.pdf}}
}
@incollection{LopLiaStu2012ppsn,
volume = 7491,
year = 2012,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
fulleditor = { Carlos A. {Coello Coello} and Vincenzo Cutello and Kalyanmoy Deb and Stephanie
Forrest and Giuseppe Nicosia and Mario Pavone},
editor = { Carlos A. {Coello Coello} and others},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XII}, Part {I}},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Liao, Tianjun and Thomas St{\"u}tzle },
title = {On the anytime behavior of {IPOP-CMA-ES}},
pages = {357--366},
doi = {10.1007/978-3-642-32937-1_36}
}
@misc{LopLiaStu2012ppsn-supp,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Liao, Tianjun and Thomas St{\"u}tzle },
title = {On the anytime behavior of {IPOP-CMA-ES}: Supplementary material},
howpublished = {\url{https://iridia.ulb.ac.be/supp/IridiaSupp2012-010/IridiaSupp2012-010.pdf}},
year = 2012
}
@incollection{LopLieVer2014ppsn,
year = 2014,
volume = 8672,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Thomas Bartz-Beielstein and J{\"u}rgen Branke and Bogdan Filipi{\v c} and Jim Smith},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XIII}},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Arnaud Liefooghe and Verel, S{\'e}bastien },
doi = {10.1007/978-3-319-10762-2_61},
title = {Local Optimal Sets and Bounded Archiving on Multi-objective
{NK}-Landscapes with Correlated Objectives},
pages = {621--630},
abstract = {The properties of local optimal solutions in multi-objective
combinatorial optimization problems are crucial for the
effectiveness of local search algorithms, particularly when
these algorithms are based on Pareto dominance. Such local
search algorithms typically return a set of mutually
nondominated Pareto local optimal (PLO) solutions, that is, a
PLO-set. This paper investigates two aspects of PLO-sets by
means of experiments with Pareto local search (PLS). First,
we examine the impact of several problem characteristics on
the properties of PLO-sets for multi-objective NK-landscapes
with correlated objectives. In particular, we report that
either increasing the number of objectives or decreasing the
correlation between objectives leads to an exponential
increment on the size of PLO-sets, whereas the variable
correlation has only a minor effect. Second, we study the
running time and the quality reached when using bounding
archiving methods to limit the size of the archive handled by
PLS, and thus, the maximum size of the PLO-set found. We
argue that there is a clear relationship between the running
time of PLS and the difficulty of a problem instance.}
}
@inproceedings{LopMasMarStu2013mista,
address = {Gent, Belgium},
editor = { Graham Kendall and Vanden Berghe, Greet and Barry McCollum},
booktitle = {Multidisciplinary International Conference on Scheduling:
Theory and Applications (MISTA 2013)},
year = 2013,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Franco Mascia and Marie-El{\'e}onore Marmion and Thomas St{\"u}tzle },
title = {Automatic Design of a Hybrid Iterated Local Search for the
Multi-Mode Resource-Constrained Multi-Project Scheduling
Problem},
pages = {1--6},
epub = {https://hal.inria.fr/hal-01094681}
}
@incollection{LopPaqStu04:ants,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 3172,
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and L. M. Gambardella and Francesco Mondada and Thomas St{\"u}tzle and Mauro Birattari and Christian Blum },
year = 2004,
booktitle = {Ant Colony Optimization and Swarm Intelligence, 4th
International Workshop, ANTS 2004 },
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {On the Design of {ACO} for the Biobjective Quadratic
Assignment Problem},
pages = {214--225},
doi = {10.1007/978-3-540-28646-2_19}
}
@techreport{LopPaqStu04:hybrid,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {Hybrid Population-based Algorithms for the Bi-objective
Quadratic Assignment Problem},
institution = {FG Intellektik, FB Informatik, TU Darmstadt},
year = 2004,
number = {AIDA--04--11},
month = dec,
note = {Published in Journal of Mathematical Modelling and Algorithms~\cite{LopPaqStu05:jmma}},
annote = {First use of EAF differences}
}
@incollection{LopPaqStu09emaa,
editor = { Thomas Bartz-Beielstein and Marco Chiarandini and Lu{\'i}s Paquete and Mike Preuss },
year = 2010,
address = {Berlin\slash Heidelberg},
publisher = {Springer},
booktitle = {Experimental Methods for the Analysis of
Optimization Algorithms},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {Exploratory Analysis of Stochastic Local Search
Algorithms in Biobjective Optimization},
pages = {209--222},
doi = {10.1007/978-3-642-02538-9_9},
abstract = {This chapter introduces two Perl programs that
implement graphical tools for exploring the
performance of stochastic local search algorithms
for biobjective optimization problems. These tools
are based on the concept of the empirical attainment
function (EAF), which describes the probabilistic
distribution of the outcomes obtained by a
stochastic algorithm in the objective space. In
particular, we consider the visualization of
attainment surfaces and differences between the
first-order EAFs of the outcomes of two
algorithms. This visualization allows us to identify
certain algorithmic behaviors in a graphical way.
We explain the use of these visualization tools and
illustrate them with examples arising from
practice.}
}
@misc{LopPaqStu2010:eaftools,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {{EAF} Graphical Tools},
year = 2010,
howpublished = {\url{http://lopez-ibanez.eu/eaftools}},
note = {These tools are described in the book chapter
``\emph{Exploratory analysis of stochastic local search
algorithms in biobjective
optimization}''~\cite{LopPaqStu09emaa}.},
annote = {Please cite the book chapter, not this.}
}
@techreport{LopPerDubStuBir2016iraceguide,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and P{\'e}rez C{\'a}ceres, Leslie and J{\'e}r{\'e}mie Dubois-Lacoste and Thomas St{\"u}tzle and Mauro Birattari },
title = {The {\rpackage{irace}} package: User Guide},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2016,
number = {TR/IRIDIA/2016-004},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2016-004.pdf}
}
@inproceedings{LopPraPae08:WDSA,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Parallel Optimisation Of Pump Schedules With A
Thread-Safe Variant Of {EPANET} Toolkit},
booktitle = {Proceedings of the 10th Annual Water Distribution
Systems Analysis Conference (WDSA 2008)},
year = 2008,
editor = { Jakobus E. van Zyl and A. A. Ilemobade and H. E. Jacobs },
month = aug,
doi = {10.1061/41024(340)40},
publisher = {ASCE}
}
@incollection{LopPraPae:gecco07,
address = { New York, NY},
publisher = {ACM Press},
year = 2007,
editor = {Dirk Thierens and others},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Solving Optimal Pump Control Problem using
{\MaxMinAntSystem}},
volume = 1,
pages = 176,
doi = {10.1145/1276958.1276990}
}
@inproceedings{LopPraPaech05:cec,
address = {Piscataway, NJ},
publisher = {IEEE Press},
month = sep,
year = 2005,
booktitle = {Proceedings of the 2005 Congress on Evolutionary Computation (CEC 2005)},
key = {IEEE CEC},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Multi-objective Optimisation of the Pump Scheduling
Problem using {SPEA2}},
pages = {435--442},
volume = 1,
doi = {10.1109/CEC.2005.1554716}
}
@inproceedings{LopPraPaech:ccwi2005,
month = sep,
address = {University of Exeter, UK},
volume = 1,
editor = { Dragan A. Savic and Godfrey A. Walters and Roger King and Soon Thiam-Khu },
year = 2005,
booktitle = {Proceedings of the Eighth International Conference on
Computing and Control for the Water Industry (CCWI 2005)},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Optimal Pump Scheduling: Representation and Multiple
Objectives},
pages = {117--122}
}
@incollection{LopStu09ea,
address = { Heidelberg, Germany},
publisher = {Springer},
editor = {Pierre Collet and Nicolas Monmarch{\'e} and Pierrick
Legrand and Marc Schoenauer and Evelyne Lutton},
shorteditor = {Pierre Collet and others},
volume = 5975,
series = {Lecture Notes in Computer Science},
year = 2010,
booktitle = {Artificial Evolution: 9th International Conference, Evolution Artificielle, EA, 2009},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An Analysis of Algorithmic Components for
Multiobjective Ant Colony Optimization: {A} Case
Study on the Biobjective {TSP}},
pages = {134--145},
doi = {10.1007/978-3-642-14156-0_12}
}
@incollection{LopStu2010:ants,
volume = 6234,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
fulleditor = { Marco Dorigo and Mauro Birattari and Gianni A. {Di Caro} and Doursat, R. and Engelbrecht, A. P. and Floreano,
D. and Gambardella, L. M. and Gro\ss, R. and Sahin,
E. and Thomas St{\"u}tzle and Sayama, H.},
editor = { Marco Dorigo and others},
year = 2010,
booktitle = {Swarm Intelligence, 7th International Conference, ANTS 2010},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Configuration of Multi-Objective {ACO}
Algorithms},
pages = {95--106},
doi = {10.1007/978-3-642-15461-4_9},
abstract = {In the last few years a significant number of ant
colony optimization (ACO) algorithms have been
proposed for tackling multi-objective optimization
problems. In this paper, we propose a software
framework that allows to instantiate the most
prominent multi-objective ACO (MOACO)
algorithms. More importantly, the flexibility of
this MOACO framework allows the application of
automatic algorithm configuration techniques. The
experimental results presented in this paper show
that such an automatic configuration of MOACO
algorithms is highly desirable, given that our
automatically configured algorithms clearly
outperform the best performing MOACO algorithms that
have been proposed in the literature. As far as we
are aware, this paper is also the first to apply
automatic algorithm configuration techniques to
multi-objective stochastic local search algorithms.}
}
@incollection{LopStu2010:gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2010,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2010},
editor = {Martin Pelikan and J{\"u}rgen Branke },
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {The impact of design choices of multi-objective ant
colony optimization algorithms on performance: An
experimental study on the biobjective {TSP}},
doi = {10.1145/1830483.1830494},
pages = {71--78},
abstract = {Over the last few years, there have been a number of
proposals of ant colony optimization (ACO)
algorithms for tackling multiobjective combinatorial
optimization problems. These proposals adapt ACO
concepts in various ways, for example, some use
multiple pheromone matrices and multiple heuristic
matrices and others use multiple ant colonies.\\ In
this article, we carefully examine several of the
most prominent of these proposals. In particular, we
identify commonalities among the approaches by
recasting the original formulation of the algorithms
in different terms. For example, several proposals
described in terms of multiple colonies can be cast
equivalently using a single ant colony, where ants
use different weights for aggregating the pheromone
and/or the heuristic information. We study
algorithmic choices for the various proposals and we
identify previously undetected trade-offs in their
performance.}
}
@misc{LopStu2010:gecco-supp,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {The impact of design choices of multi-objective ant
colony optimization algorithms on performance: An
experimental study on the biobjective {TSP}},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2010-003/}},
year = 2010,
note = {Supplementary material of \cite{LopStu2010:gecco}}
}
@misc{LopStu2011moaco-supp,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {The Automatic Design of Multi-Objective Ant Colony
Optimization Algorithms: {Supplementary} material},
url = {http://iridia.ulb.ac.be/supp/IridiaSupp2011-007/Iridia-2011-007.pdf},
year = 2011
}
@misc{LopStu2012si-supp,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An experimental analysis of design choices of
multi-objective ant colony optimization algorithms:
Supplementary material},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2012-006/}},
year = 2012
}
@incollection{LopStuDor2017aco,
isbn = {978-3-319-07125-1},
publisher = {Springer International Publishing},
year = 2018,
booktitle = {Handbook of Heuristics},
editor = { Rafael Mart{\'i} and Panos M. Pardalos and Mauricio G. C. Resende },
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle and Marco Dorigo },
title = {Ant Colony Optimization: A Component-Wise Overview},
pages = {371--407},
annote = {Proposed ACOTSPQAP software},
doi = {10.1007/978-3-319-07124-4_21},
supplement = {http://iridia.ulb.ac.be/aco-tsp-qap/}
}
@phdthesis{LopezDiploma,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Multi-objective Ant Colony Optimization},
school = {Intellectics Group, Computer Science Department, Technische
Universit{\"a}t Darmstadt, Germany},
year = 2004,
type = {Diploma thesis}
}
@phdthesis{LopezIbanezPhD,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Operational Optimisation of Water Distribution
Networks},
school = {School of Engineering and the Built Environment},
year = 2009,
address = {Edinburgh Napier University, UK},
url = {https://researchrepository.napier.ac.uk/id/eprint/3044}
}
@incollection{LosSchSeb2012ppsn,
volume = 7491,
year = 2012,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
fulleditor = { Carlos A. {Coello Coello} and Vincenzo Cutello and Kalyanmoy Deb and Stephanie
Forrest and Giuseppe Nicosia and Mario Pavone},
editor = { Carlos A. {Coello Coello} and others},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XII}, Part {I}},
author = {Ilya Loshchilov and Marc Schoenauer and Mich{\`e}le Sebag },
title = {Alternative Restart Strategies for {CMA-ES}},
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publisher = {Springer},
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editor = { Carlos A. {Coello Coello} and others},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XII}, Part {II}},
author = {Hoang N. Luong and Peter A. N. Bosman },
title = {Elitist Archiving for Multi-Objective Evolutionary
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tariffs. A simple example shows that the method is easy to
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pages = {191--202}
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author = { Franco Mascia and Mauro Birattari and Thomas St{\"u}tzle },
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isbn = {978-3-642-29827-1},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 7298,
booktitle = {Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2012},
publisher = {Springer},
year = 2012,
editor = {Nicolas Beldiceanu and Narendra Jussien and Eric Pinson},
author = { Florence Massen and Yves Deville and van Hentenryck, Pascal },
title = {Pheromone-Based Heuristic Column Generation for Vehicle
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year = 2013,
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2013-009/}}
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address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
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isbn = {978-3-319-07643-0},
editor = { Mar{\'i}a J. Blesa and Christian Blum and Stefan Vo{\ss} },
year = 2014,
booktitle = {Hybrid Metaheuristics},
author = { Franco Mascia and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and Marie-El{\'e}onore Marmion and Thomas St{\"u}tzle },
title = {Algorithm Comparison by Automatically Configurable Stochastic
Local Search Frameworks: A Case Study Using Flow-Shop
Scheduling Problems},
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@incollection{MasLopDubStu2013lion,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 7997,
booktitle = {Learning and Intelligent Optimization, 7th International Conference, LION 7},
publisher = {Springer},
year = 2013,
editor = { Panos M. Pardalos and G. Nicosia},
author = { Franco Mascia and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and Thomas St{\"u}tzle },
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doi = {10.1007/978-3-642-44973-4_36}
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@incollection{MasLopStu2013hm,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 7919,
series = {Lecture Notes in Computer Science},
editor = { Mar{\'i}a J. Blesa and Christian Blum and Paola Festa and Andrea Roli and M. Sampels },
isbn = {978-3-642-38515-5},
year = 2013,
booktitle = {Hybrid Metaheuristics},
author = { Florence Massen and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle and Yves Deville },
title = {Experimental Analysis of Pheromone-Based Heuristic
Column Generation Using irace},
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booktitle = {Learning Meets Combinatorial Algorithms Workshop at NeurIPS
2020},
year = 2020,
author = {Massobrio, Renzo and Nesmachnow, Sergio and Bernab{\'e} Dorronsoro },
title = {Virtual {Savant}: learning for optimization},
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Toshiyuki and Shibasaki, Takayuki and Watanabe, Yasuhiro and
Takemoto, Kazuya and Tamura, Hirotaka},
title = {Digital Annealer for High-Speed Solving of Combinatorial
optimization Problems and Its Applications},
booktitle = {2020 25th Asia and South Pacific Design Automation Conference
(ASP-DAC)},
year = 2020,
pages = {667--672},
organization = {IEEE},
doi = {10.1109/ASP-DAC47756.2020.9045100},
abstract = {A Digital Annealer (DA) is a dedicated architecture for
high-speed solving of combinatorial optimization problems
mapped to an Ising model. With fully coupled bit connectivity
and high coupling resolution as a major feature, it can be
used to express a wide variety of combinatorial optimization
problems. The DA uses Markov Chain Monte Carlo as a basic
search mechanism, accelerated by the hardware implementation
of multiple speed-enhancement techniques such as parallel
search, escape from a local solution, and replica
exchange. It is currently being offered as a cloud service
using a second-generation chip operating on a scale of 8,192
bits. This paper presents an overview of the DA, its
performance against benchmarks, and application examples.}
}
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year = 2015,
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year = 2015,
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keywords = {continuous optimization, landscape analysis, instance
features}
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volume = 5199,
series = {Lecture Notes in Computer Science},
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Carlo Poloni and Nicola Beume},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {X}},
author = { Peter Merz and Jutta Huhse},
title = {An Iterated Local Search Approach for Finding Provably Good Solutions
for Very Large {TSP} Instances},
pages = {929--939}
}
@incollection{MerMid01,
author = { D. Merkle and Martin Middendorf },
title = {Prospects for Dynamic Algorithm Control: Lessons
from the Phase Structure of Ant Scheduling
Algorithms},
booktitle = {Proceedings of the 2001 Genetic and Evolutionary
Computation Conference -- Workshop Program. Workshop
``The Next Ten Years of Scheduling Research''},
pages = {121--126},
year = 2001,
editor = {R. B. Heckendorn},
address = {San Francisco, CA},
publisher = {Morgan Kaufmann Publishers}
}
@incollection{MerMidSch00:gecco,
publisher = {Morgan Kaufmann Publishers, San Francisco, CA},
editor = { Darrell Whitley and others},
fulleditor = { Darrell Whitley and David E. Goldberg and E. Cantu-Paz and L. Spector and
I. Parmee and Hans-Georg Beyer },
year = 2000,
booktitle = {Proceedings of the Genetic and Evolutionary Computation
Conference, GECCO 2000},
author = { D. Merkle and Martin Middendorf and Hartmut Schmeck },
title = {Ant Colony Optimization for Resource-Constrained Project Scheduling},
pages = {893--900}
}
@inproceedings{MerTraNau2010cec,
year = 2010,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2010 Congress on Evolutionary Computation (CEC 2010)},
editor = { Ishibuchi, Hisao and others},
key = {IEEE CEC},
author = { Olaf Mersmann and Heike Trautmann and Boris Naujoks and Claus Weihs },
title = {Benchmarking Evolutionary Multiobjective Optimization Algorithms},
pages = {1--8},
annote = {TR: \url{http://hdl.handle.net/2003/26671}}
}
@inproceedings{Mey2004:gecco,
author = { Bernd Meyer },
title = {Convergence control in {ACO}},
note = {Late-breaking paper available on CD},
year = 2004,
booktitle = {Genetic and Evolutionary Computation Conference
(GECCO)},
address = {Seattle, WA}
}
@incollection{MeyErn04:ants,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 3172,
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and L. M. Gambardella and Francesco Mondada and Thomas St{\"u}tzle and Mauro Birattari and Christian Blum },
booktitle = {Ant Colony Optimization and Swarm Intelligence, 4th
International Workshop, ANTS 2004 },
author = { Bernd Meyer and Andreas T. Ernst },
title = {Integrating {ACO} and Constraint Propagation},
pages = {166--177},
year = 2004
}
@incollection{MezReyCoe2008,
address = { Heidelberg, Germany},
publisher = {Springer},
editor = {Uday K. Chakraborty},
year = 2008,
booktitle = {Advances in differential evolution},
author = { Efr{\'e}n Mezura-Montes and Reyes-Sierra, M. and Carlos A. {Coello Coello} },
title = {Multi-objective optimization using differential
evolution: a survey of the state-of-the-art},
pages = {173--196},
doi = {10.1007/978-3-540-68830-3_7}
}
@incollection{MezVelCoe2006,
address = { New York, NY},
publisher = {ACM Press},
year = 2006,
editor = {M. Cattolico and others},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2006},
title = {A comparative study of differential evolution variants for
global optimization},
author = { Efr{\'e}n Mezura-Montes and Vel{\'a}zquez-Reyes, Jes{\'u}s and Carlos A. {Coello Coello} },
pages = {485--492},
doi = {10.1145/1143997.1144086}
}
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author = { Zbigniew Michalewicz and David B. Fogel },
title = {How to Solve It: Modern Heuristics},
publisher = {Springer},
year = 2004,
edition = {2nd}
}
@incollection{MicHen2004,
publisher = {{AAAI} Press\slash {MIT} Press, Menlo Park, CA},
year = 2004,
editor = { Shlomo Zilberstein and J. Koehler and S. Koenig},
booktitle = {Proceedings of the Fourteenth International Conference on
Automated Planning and Scheduling (ICAPS 2004)},
author = { Laurent D. Michel and van Hentenryck, Pascal },
title = {Iterative Relaxations for Iterative Flattening in Cumulative
Scheduling},
pages = {200--208}
}
@incollection{MicMid98,
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Agoston E. Eiben and Thomas B{\"a}ck and Marc Schoenauer and Hans-Paul Schwefel },
volume = 1498,
series = {Lecture Notes in Computer Science},
year = 1998,
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {V}},
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pages = {692--701}
}
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author = { Zbigniew Michalewicz },
title = {Genetic Algorithms + Data Structures = Evolution Programs},
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year = 1996,
address = { Berlin, Germany},
publisher = {Springer}
}
@incollection{Mie2006indnimbus,
author = { Kaisa Miettinen },
title = {{IND-NIMBUS} for Demanding Interactive Multiobjective
Optimization},
booktitle = {Multiple Criteria Decision Making '05},
publisher = {Karol Adamiecki University of Economics in Katowice},
year = 2006,
editor = {T. Trzaskalik},
pages = {137--150},
language = {English},
isbn = 8372468435
}
@book{Mie99,
author = { Kaisa Miettinen },
title = {Nonlinear Multiobjective Optimization},
publisher = {Kluwer Academic Publishers},
address = { Boston, MA},
year = 1999,
abstract = {Nonlinear Multiobjective Optimization provides an extensive,
up-to-date, self-contained and consistent survey and review
of the literature and of the state of the art on nonlinear
(deterministic) multiobjective optimization, its methods, its
theory and its background. This book is intended for both
researchers and students in the areas of (applied)
mathematics, engineering, economics, operations research and
management science; it is meant for both professionals and
practitioners in many different fields of application. The
intention is to provide a consistent summary that may help in
selecting an appropriate method for the problem to be
solved. The extensive bibliography will be of value to
researchers.},
numpages = 298
}
@incollection{MieRuiWie08interactive,
editor = { J{\"u}rgen Branke and Kalyanmoy Deb and Kaisa Miettinen and Roman S{\l}owi{\'n}ski },
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 5252,
year = 2008,
booktitle = {Multiobjective Optimization: Interactive and Evolutionary
Approaches},
author = { Kaisa Miettinen and Francisco Ruiz and Andrzej P. Wierzbicki },
title = {Introduction to Multiobjective Optimization:
Interactive Approaches},
doi = {10.1007/978-3-540-88908-3_2},
abstract = {We give an overview of interactive methods developed
for solving nonlinear multiobjective optimization
problems. In interactive methods, a decision maker
plays an important part and the idea is to support
her/him in the search for the most preferred
solution. In interactive methods, steps of an
iterative solution algorithm are repeated and the
decision maker progressively provides preference
information so that the most preferred solution can
be found. We identify three types of specifying
preference information in interactive methods and
give some examples of methods representing each
type. The types are methods based on trade-off
information, reference points and classification of
objective functions.}
}
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year = 2014,
booktitle = {European Symposium on Artificial Neural Networks, ESSAN},
key = {ESANN},
author = {P{\'e}ricles Miranda and Ricardo M. Silva and Ricardo
B. Prud{\^e}ncio},
title = {Fine-Tuning of Support Vector Machine Parameters Using Racing
Algorithms},
pages = {325--330},
keywords = {irace}
}
@inproceedings{MirSilPru2015:esann,
epub = {https://www.esann.org/proceedings/2015},
year = 2015,
booktitle = {European Symposium on Artificial Neural Networks, ESSAN},
key = {ESANN},
author = {P{\'e}ricles Miranda and Ricardo M. Silva and Ricardo
B. Prud{\^e}ncio},
title = {{I/S-Race}: An Iterative Multi-objective Racing Algorithm for
the {SVM} Parameter Selection Problem},
pages = {573--578}
}
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address = { Heidelberg, Germany},
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year = 2003,
booktitle = {Proceedings of the Genetic and Evolutionary Computation
Conference, GECCO 2003, Part I},
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Assignment Problem},
pages = {598--609}
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}
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booktitle = { Evolutionary Multi-criterion Optimization, EMO 2011},
address = {Berlin\slash Heidelberg},
series = {Lecture Notes in Computer Science},
volume = 6576,
year = 2011,
publisher = {Springer},
editor = { Takahashi, R. H. C. and Kalyanmoy Deb and Wanner, Elizabeth F. and Salvatore Greco },
title = {{Lorenz} versus {Pareto} Dominance in a Single Machine
Scheduling Problem with Rejection},
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Lionel},
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year = 2012,
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epub = {http://icml.cc/2012/papers/838.pdf}
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@incollection{MonDevHen2009,
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}
@incollection{MonPerRiffCoe12dummy,
volume = 7491,
year = 2012,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
fulleditor = { Carlos A. {Coello Coello} and Vincenzo Cutello and Kalyanmoy Deb and Stephanie
Forrest and Giuseppe Nicosia and Mario Pavone},
editor = { Carlos A. {Coello Coello} and others},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XII}, Part {I}},
author = { Elizabeth Montero and P{\'e}rez C{\'a}ceres, Leslie and Mar{\'i}a-Cristina Riff and Carlos A. {Coello Coello} },
title = {Are State-of-the-Art Fine-Tuning Algorithms Able to
Detect a Dummy Parameter?},
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year = 2013,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2013 Congress on Evolutionary Computation (CEC 2013)},
key = {IEEE CEC},
title = {A new algorithm for reducing metaheuristic design effort},
author = { Mar{\'i}a-Cristina Riff and Elizabeth Montero },
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doi = {10.1109/CEC.2013.6557972}
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@incollection{MonRif2014ppsn,
year = 2014,
volume = 8672,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Thomas Bartz-Beielstein and J{\"u}rgen Branke and Bogdan Filipi{\v c} and Jim Smith},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XIII}},
author = { Elizabeth Montero and Mar{\'i}a-Cristina Riff },
title = {Towards a Method for Automatic Algorithm Configuration: A Design Evaluation Using Tuners},
pages = {90--99},
doi = {10.1007/978-3-319-10762-2_9}
}
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address = { New York, NY},
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year = 2010,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2010},
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title = {An Evaluation of Off-line Calibration Techniques for
Evolutionary Algorithms},
pages = {299--300}
}
@incollection{MonYos11,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2011},
address = {Berlin\slash Heidelberg},
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volume = 6576,
year = 2011,
publisher = {Springer},
editor = { Takahashi, R. H. C. and Kalyanmoy Deb and Wanner, Elizabeth F. and Salvatore Greco },
title = {A Framework for Locating Logistic Facilities with
Multi-Criteria Decision Analysis},
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@phdthesis{Montes2011PhD,
author = { Marco A. {Montes de Oca} },
title = {Incremental Social Learning in Swarm Intelligence Systems},
school = {IRIDIA, {\'E}cole polytechnique, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2011,
annote = {Supervised by Marco Dorigo}
}
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publisher = {AAAI Press, Menlo Park, CA},
editor = {Manuela M. Veloso},
year = 2007,
booktitle = {Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-07)},
author = {V. Nannen and Agoston E. Eiben },
title = {Relevance Estimation and Value Calibration of Evolutionary
Algorithm Parameters},
pages = {975--980},
keywords = {REVAC}
}
@incollection{NasMesUgo2012,
volume = 7492,
year = 2012,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
fulleditor = { Carlos A. {Coello Coello} and Vincenzo Cutello and Kalyanmoy Deb and Stephanie
Forrest and Giuseppe Nicosia and Mario Pavone},
editor = { Carlos A. {Coello Coello} and others},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XII}, Part {II}},
author = {Youssef S. G. Nashed and Pablo Mesejo and Roberto Ugolotti and J{\'e}r{\'e}mie Dubois-Lacoste and Stefano Cagnoni},
title = {A Comparative Study of Three {GPU}-Based Metaheuristics},
pages = {398--407},
doi = {10.1007/978-3-642-32964-7_40}
}
@inproceedings{NebDurCoe2013cec,
year = 2013,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2013 Congress on Evolutionary Computation (CEC 2013)},
key = {IEEE CEC},
author = { Nebro, Antonio J. and Durillo, Juan J. and Carlos A. {Coello Coello} },
title = {Analysis of leader selection strategies in a multi-objective
{Particle} {Swarm} {Optimizer}},
pages = {3153--3160},
doi = {10.1109/CEC.2013.6557955}
}
@inproceedings{NebDurGar2009smpso,
author = { Nebro, Antonio J. and Durillo, Juan J. and Jos{\'e} Garc{\'i}a-Nieto and Carlos A. {Coello Coello} and F. Luna and Alba, Enrique },
doi = {10.1109/MCDM.2009.4938830},
isbn = 9781424427642,
booktitle = {2009 IEEE Symposium on Computational Intelligence in
Multi-Criteria Decision-Making (MCDM)},
pages = {66--73},
title = {{SMPSO}: A new {PSO}-based metaheuristic for multi-objective
optimization},
year = 2009
}
@incollection{NebDurVer2015jmetal,
address = { New York, NY},
publisher = {ACM Press},
year = 2015,
editor = { Jim{\'e}nez Laredo, Juan Luis and Sara Silva and Anna I. Esparcia{-}Alc{\'{a}}zar },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2015},
author = { Nebro, Antonio J. and Durillo, Juan J. and Vergne, Matthieu},
title = {Redesigning the {jMetal} Multi-Objective Optimization
Framework},
keywords = {JMetal, Multi-objective metaheuristics, Open source,
Optimization framework},
pages = {1093--1100},
numpages = 8,
doi = {10.1145/2739482.2768462}
}
@incollection{NebLopBarGar2019gecco,
isbn = {978-1-4503-6748-6},
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
author = { Nebro, Antonio J. and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Barba-Gonz{\'a}lez, Crist{\'o}bal and Jos{\'e} Garc{\'i}a-Nieto },
title = {Automatic Configuration of {NSGA-II} with {jMetal} and irace},
pages = {1374--1381},
doi = {10.1145/3319619.3326832}
}
@book{NemWol88,
author = {G. L. Nemhauser and L. A. Wolsey},
title = {Integer and Combinatorial Optimization},
publisher = {John Wiley \& Sons},
address = { New York, NY},
year = 1988
}
@incollection{NikJac2010,
address = { New York, NY},
edition = {2nd},
series = {International Series in Operations Research \& Management
Science},
volume = 146,
booktitle = {Handbook of Metaheuristics},
year = 2010,
editor = { Michel Gendreau and Jean-Yves Potvin },
title = {Simulated Annealing},
author = { Alexander G. Nikolaev and Sheldon H. Jacobson },
pages = {1--39},
publisher = {Springer}
}
@inproceedings{NikMarJan2009,
title = {Instance-based selection of policies for {SAT} solvers},
author = {Nikoli{\'c}, Mladen and Mari{\'c}, Filip and Jani{\v{c}}i{\'c}, Predrag},
booktitle = {International Conference on Theory and Applications of Satisfiability Testing},
pages = {326--340},
year = 2009,
organization = {Springer}
}
@incollection{NisOyaAkiAguTan2014:space,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 8426,
booktitle = {Learning and Intelligent Optimization, 8th International Conference, LION 8},
publisher = {Springer},
year = 2014,
editor = { Panos M. Pardalos and Mauricio G. C. Resende and Chrysafis Vogiatzis and Jose
L. Walteros},
author = {Y. Nishio and A. Oyama and Y. Akimoto and Aguirre, Hern\'{a}n E. and Tanaka, Kiyoshi },
title = {Many-objective Optimization of Trajectory Design for
{DESTINY} Mission}
}
@book{NixAgu2012,
title = {Feature extraction \& image processing for computer vision},
author = {Nixon, Mark S. and Aguado, Alberto S.},
year = 2012,
publisher = {Academic Press},
address = { New York, NY}
}
@incollection{NobVerWan2021gecco,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2021},
address = { New York, NY},
year = 2021,
publisher = {ACM Press},
editor = { Chicano, Francisco and Krzysztof Krawiec },
author = {Jacob de Nobel and Diederick Vermetten and Wang, Hao and Carola Doerr and Thomas B{\"a}ck },
title = {Tuning as a means of assessing the benefits of new ideas in
interplay with existing algorithmic modules},
pages = {1375--1384},
doi = {10.1145/3449726.3463167},
supplement = {https://doi.org/10.5281/zenodo.4524959}
}
@misc{NobVerWan2021gecco-supp,
author = {Jacob de Nobel and Diederick Vermetten and Wang, Hao and Carola Doerr and Thomas B{\"a}ck },
title = {Data and Code from Tuning as a means of assessing the
benefits of new ideas in interplay with existing algorithmic
modules},
month = feb,
year = 2021,
publisher = {Zenodo},
version = {1.0},
doi = {10.5281/zenodo.4524959}
}
@book{NocWri2006,
author = {Jorge Nocedal and Stephen J. Wright},
title = {Numerical Optimization},
publisher = {Springer},
year = 2006,
edition = {2nd}
}
@incollection{NouDriGhe2016,
volume = 464,
series = {Advances in Intelligent Systems and Computing},
publisher = {Springer International Publishing},
editor = {Silhavy, Radek and Senkerik, Roman and Oplatkova, Zuzana Kominkova and Silhavy, Petr and Prokopova, Zdenka},
year = 2016,
booktitle = {Artificial Intelligence Perspectives in Intelligent Systems},
title = {A Classification Schema for the Job Shop Scheduling Problem with Transportation Resources: State-of-the-Art Review},
author = {Nouri, Houssem Eddine and Driss, Olfa Belkahla and Gh{\'e}dira, Khaled},
pages = {1--11}
}
@incollection{Now2014hv,
year = 2014,
volume = 8672,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Thomas Bartz-Beielstein and J{\"u}rgen Branke and Bogdan Filipi{\v c} and Jim Smith},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XIII}},
title = {Empirical Performance of the Approximation of the Least
Hypervolume Contributor},
author = {Nowak, Krzysztof and M{\"a}rtens, Marcus and Dario Izzo },
pages = {662--671}
}
@inproceedings{OMaHebHolNugOSu2008cphydra,
title = {Using case-based reasoning in an algorithm portfolio for
constraint solving},
author = {O'Mahony, Eoin and Hebrard, Emmanuel and Holland, Alan and
Nugent, Conor and O'Sullivan, Barry },
booktitle = {Irish Conference on Artificial Intelligence and Cognitive
Science},
pages = {210--216},
editor = {Bridge and others},
year = 2008
}
@incollection{ObaSas2003visualization,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 2632,
series = {Lecture Notes in Computer Science},
editor = { Carlos M. Fonseca and Peter J. Fleming and Eckart Zitzler and Kalyanmoy Deb and Lothar Thiele },
year = 2003,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2003},
title = {Visualization and data mining of {Pareto} solutions using
self-organizing map},
author = {Obayashi, Shigeru and Sasaki, Daisuke},
pages = {796--809},
keywords = {objective reduction}
}
@incollection{OchHydCur2012evocop,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 7245,
year = 2012,
editor = { Jin-Kao Hao and Martin Middendorf },
booktitle = {Proceedings of EvoCOP 2012 -- 12th European Conference on Evolutionary Computation in Combinatorial Optimization },
title = {Hyflex: A benchmark framework for cross-domain heuristic
search},
author = { Gabriela Ochoa and Matthew Hyde and Tim Curtois and Jose
A. Vazquez-Rodriguez and James Walker and Michel Gendreau and Graham Kendall and Barry McCollum and Andrew J. Parkes and Sanja Petrovic and Edmund K. Burke },
pages = {136--147}
}
@incollection{OchTomVerDar2008,
address = { New York, NY},
publisher = {ACM Press},
year = 2008,
editor = {Conor Ryan},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2008},
author = { Gabriela Ochoa and Tomassini, Marco and Verel, S{\'e}bastien and Darabos, Christian},
title = {A Study of {NK} Landscapes' Basins and Local Optima Networks},
pages = {555--562}
}
@inproceedings{OddRasCesSmi2011,
publisher = {IJCAI/AAAI Press, Menlo Park, CA},
editor = {Toby Walsh},
year = 2011,
booktitle = {Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI-11)},
author = { Angelo Oddi and Riccardo Rasconi and Amadeo Cesta and Stephen F. Smith },
title = {Iterative Flattening Search for the Flexible Job Shop Scheduling Problem},
pages = {1991--1996}
}
@incollection{OjaPodMie2016ppsn,
isbn = {978-3-319-45822-9},
year = 2016,
volume = 9921,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Julia Handl and Emma Hart and Lewis, P. R. and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Gabriela Ochoa and Ben Paechter },
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XIV}},
author = {Vesa Ojalehto and Dmitry Podkopaev and Kaisa Miettinen },
title = {Towards Automatic Testing of Reference Point Based
Interactive Methods},
pages = {483--492},
doi = {10.1007/978-3-319-45823-6_45},
keywords = {artificial DMs},
abstract = {In this research, we proposed to build an automated framework
for testing interactive multiobjective optimization methods,
without utilizing a value function to represent the DM's
preferences. This was achieved by replacing the human DM with
an artificial DM constructed from two distinct parts: the
steady part and the current context. With the steady part the
artificial DM tries to maintain the search towards its
preferences, while at the same time the current context
allows changing the direction as well as ending the solution
process prematurely, mimicking actions of a human DM.}
}
@inproceedings{OliHusRolDorStu2017cec,
year = 2017,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2017 Congress on Evolutionary Computation (CEC 2017)},
key = {IEEE CEC},
author = {Sabrina M. Oliveira and Mohamed Saifullah Hussin and Andrea Roli and Marco Dorigo and Thomas St{\"u}tzle },
title = {Analysis of the Population-based Ant Colony Optimization Algorithm for the {TSP} and the {QAP}},
pages = {1734--1741}
}
@incollection{OlsBarUrb2016gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2016,
editor = { Tobias Friedrich and Frank Neumann and Andrew M. Sutton },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2016},
author = {Olson, Randal S. and Bartley, Nathan and Urbanowicz, Ryan
J. and Moore, Jason H.},
title = {Evaluation of a Tree-based Pipeline Optimization Tool for
Automating Data Science},
pages = {485--492},
numpages = 8,
doi = {10.1145/2908812.2908918},
acmid = 2908918,
keywords = {TPOT}
}
@incollection{OlsUrbAnd2016evobio,
volume = 9597,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
booktitle = {Applications of Evolutionary Computation},
year = 2016,
editor = {Squillero, Giovanni and Burelli, Paolo},
author = {Olson, Randal S. and Urbanowicz, Ryan J. and Andrews, Peter
C. and Lavender, Nicole A. and Kidd, La Creis and Moore,
Jason H.},
title = {Automating Biomedical Data Science Through Tree-Based
Pipeline Optimization},
pages = {123--137},
doi = {10.1007/978-3-319-31204-0_9},
keywords = {TPOT}
}
@inproceedings{OstSal04,
author = { Avi Ostfeld and Elad Salomons},
title = {Optimal Scheduling of Pumping and Chlorine
Injections under Unsteady Hydraulics},
booktitle = {Critical Transitions In Water And Environmental
Resources Management},
pages = {1--9},
year = 2004,
editor = {Gerald Sehlke and Donald F. Hayes and David
K. Stevens},
month = jul,
abstract = {This paper describes the methodology and application
of a genetic algorithm (GA) scheme, tailor-made to
EPANET for simultaneously optimizing the scheduling
of existing pumping and booster disinfection units,
as well as the design of new disinfection booster
chlorination stations, under unsteady
hydraulics. The objective is to minimize the total
cost of operating the pumping units and the chlorine
booster operation and design for a selected
operational time horizon, while delivering the
consumers required water quantities, at acceptable
pressures and chlorine residual concentrations. The
decision variables, for each of the time steps that
encompass the total operational time horizon,
include: the scheduling of the pumping units,
settings of the water distribution system control
valves, and the mass injection rates at each of the
booster chlorination stations. The constraints are
domain heads and chlorine concentrations at the
consumer nodes, maximum injection rates at the
chlorine injection stations, maximum allowable
amounts of water withdraws at the sources, and
returning at the end of the operational time horizon
to a prescribed total volume in the tanks. The model
is demonstrated through an example application.}
}
@incollection{OztTsoVin2005:mcda,
editor = { Jos{\'e} Rui Figueira and Salvatore Greco and Matthias Ehrgott },
year = 2005,
publisher = {Springer},
booktitle = {Multiple Criteria Decision Analysis, State of the
Art Surveys},
author = { Meltem {\"O}zt{\"u}rk and Alexis Tsouki{\`a}s and Philippe Vincke },
title = {Preference Modelling},
chapter = 2,
pages = {27--72}
}
@techreport{PagStu2018:pfsp,
author = { Federico Pagnozzi and Thomas St{\"u}tzle },
title = {Automatic Design of Hybrid Stochastic Local Search Algorithms
for Permutation Flowshop Problems},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2018-005},
year = 2018,
month = apr,
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2018-005.pdf}
}
@misc{PagStu2018:pfsp-supp,
author = { Federico Pagnozzi and Thomas St{\"u}tzle },
title = {Automatic Design of Hybrid Stochastic Local Search Algorithms
for Permutation Flowshop Problems: Supplementary Material},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2018-002/}},
year = 2018
}
@misc{PagStu2019:pfsp-supp,
author = { Federico Pagnozzi and Thomas St{\"u}tzle },
title = {Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems with additional constraints},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2018-002/}},
year = 2019
}
@phdthesis{Pagnozzi2019PhD,
author = { Federico Pagnozzi },
title = {Automatic Design of Hybrid Stochastic Local Search Algorithms},
school = {IRIDIA, {\'E}cole polytechnique, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2019,
annote = {Supervised by Thomas St\"utzle}
}
@inproceedings{PanIshSha2020,
title = {Algorithm configurations of {MOEA/D} with an unbounded
external archive},
author = {Pang, Lie Meng and Ishibuchi, Hisao and Shang, Ke},
booktitle = {2020 IEEE International Conference on Systems, Man, and
Cybernetics (SMC)},
year = 2020,
organization = {IEEE},
pages = {1087--1094}
}
@incollection{PanVerLopBac2024transfer,
location = {Melbourne, Australia},
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2024},
publisher = {ACM Press},
year = 2024,
editor = { Julia Handl and Li, Xiaodong },
author = {Shuaiqun Pan and Diederick Vermetten and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas B{\"a}ck and Wang, Hao },
title = {Transfer Learning of Surrogate Models via Domain Affine
Transformation},
doi = {10.1145/3638529.3654032}
}
@misc{PanVerLopBac2024transfer-supp,
author = {Shuaiqun Pan and Diederick Vermetten and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas B{\"a}ck and Wang, Hao },
title = {Transfer Learning of Surrogate Models via Domain Affine
Transformation: Supplementary Material},
howpublished = {\url{https://doi.org/10.5281/zenodo.10608095}},
year = 2024,
publisher = {Zenodo}
}
@book{PapSte1982:ph,
author = { Christos H. Papadimitriou and Steiglitz, K.},
title = {Combinatorial Optimization -- Algorithms and
Complexity},
publisher = {Prentice Hall, Englewood Cliffs, NJ},
year = 1982
}
@inproceedings{PapYan2000focs,
publisher = {IEEE Computer Society Press},
year = 2000,
booktitle = {41st Annual Symposium on Foundations of Computer Science},
editor = {Avrim Blum},
author = { Christos H. Papadimitriou and Mihalis Yannakakis },
title = {On the Approximability of Trade-offs and Optimal
Access of Web Sources},
pages = {86--92},
doi = {10.1109/SFCS.2000.892068}
}
@mastersthesis{Paq2001:Msc,
author = { Lu{\'i}s Paquete },
title = {Algoritmos Evolutivos Multiobjectivo para
Afecta\c{c}\~ao de Recursos e sua Aplica\c{c}\~ao
\`a Gera\c{c}\~ao de Hor\'arios em Universidades
({Multiobjective} Evolutionary Algorithms for
Resource Allocation and their Application to
University Timetabling)},
school = {University of Algarve},
year = 2001,
note = {In Portuguese},
abstract = {The aim of this study is the application of
multiobjective evolutionary algorithms to resource
allocation problems, such as university examination
timetabling and course timetabling
problems. Usually, these problems are characterized
by multiple conflicting objectives. A multiobjective
formalization of these problems is presented, based
on goals and priorities. Various aspects of
evolutionary algorithms are proposed and studied for
these problems, particulary, selection methods and
types and parameters of mutation operator. The
choice of both representation and operators is made
so as not to favour excessively certain objectives
with respect to others at the level of the
exploration mechanism. A comparative study of
performance is presented for the proposed algorithms
by means of statistical inference, based on real
problems of the University of Algarve. The notion of
attainment functions is used as a base for the
assessment of performance of multiobjective
evolutionary algorithms. Finally, the evolution of
the solution cost during the runs is analysed by
means of attainment functions, as well.}
}
@phdthesis{Paq2005:PhD,
author = { Lu{\'i}s Paquete },
title = {Stochastic Local Search Algorithms for Multiobjective
Combinatorial Optimization: Methods and Analysis},
school = {FB Informatik, TU Darmstadt, Germany},
year = 2005
}
@incollection{PaqChiStu2004mmo,
year = 2004,
address = {Berlin\slash Heidelberg},
publisher = {Springer},
volume = 535,
series = {Lecture Notes in Economics and Mathematical Systems},
editor = { Xavier Gandibleux and Marc Sevaux and Kenneth S{\"o}rensen and V. {T'Kindt} },
booktitle = {Metaheuristics for Multiobjective Optimisation},
author = { Lu{\'i}s Paquete and Marco Chiarandini and Thomas St{\"u}tzle },
title = {{Pareto} Local Optimum Sets in the Biobjective Traveling
Salesman Problem: An Experimental Study},
pages = {177--199},
abstract = {In this article, we study {Pareto} local optimum sets for the
biobjective Traveling Salesman Problem applying
straightforward extensions of local search algorithms for the
single objective case. The performance of the local search
algorithms is illustrated by experimental results obtained
for well known benchmark instances and comparisons to methods
from literature. In fact, a 3-opt local search is able to
compete with the best performing metaheuristics in terms of
solution quality. Finally, we also present an empirical study
of the features of the solutions found by 3-opt on a set of
randomly generated instances. The results indicate the
existence of several clusters of near-optimal solutions that
are separated by only a few edges.},
keywords = {Pareto local search, PLS},
doi = {10.1007/978-3-642-17144-4_7}
}
@techreport{PaqFonLop06-CSI-klee,
author = { Lu{\'i}s Paquete and Carlos M. Fonseca and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {An optimal algorithm for a special case of {Klee}'s
measure problem in three dimensions},
institution = {CSI, Universidade do Algarve},
year = 2006,
number = {CSI-RT-I-01/2006},
abstract = {The measure of the region dominated by (the maxima
of) a set of $n$ points in the positive $d$-orthant
has been proposed as an indicator of performance in
multiobjective optimization, known as the
hypervolume indicator, and the problem of computing
it efficiently is attracting increasing
attention. In this report, this problem is
formulated as a special case of Klee's measure
problem in $d$ dimensions, which immediately
establishes $O(n^{d/2}\log n)$ as a, possibly
conservative, upper bound on the required
computation time. Then, an $O(n log n)$ algorithm
for the 3-dimensional version of this special case
is constructed, based on an existing dimension-sweep
algorithm for the related maxima problem. Finally,
$O(n log n)$ is shown to remain a lower bound on the
time required by the hypervolume indicator for
$d>1$, which attests the optimality of the algorithm
proposed.},
note = {Superseded by paper in IEEE Transactions on Evolutionary Computation~\cite{BeuFonLopPaqVah09:tec}},
annote = {Proof of Theorem 3.1 is incorrect}
}
@incollection{PaqStu08:lnems,
author = { Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {Clusters of non-dominated solutions in multiobjective
combinatorial optimization: An experimental analysis},
booktitle = {Multiobjective Programming and Goal Programming: Theoretical
Results and Practical Applications},
pages = {69--77},
year = 2009,
volume = 618,
series = {Lecture Notes in Economics and Mathematical Systems},
editor = {V. Barichard and M. Ehrgott and Xavier Gandibleux and V. T'Kindt},
publisher = {Springer},
address = { Berlin, Germany},
doi = {10.1007/978-3-540-85646-7}
}
@incollection{PaqStu2002:evocop,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 2279,
editor = {S. Cagnoni and others},
aeditor = {S. Cagnoni and J. Gottlieb and E. Hart and Martin Middendorf and G{\"u}nther R. Raidl },
year = 2002,
booktitle = {Applications of Evolutionary Computing,
Proceedings of EvoWorkshops 2002},
author = { Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {An Experimental Investigation of Iterated Local Search for Coloring Graphs},
pages = {122--131}
}
@incollection{PaqStu2003tpls,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 2632,
series = {Lecture Notes in Computer Science},
editor = { Carlos M. Fonseca and Peter J. Fleming and Eckart Zitzler and Kalyanmoy Deb and Lothar Thiele },
year = 2003,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2003},
author = { Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {A Two-Phase Local Search for the Biobjective
Traveling Salesman Problem},
pages = {479--493}
}
@incollection{PaqStu2018handbook,
author = { Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {Stochastic Local Search Algorithms for Multiobjective
Combinatorial Optimization: {A} Review},
booktitle = {Handbook of Approximation Algorithms and Metaheuristics},
pages = {411--425},
publisher = {Chapman \& Hall/CRC},
address = { Boca Raton, FL},
doi = {10.1201/9781351236423-24},
year = 2018,
editor = {Teofilo F. Gonzalez}
}
@techreport{PaqStuLop-IRIDIA-2005-029,
author = { Lu{\'i}s Paquete and Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {On the design and analysis of {SLS} algorithms for
multiobjective combinatorial optimization problems},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2005,
number = {TR/IRIDIA/2005-029},
abstract = {Effective Stochastic Local Search (SLS) algorithms
can be seen as being composed of several algorithmic
components, each of which plays some specific role
with respect to overall performance. In this
article, we explore the application of experimental
design techniques to analyze the effect of different
choices for these algorithmic components on SLS
algorithms applied to Multiobjective Combinatorial
Optimization Problems that are solved in terms of
{Pareto} optimality. This analysis is done using the
example application of SLS algorithms to the
biobjective Quadratic Assignment Problem and we show
also that the same choices for algorithmic
components can lead to different behavior in
dependence of various instance features, such as the
structure of input data and the correlation between
objectives.},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2005-029.pdf}
}
@inproceedings{PaqStuLop05mic,
address = {Vienna, Austria},
year = 2005,
booktitle = {6th Metaheuristics International Conference (MIC 2005)},
editor = { Karl F. Doerner and Michel Gendreau and Peter Greistorfer and Gutjahr, Walter J. and Richard F. Hartl and Marc Reimann },
author = { Lu{\'i}s Paquete and Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Towards the Empirical Analysis of {SLS} Algorithms
for Multiobjective Combinatorial Optimization
Problems through Experimental Design},
pages = {739--746},
abstract = { Stochastic Local Search (SLS) algorithms for
Multiobjective Combinatorial Optimization Problems
(MCOPs) typically involve the selection and
parameterization of many algorithm components whose
role with respect to their overall performance and
relation to certain instance features is often not
clear. In this abstract, we use a modular approach
for the design of SLS algorithms for MCOPs defined
in terms of {Pareto} optimality and we present an
extensive analysis of SLS algorithms through
experimental design techniques, where each algorithm
component is considered a factor. The experimental
analysis is based on a sound experimental
methodology for analyzing the output of algorithms
for MCOPs. We show that different choices for
algorithm components can lead to different behavior
in dependence of various instance features.}
}
@incollection{PaqStuLop07metaheuristics,
author = { Lu{\'i}s Paquete and Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Using experimental design to analyze stochastic local search
algorithms for multiobjective problems},
booktitle = {Metaheuristics: Progress in Complex Systems Optimization},
pages = {325--344},
year = 2007,
doi = {10.1007/978-0-387-71921-4_17},
volume = 39,
series = {Operations Research / Computer Science Interfaces},
publisher = {Springer},
address = { New York, NY},
annote = {Post-Conference Proceedings of the 6th Metaheuristics
International Conference (MIC 2005)},
editor = {Karl F. Doerner and Michel Gendreau and Peter Greistorfer and Gutjahr, Walter J. and Richard F. Hartl and Marc Reimann },
abstract = {Stochastic Local Search (SLS) algorithms can be seen as being
composed of several algorithmic components, each playing some
specific role with respect to overall performance. This
article explores the application of experimental design
techniques to analyze the effect of components of SLS
algorithms for Multiobjective Combinatorial Optimization
problems, in particular for the Biobjective Quadratic
Assignment Problem. The analysis shows that there exists a
strong dependence between the choices for these components
and various instance features, such as the structure of the
input data and the correlation between the objectives.}
}
@incollection{Paulli1993,
publisher = {Springer},
year = 1993,
editor = { Vidal, Ren{\'e} Victor Valqui },
booktitle = {Applied Simulated Annealing},
title = {A computational comparison of simulated annealing and tabu search applied to the quadratic assignment problem},
author = {Paulli, J},
pages = {85--102}
}
@incollection{PavDelKes2019,
doi = {10.1145/3321707},
isbn = {978-1-4503-6111-8},
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
author = {Pavelski, Lucas Marcondes and Delgado, Myriam Regattieri and Marie-El{\'e}onore Kessaci },
title = {Meta-Learning on Flowshop Using Fitness Landscape Analysis},
pages = {925--933}
}
@book{Pea84,
author = { Judea Pearl },
title = {Heuristics: Intelligent Search Strategies for Computer
Problem Solving},
publisher = {Addison-Wesley},
address = { Reading, MA},
year = 1984
}
@book{Pea93,
author = {Glen S. Peace},
title = {Taguchi Methods: A Hands-On Approach},
publisher = {Addison-Wesley},
year = 1993
}
@inproceedings{Pea2012uai,
year = 2013,
publisher = {AUAI Press},
editor = { Nando de Freitas and Murphy, Kevin},
booktitle = {Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence
(UAI'12), Catalina Island, CA August 14-18 2012},
title = {The do-calculus revisited},
author = { Judea Pearl },
pages = {4--11}
}
@inproceedings{PeaBar2011aaai,
year = 2011,
publisher = {{AAAI} Press},
booktitle = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
editor = {Wolfram Burgard and Dan Roth},
title = {Transportability of causal and statistical relations: A formal approach},
author = { Judea Pearl and Elias Bareinboim },
pages = {247--254}
}
@book{PeaMac2018,
title = {The book of why: the new science of cause and effect},
author = { Judea Pearl and Mackenzie, Dana},
year = 2018,
publisher = {Basic books}
}
@book{Pearl2009causality,
author = { Judea Pearl },
title = {Causality: Models, Reasoning and Inference},
year = 2009,
publisher = {Cambridge University Press},
edition = {2nd}
}
@incollection{PedCarCanVen2014,
author = {Juan A. Pedraza and Carlos Garc{\'i}a-Mart{\'i}nez and Alberto Cano and Sebasti\'an Ventura},
title = {Classification Rule Mining with Iterated Greedy},
booktitle = {Hybrid Artificial Intelligence Systems - 9th International Conference,
{HAIS} 2014, Salamanca, Spain, June 11-13, 2014. Proceedings},
publisher = {Springer},
address = { Heidelberg, Germany},
year = 2014,
editor = {Marios M. Polycarpou and Andr{\'{e}} Carlos Ponce Leon Ferreira de Carvalho and Jeng{-}Shyang Pan and Michal Wozniak and H{\'{e}}ctor Quinti{\'{a}}n and Emilio Corchado},
volume = 8480,
series = {Lecture Notes in Computer Science},
pages = {585--596}
}
@incollection{PedTak2013emco,
isbn = {978-3-642-37139-4},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2013},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 7811,
year = 2013,
publisher = {Springer},
editor = { Robin C. Purshouse and Peter J. Fleming and Carlos M. Fonseca and Salvatore Greco and Jane Shaw},
author = {Pedro, Luciana R. and Takahashi, R. H. C. },
title = {Decision-Maker Preference Modeling in Interactive
Multiobjective Optimization},
pages = {811--824},
doi = {10.1007/978-3-642-37140-0_60},
keywords = {decision-maker, interactive, neural networks}
}
@incollection{PelBir2007:slse,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 4638,
editor = { Thomas St{\"u}tzle and Mauro Birattari and Holger H. Hoos },
year = 2007,
booktitle = {Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics. SLS~2007},
author = { Paola Pellegrini and Mauro Birattari },
title = {Implementation Effort and Performance},
pages = {31--45}
}
@incollection{PelFavMor06,
address = { Heidelberg, Germany},
publisher = {Springer},
year = 2006,
volume = 4150,
series = {Lecture Notes in Computer Science},
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and L. M. Gambardella and Mauro Birattari and
Martinoli, A. and Poli, R. and Thomas St{\"u}tzle },
booktitle = {Ant Colony Optimization and Swarm Intelligence, 5th
International Workshop, ANTS 2006},
author = { Paola Pellegrini and D. Favaretto and E. Moretti },
title = {On {\MaxMinAntSystem}'s Parameters},
pages = {203--214}
}
@incollection{PelFavMor09:NICSO,
doi = {10.1007/978-3-642-03211-0},
editor = {Natalio Krasnogor and Belén Melián-Batista and José
Andrés Moreno-Pérez and J. Marcos Moreno-Vega and David Alejandro Pelta},
address = { Berlin, Germany},
volume = 236,
series = {Studies in Computational Intelligence},
year = 2009,
publisher = {Springer},
booktitle = {Nature Inspired Cooperative Strategies for Optimization
(NICSO 2008)},
author = { Paola Pellegrini and D. Favaretto and E. Moretti },
title = {Exploration in stochastic algorithms: An application
on {\MaxMinAntSystem}},
pages = {1--13}
}
@incollection{PelStuBir2010,
volume = 6234,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
fulleditor = { Marco Dorigo and Mauro Birattari and Gianni A. {Di Caro} and Doursat, R. and Engelbrecht, A. P. and Floreano,
D. and Gambardella, L. M. and Gro\ss, R. and Sahin,
E. and Thomas St{\"u}tzle and Sayama, H.},
editor = { Marco Dorigo and others},
year = 2010,
booktitle = {Swarm Intelligence, 7th International Conference, ANTS 2010},
author = { Paola Pellegrini and Thomas St{\"u}tzle and Mauro Birattari },
title = {Off-line vs. On-line Tuning: A Study on {\MaxMinAntSystem}
for the {TSP}},
pages = {239--250},
doi = {10.1007/978-3-642-15461-4_21}
}
@incollection{PerBisStu2017rf,
address = { New York, NY},
publisher = {ACM Press},
year = 2017,
editor = { Peter A. N. Bosman },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2017},
author = { P{\'e}rez C{\'a}ceres, Leslie and Bernd Bischl and Thomas St{\"u}tzle },
title = {Evaluating random forest models for irace},
pages = {1146--1153},
doi = {10.1145/3067695.3082057}
}
@incollection{PerLopHooStu2017:lion,
address = { Cham, Switzerland},
series = {Lecture Notes in Computer Science},
volume = 10556,
booktitle = {Learning and Intelligent Optimization, 11th International Conference, LION 11},
publisher = {Springer},
year = 2017,
editor = { Roberto Battiti and Dmitri E. Kvasov and Yaroslav D. Sergeyev},
author = { P{\'e}rez C{\'a}ceres, Leslie and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Holger H. Hoos and Thomas St{\"u}tzle },
title = {An Experimental Study of Adaptive Capping in {\rpackage{irace}}},
pages = {235--250},
doi = {10.1007/978-3-319-69404-7_17},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2016-007/}
}
@misc{PerLopHooStu2017:lion-supp,
author = { P{\'e}rez C{\'a}ceres, Leslie and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Holger H. Hoos and Thomas St{\"u}tzle },
title = {An experimental study of adaptive capping in irace: Supplementary material},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2016-007/}},
year = 2017
}
@incollection{PerLopStu2014ants,
volume = 8667,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Marco Dorigo and others},
year = 2014,
booktitle = {Swarm Intelligence, 9th International Conference, ANTS 2014},
author = { P{\'e}rez C{\'a}ceres, Leslie and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Ant Colony Optimization on a Budget of 1000},
doi = {10.1007/978-3-319-09952-1_5},
pages = {50--61}
}
@incollection{PerLopStu2014evocop,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 8600,
series = {Lecture Notes in Computer Science},
year = 2014,
booktitle = {Proceedings of EvoCOP 2014 -- 14th European Conference on Evolutionary Computation in Combinatorial Optimization },
editor = { Christian Blum and Gabriela Ochoa },
author = { P{\'e}rez C{\'a}ceres, Leslie and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An Analysis of Parameters of irace},
doi = {10.1007/978-3-662-44320-0_4},
pages = {37--48}
}
@misc{PerLopStu2015budget-supp,
author = { P{\'e}rez C{\'a}ceres, Leslie and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Ant Colony Optimization on a Budget of 1000: {Supplementary} material},
url = {http://iridia.ulb.ac.be/supp/IridiaSupp2015-004},
year = 2015
}
@incollection{PerPagFraStu2017gcc,
address = { Heidelberg, Germany},
publisher = {Springer},
editor = {Lutton, Evelyne and Legrand, Pierrick and Parrend, Pierre and Nicolas Monmarch{\'e} and Marc Schoenauer },
volume = 10764,
series = {Lecture Notes in Computer Science},
year = 2017,
booktitle = {EA 2017: Artificial Evolution},
author = { P{\'e}rez C{\'a}ceres, Leslie and Federico Pagnozzi and Alberto Franzin and Thomas St{\"u}tzle },
title = {Automatic Configuration of {GCC} Using {\rpackage{irace}}},
pages = {202--216},
abstract = {Automatic algorithm configuration techniques have proved to
be successful in finding performance-optimizing parameter
settings of many search-based decision and optimization
algorithms. A recurrent, important step in software
development is the compilation of source code written in some
programming language into machine-executable code. The
generation of performance-optimized machine code itself is a
difficult task that can be parametrized in many different
possible ways. While modern compilers usually offer different
levels of optimization as possible defaults, they have a
larger number of other flags and numerical parameters that
impact properties of the generated machine-code. While the
generation of performance-optimized machine code has received
large attention and is dealt with in the research area of
auto-tuning, the usage of standard automatic algorithm
configuration software has not been explored, even though, as
we show in this article, the performance of the compiled code
has significant stochasticity, just as standard optimization
algorithms. As a practical case study, we consider the
configuration of the well-known GNU compiler collection (GCC)
for minimizing the run-time of machine code for various
heuristic search methods. Our experimental results show that,
depending on the specific code to be optimized, improvements
of up to 40{\%} of execution time when compared to the -O2
and -O3 optimization flags is possible.},
doi = {10.1007/978-3-319-78133-4_15}
}
@misc{PerPagFraStu2017gccsup,
author = { P{\'e}rez C{\'a}ceres, Leslie and Federico Pagnozzi and Alberto Franzin and Thomas St{\"u}tzle },
title = {Automatic configuration of {GCC} using irace: Supplementary
material},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2017-009/}},
year = 2017
}
@phdthesis{Perez-Caceres2017phd,
author = { P{\'e}rez C{\'a}ceres, Leslie and Thomas St{\"u}tzle },
title = {Automatic Algorithm Configuration: Analysis, Improvements and
Applications},
school = {IRIDIA, {\'E}cole polytechnique, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2017,
annote = {Supervised by Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
epub = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/262048}
}
@incollection{PetEve2002control,
publisher = {Morgan Kaufmann Publishers, San Francisco, CA},
editor = { Langdon, William B. and others},
year = 2002,
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2002},
title = {Controlling genetic algorithms with reinforcement learning},
author = {Pettinger, James E. and Everson, Richard M. },
pages = {692--692}
}
@book{PetJanSch2017,
title = {Elements of causal inference: foundations and learning algorithms},
author = {Peters, Jonas and Janzing, Dominik and Sch{\"o}lkopf, Bernhard},
year = 2017,
publisher = {MIT Press}
}
@incollection{PhiBhaPas2021portfolio,
author = {Phillipson, Frank and Bhatia, Harshil Singh},
title = {Portfolio Optimisation Using the {D-Wave} Quantum Annealer},
booktitle = {Computational Science -- ICCS 2021},
publisher = {Springer International Publishing},
year = 2021,
editor = {Paszynski, Maciej and Kranzlm{\"u}ller, Dieter and
Krzhizhanovskaya, Valeria V. and Dongarra, Jack J. and
Sloot, Peter M. A.},
pages = {45--59},
address = { Cham, Switzerland}
}
@inproceedings{PihMus2014,
publisher = {IEEE Press},
year = 2014,
editor = {Papadopoulos, George Angelos},
booktitle = {26th {IEEE} International Conference on Tools with Artificial Intelligence,
{ICTAI} 2014, Limassol, Cyprus, November 10-12, 2014},
title = {Application of Machine Learning to Algorithm Selection for {TSP}},
author = {Pihera, Josef and Musliu, Nysret },
pages = {47--54}
}
@incollection{PilWhi02,
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and Gianni A. {Di Caro} and M. Sampels },
volume = 2463,
series = {Lecture Notes in Computer Science},
year = 2002,
booktitle = {Ant Algorithms, Third International Workshop, ANTS
2002},
author = {M. L. Pilat and T. White},
title = {Using Genetic Algorithms to optimize {ACS-TSP}},
pages = {282--287}
}
@book{Pin2012,
author = {Michael L. Pinedo},
title = {Scheduling: Theory, Algorithms, and Systems},
publisher = {Springer},
year = 2012,
edition = {4th},
address = { New York, NY}
}
@incollection{PinRunSoup07:maxsat,
author = {Pedro Pinto and Thomas Runkler and Jo{\~a}o Sousa},
title = {Ant Colony Optimization and its Application to
Regular and Dynamic {MAX-SAT} Problems},
booktitle = {Advances in Biologically Inspired Information
Systems},
year = 2007,
volume = 69,
pages = {285--304},
series = {Studies in Computational Intelligence},
doi = {10.1007/978-3-540-72693-7_15},
publisher = {Springer},
address = { Berlin, Germany},
abstract = {In this chapter we discuss the ant colony
optimization meta-heuristic {(ACO)} and its
application to static and dynamic constraint
satisfaction optimization problems, in particular
the static and dynamic maximum satisfiability
problems {(MAX-SAT).} In the first part of the
chapter we give an introduction to meta-heuristics
in general and ant colony optimization in
particular, followed by an introduction to
constraint satisfaction and static and dynamic
constraint satisfaction optimization problems.
Then, we describe how to apply the {ACO} algorithm
to the problems, and do an analysis of the results
obtained for several benchmarks. The adapted ant
colony optimization accomplishes very well the task
of dealing with systematic changes of dynamic
{MAX-SAT} instances derived from static problems. }
}
@misc{PinSin2020checklist,
author = {Joelle Pineau and Koustuv Sinha},
title = {The Machine Learning Reproducibility Checklist (v2.0)},
howpublished = {\url{https://www.cs.mcgill.ca/~jpineau/ReproducibilityChecklist-v2.0.pdf}},
year = 2020,
annote = {Used in NeurIPS 2020}
}
@incollection{PisRop2010:handbook,
address = { New York, NY},
publisher = {Springer},
edition = {2nd},
series = {International Series in Operations Research \& Management
Science},
volume = 146,
booktitle = {Handbook of Metaheuristics},
year = 2010,
editor = { Michel Gendreau and Jean-Yves Potvin },
title = {Large Neighborhood Search},
author = { David Pisinger and Stefan Ropke },
pages = {399--419}
}
@incollection{PitBehAff2013,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
year = 2013,
volume = 7832,
booktitle = {Proceedings of EvoCOP 2013 -- 13th European Conference on Evolutionary Computation in Combinatorial Optimization },
editor = { Martin Middendorf and Christian Blum },
author = {Pitzer, Erik and Beham, Andreas and Affenzeller, Michael},
title = {Automatic Algorithm Selection for the Quadratic Assignment
Problem Using Fitness Landscape Analysis},
pages = {109--120}
}
@inproceedings{PloMelVarBucZhuLee2013tact,
series = {Procedia Computer Science},
year = 2013,
volume = 18,
publisher = {Elsevier},
editor = {Vassil Alexandrov and Michael Lees and Valeria Krzhizhanovskaya and Jack Dongarra and Peter M. A. Sloot},
booktitle = {2013 International Conference on Computational Science},
author = {Dmitry Plotnikov and Dmitry Melnik and Mamikon Vardanyan and
Ruben Buchatskiy and Roman Zhuykov and Je-Hyung Lee},
title = {Automatic Tuning of Compiler Optimizations and Analysis of
their Impact},
pages = {1312--1321},
doi = {10.1016/j.procs.2013.05.298}
}
@book{Plotnick2009book,
author = {Robert Plotnick},
title = {The Genie in the Machine: How Computer-Automated Inventing Is
Revolutionizing Law and Business},
publisher = {Stanford Law Books},
year = 2009,
annote = {Mentions evolutionary optimization of Oral-B toothbrushes}
}
@techreport{Pow2009bobyqa,
author = { Powell, Michael J. D.},
title = {The {BOBYQA} algorithm for bound constrained optimization
without derivatives},
institution = {University of Cambridge, UK},
year = 2009,
number = {Cambridge NA Report NA2009/06},
epub = {http://www6.cityu.edu.hk/rcms/publications/preprint26.pdf}
}
@incollection{Powell1994cobyla,
author = { Powell, Michael J. D.},
title = {A Direct Search Optimization Method That Models the Objective
and Constraint Functions by Linear Interpolation},
booktitle = {Advances in Optimization and Numerical Analysis},
publisher = {Springer},
year = 1994,
pages = {51--67},
address = { Dordrecht, The Netherlands},
annote = {Proposed COBYLA},
isbn = 9789401583305,
doi = {10.1007/978-94-015-8330-5_4}
}
@inproceedings{PraTraWanBaeKer2020,
publisher = {IEEE Press},
year = 2020,
booktitle = {2020 {IEEE} Symposium Series on Computational Intelligence, {SSCI}
2020, Canberra, Australia, December 1-4, 2020},
editor = { Carlos A. {Coello Coello} },
title = {Per-Instance Configuration of the Modularized {CMA-ES}
by Means of Classifier Chains and Exploratory Landscape Analysis},
author = {Prager, Raphael Patrick and Heike Trautmann and Wang, Hao and Thomas B{\"a}ck and Pascal Kerschke },
pages = {996--1003}
}
@inproceedings{PraYao2006taa,
title = {A new multi-objective evolutionary optimisation algorithm:
the two-archive algorithm},
author = {Praditwong, Kata and Xin Yao },
booktitle = {International Conference on Computational Intelligence and
Security},
volume = 1,
pages = {286--291},
year = 2006,
organization = {IEEE}
}
@incollection{Prasad03,
publisher = {CRC Press},
year = 2003,
booktitle = {Advances in Water Supply Management},
editor = { C. Maksimovi{\'c} and David Butler and Fayyaz Ali Memon },
author = { T. Devi Prasad and Godfrey A. Walters },
title = {Optimal rerouting to minimise residence times in
water distribution networks},
pages = {299--306}
}
@book{PrepSha1988:compgeom,
author = {F. P. Preparata and M. I. Shamos},
title = {Computational Geometry. An Introduction},
publisher = {Springer},
address = { Berlin, Germany},
year = 1988,
edition = {2nd}
}
@incollection{PriAllLop2022gecco,
location = {Boston, Massachusetts},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2022},
address = { New York, NY},
year = 2022,
publisher = {ACM Press},
editor = { Jonathan E. Fieldsend and Markus Wagner },
author = { Pricopie, Stefan and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Fare,
Clyde and Benatan, Matt and Joshua D. Knowles },
title = {Expensive Optimization with Production-Graph Resource
Constraints: A First Look at a New Problem Class},
doi = {10.1145/3512290.3528741},
abstract = {We consider a new class of expensive, resource-constrained
optimization problems (here arising from molecular discovery)
where costs are associated with the experiments (or
evaluations) to be carried out during the optimization
process. In the molecular discovery problem, candidate
compounds to be optimized must be synthesized in an iterative
process that starts from a set of purchasable items and
builds up to larger molecules. To produce target molecules,
their required resources are either used from
already-synthesized items in storage or produced themselves
on-demand at an additional cost. Any remaining resources from
the production process are stored for reuse for the next
evaluations. We model these resource dependencies with a
directed acyclic production graph describing the development
process from granular purchasable items to evaluable target
compounds. Moreover, we develop several resource-eficient
algorithms to address this problem. In particular, we develop
resource-aware variants of Random Search heuristics and of
Bayesian Optimization and analyze their performance in terms
of anytime behavior. The experimental results were obtained
from a real-world molecular optimization problem. Our results
suggest that algorithms that encourage exploitation by
reusing existing resources achieve satisfactory results while
using fewer resources overall.},
pages = {840--848},
numpages = 9,
keywords = {molecular discovery, resource constraints, expensive
optimization, production costs}
}
@incollection{PriAllLop2024ppsn,
address = { Cham, Switzerland},
series = {Lecture Notes in Computer Science},
volume = 15149,
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XVIII}},
publisher = {Springer},
year = 2024,
editor = {Michael Affenzeller and Stephan M. Winkler and Anna
V. Kononova and Heike Trautmann and Tea Tu{\v s}ar and Penousal Machado and Thomas B{\"a}ck },
author = { Pricopie, Stefan and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Fare,
Clyde and Benatan, Matt and Joshua D. Knowles },
title = {An Adaptive Approach to Bayesian Optimization with Setup
Switching Costs},
pages = {340--355},
abstract = {Black-box optimization methods typically assume that
evaluations of the black-box objective function are equally
costly to evaluate. We investigate here a
resource-constrained setting where changes to certain
decision variables of the search space incur a higher
switching cost, e.g., due to expensive changes to the
experimental setup. In this scenario, there is a trade-off
between fixing the values of those costly variables or
accepting this additional cost to explore more of the search
space. We adapt two process-constrained batch algorithms to
this sequential problem formulation, and propose two new
methods: one one cost-aware and one cost-ignorant. We
validate and compare the algorithms using a set of 7 scalable
test functions with different switching-cost settings. Our
proposed cost-aware parameter-free algorithm yields
comparable results to tuned process-constrained algorithms in
all settings we considered, suggesting some degree of
robustness to varying landscape features and cost
trade-offs. This method starts to outperform the other
algorithms with increasing switching cost. Our work expands
on other recent Bayesian Optimization studies in
resource-constrained settings that consider a batch setting
only. Although the contributions of this work are relevant to
the general class of resource-constrained problems, they are
particularly relevant to problems where adaptability to
varying resource availability is of high importance.},
doi = {10.1007/978-3-031-70068-2_21}
}
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publisher = {Springer},
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title = {Heatmap visualization of population based multi objective
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pages = {361--375}
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address = { Heidelberg, Germany},
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year = 2003,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2003},
author = { Gregorio {Toscano Pulido} and Carlos A. {Coello Coello} },
title = {The Micro Genetic Algorithm 2: Towards Online
Adaptation in Evolutionary Multiobjective
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pages = {252--266},
doi = {10.1007/3-540-36970-8_18}
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institution = {Computational Optimization and Innovation (COIN) Laboratory, University of Michigan, USA},
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month = jan
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author = { Robin C. Purshouse and Peter J. Fleming },
doi = {10.1109/CEC.2003.1299927},
pages = {2066--2073},
annote = {First to mention NSGA-II failure to deal with
many-objectives. Mentions exponential number of nondominated
solutions with respect to many objectives (but
\cite{FarAma2002nafips} already did).}
}
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volume = 11101,
year = 2018,
address = { Cham, Switzerland},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Anne Auger and Carlos M. Fonseca and Louren{\c c}o, N. and Penousal Machado and Lu{\'i}s Paquete and Darrell Whitley },
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XV}},
author = {Yasha Pushak and Holger H. Hoos },
title = {Algorithm Configuration Landscapes: More Benign Than
Expected?},
pages = {271--283},
doi = {10.1007/978-3-319-99259-4_22},
supplement = {http://www.cs.ubc.ca/labs/beta/Projects/ACLandscapes/},
annote = {Best paper award at PPSN2018}
}
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epub = {https://dl.acm.org/citation.cfm?id=3377930},
location = {Canc{\'u}n, Mexico},
doi = {10.1145/3377930},
isbn = {978-1-4503-7128-5},
address = { New York, NY},
publisher = {ACM Press},
year = 2020,
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title = {Golden parameter search: exploiting structure to quickly
configure parameters in parallel},
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keywords = {algorithm configuration}
}
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@book{Rec1973,
author = { Rechenberg, Ingo },
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year = 1973
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year = 2015,
pages = {249--263},
author = { Ruiz, Ana Bel{\'e}n and Mariano Luque and Kaisa Miettinen and Rub{\'{e}}n Saborido },
title = {An Interactive Evolutionary Multiobjective Optimization
Method: Interactive {WASF}-{GA}}
}
@incollection{RuiValFer2009,
title = {Scheduling in flowshops with no-idle machines},
author = { Rub{\'e}n Ruiz and Eva Vallada and Fern{\'a}ndez-Mart{\'i}nez, Carlos},
booktitle = {Computational intelligence in flow shop and job shop scheduling},
pages = {21--51},
year = 2009,
publisher = {Springer}
}
@inproceedings{Rum01:ijcai,
publisher = {IEEE Press},
year = 2001,
booktitle = {Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI-01)},
editor = {Bernhard Nebel},
author = {W. Ruml},
title = {Incomplete Tree Search using Adaptive Probing},
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year = 2003,
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@incollection{Rust1994mdp,
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System Simulation},
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address = {Barcelona, Spain},
organization = {ThinkMind}
}
@incollection{SaiLopMie2019gecco,
isbn = {978-1-4503-6748-6},
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
author = { Saini, Bhupinder Singh and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Kaisa Miettinen },
title = {Automatic Surrogate Modelling Technique Selection based on
Features of Optimization Problems},
doi = {10.1145/3319619.3326890},
pages = {1765--1772},
abstract = {A typical scenario when solving industrial single or
multiobjective optimization problems is that no explicit
formulation of the problem is available. Instead, a dataset
containing vectors of decision variables together with their
objective function value(s) is given and a surrogate model
(or metamodel) is build from the data and used for
optimization and decision-making. This data-driven
optimization process strongly depends on the ability of the
surrogate model to predict the objective value of decision
variables not present in the original dataset. Therefore, the
choice of surrogate modelling technique is crucial. While
many surrogate modelling techniques have been discussed in
the literature, there is no standard procedure that will
select the best technique for a given problem. In this work,
we propose the automatic selection of a surrogate modelling
technique based on exploratory landscape features of the
optimization problem that underlies the given dataset. The
overall idea is to learn offline from a large pool of
benchmark problems, on which we can evaluate a large number
of surrogate modelling techniques. When given a new dataset,
features are used to select the most appropriate surrogate
modelling technique. The preliminary experiments reported
here suggest that the proposed automatic selector is able to
identify high-accuracy surrogate models as long as an
appropriate classifier is used for selection.}
}
@incollection{SakTakKaw2010,
title = {A method to control parameters of evolutionary algorithms by
using reinforcement learning},
author = {Sakurai, Yoshitaka and Takada, Kouhei and Kawabe, Takashi and
Tsuruta, Setsuo},
booktitle = {2010 Sixth International Conference on Signal-Image
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publisher = {IEEE}
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@incollection{Sakarya99,
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key = {WCCI},
author = {Valentino Santucci and Marco Baioletti},
title = {A Fast Randomized Local Search for Low Budget Optimization in
Black-Box Permutation Problems},
keywords = {UMM, CEGO}
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@incollection{SaxDeb2007nonlinear,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 4403,
fulleditor = {Obayashi, Shigeru and Kalyanmoy Deb and Poloni, Carlo and Hiroyasu, Tomoyuki and Murata, Tadahiko},
editor = {S. Obayashi and others},
year = 2007,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2007},
author = { Saxena, Dhish Kumar and Kalyanmoy Deb },
title = {Non-linear Dimensionality Reduction Procedures for Certain
Large-Dimensional Multi-objective Optimization Problems:
Employing Correntropy and a Novel Maximum Variance Unfolding},
doi = {10.1007/978-3-540-70928-2_58},
pages = {772--787},
abstract = {In our recent publication, we began with an understanding
that many real-world applications of multi-objective
optimization involve a large number (10 or more) of
objectives but then, existing evolutionary multi-objective
optimization (EMO) methods have primarily been applied to
problems having smaller number of objectives (5 or
less). After highlighting the major impediments in handling
large number of objectives, we proposed a principal component
analysis (PCA) based EMO procedure, for dimensionality
reduction, whose efficacy was demonstrated by solving upto
50-objective optimization problems. Here, we are addressing
the fact that, when the data points live on a non-linear
manifold or that the data structure is non-gaussian, PCA
which yields a smaller dimensional 'linear' subspace may be
ineffective in revealing the underlying dimensionality. To
overcome this, we propose two new non-linear dimensionality
reduction algorithms for evolutionary multi-objective
optimization, namely C-PCA-NSGA-II and MVU-PCA-NSGA-II. While
the former is based on the newly introduced correntropy PCA
[2], the later implements maximum variance unfolding
principle [3,4,5] in a novel way. We also establish the
superiority of these new EMO procedures over the earlier
PCA-based procedure, both in terms of accuracy and
computational time, by solving upto 50-objective optimization
problems.}
}
@inproceedings{SaxDeb2007trading,
address = {Piscataway, NJ},
publisher = {IEEE Press},
year = 2007,
booktitle = {Proceedings of the 2007 Congress on Evolutionary Computation (CEC 2007)},
key = {IEEE CEC},
author = { Saxena, Dhish Kumar and Kalyanmoy Deb },
title = {Trading on infeasibility by exploiting constraint's
criticality through multi-ob\-jec\-ti\-vi\-za\-tion: A system
design perspective},
pages = {919--926},
doi = {10.1109/CEC.2007.4424568},
keywords = {multi-objectivization}
}
@inproceedings{SaxDeb2008dimensionality,
address = {Piscataway, NJ},
publisher = {IEEE Press},
year = 2008,
booktitle = {Proceedings of the 2008 Congress on Evolutionary Computation (CEC 2008)},
key = {IEEE CEC},
author = { Saxena, Dhish Kumar and Kalyanmoy Deb },
title = {Dimensionality reduction of objectives and constraints in
multi-objective optimization problems: A system design
perspective},
pages = {3204--3211},
doi = {10.1109/CEC.2008.4631232}
}
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isbn = 0805804269,
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@book{SchHer2021archiving,
title = {Archiving Strategies for Evolutionary Multi-objective
Optimization Algorithms},
author = { Oliver Sch{\"u}tze and Carlos Hern{\'a}ndez },
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year = 2021
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@incollection{SchHoo2012quanti,
address = { Heidelberg, Germany},
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Configuration},
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Optimization; Algorithm Configuration},
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}
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clustering},
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year = 2015
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@incollection{SchLip2011age,
title = {Age-Fitness {Pareto} Optimization},
author = {Schmidt, Michael and Lipson, Hod},
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series = {Lecture Notes in Computer Science},
fulleditor = {Albert Bifet and Michael May and Bianca Zadrozny and Ricard
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Jaime S. Cardoso and Myra Spiliopoulou},
booktitle = {Machine Learning and Knowledge Discovery in Databases, ECML
PKDD 2015},
key = {ECML PKDD},
title = {Safe Exploration for Active Learning with {Gaussian}
Processes},
author = {Schreiter, Jens and Nguyen-Tuong, Duy and Eberts, Mona and
Bischoff, Bastian and Markert, Heiner and Toussaint, Marc},
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doi = {10.1007/978-3-319-23461-8_9},
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and Schreiter, Jens and Meister, Mona and Nguyen-Tuong, Duy
and Nelles, Oliver},
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pages = {286--292},
year = 2018,
doi = {10.3384/ecp17142286},
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volume = 2
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@incollection{SeaDeb2015unsga3,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2015 Part {I}},
address = { Heidelberg, Germany},
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year = 2015,
publisher = {Springer},
editor = { Ant{\'o}nio Gaspar{-}Cunha and Carlos Henggeler Antunes and Carlos A. {Coello Coello} },
author = {Seada, Haitham and Kalyanmoy Deb },
title = {{U-NSGA-III}: A Unified Evolutionary Optimization Procedure
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booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XVI}},
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@incollection{ShaIshChe2021greedy,
location = {Lille, France},
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booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021},
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title = {Greedy approximated hypervolume subset selection for
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@incollection{ShaIshNan2021subset,
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location = {Lille, France},
address = { New York, NY},
publisher = {ACM Press},
year = 2021,
editor = { Chicano, Francisco and Krzysztof Krawiec },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021},
title = {Distance-based subset selection revisited},
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@incollection{ShaKomLopKaz2019gecco,
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publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
author = { Mudita Sharma and Alexandros Komninos and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Dimitar Kazakov },
title = {Deep Reinforcement Learning-Based Parameter Control in
Differential Evolution},
pages = {709--717},
supplement = {https://dx.doi.org/10.5281/zenodo.2628228},
doi = {10.1145/3321707.3321813},
keywords = {DE-DDQN}
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@incollection{ShaLopAllKno2023emo,
address = { Cham, Switzerland},
series = {Lecture Notes in Computer Science},
volume = 13970,
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publisher = {Springer International Publishing},
year = 2023,
editor = { Emmerich, Michael T. M. and others},
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title = {An Interactive Decision Tree-Based Evolutionary
Multi-Objective Algorithm: Supplementary material},
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doi = {10.1007/978-3-031-27250-9_44}
}
@misc{ShaLopAllKno2023emo-supp,
author = { Shavarani, Seyed Mahdi and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Allmendinger, Richard and Joshua D. Knowles },
title = {An Interactive Decision Tree-Based Evolutionary
Multi-Objective Algorithm: Supplementary material},
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volume = 11102,
year = 2018,
address = { Cham, Switzerland},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Anne Auger and Carlos M. Fonseca and Louren{\c c}o, N. and Penousal Machado and Lu{\'i}s Paquete and Darrell Whitley },
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XV}},
author = { Mudita Sharma and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Dimitar Kazakov },
title = {Performance Assessment of Recursive Probability Matching for
Adaptive Operator Selection in Differential Evolution},
supplement = {https://github.com/mudita11/AOS-comparisons},
doi = {10.1007/978-3-319-99259-4_26},
pages = {321--333},
keywords = {Rec-PM}
}
@misc{ShaLopKaz2018ppsn-supp,
author = { Mudita Sharma and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Dimitar Kazakov },
title = {Performance Assessment of Recursive Probability Matching for
Adaptive Operator Selection in Differential Evolution:
Supplementary material},
howpublished = {\url{https://github.com/mudita11/AOS-comparisons}},
doi = {10.5281/zenodo.1257672},
year = 2018
}
@incollection{ShaLopKno2021gecco,
location = {Lille, France},
address = { New York, NY},
publisher = {ACM Press},
year = 2021,
editor = { Chicano, Francisco and Krzysztof Krawiec },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021},
author = { Shavarani, Seyed Mahdi and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Joshua D. Knowles },
title = {Realistic Utility Functions Prove Difficult for
State-of-the-Art Interactive Multiobjective Optimization
Algorithms},
pages = {457--465},
doi = {10.1145/3449639.3459373}
}
@misc{ShaLopKno2023bench-supp,
author = { Shavarani, Seyed Mahdi and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Joshua D. Knowles },
title = {On Benchmarking Interactive Evolutionary Multi-Objective
Algorithms: Supplementary material},
howpublished = {\url{https://doi.org/10.5281/zenodo.7863301}},
year = 2023
}
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title = {Multi-Objective Optimisation under Deep Uncertainty},
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publisher = {Springer},
volume = 1447,
series = {Lecture Notes in Computer Science},
editor = {V. W. Porto and N. Saravanan and D. Waagen and Agoston E. Eiben },
booktitle = {Evolutionary Programming VII},
author = { Shi, Yuhui and Eberhart, Russell C. },
title = {Parameter selection in particle swarm optimization},
pages = {591--600}
}
@book{Shipley2000,
author = {B. Shipley},
title = {Cause and Correlation in Biology: a User's Guide to Path
Analysis, Structural Equations and Causal Inference},
publisher = {Cambridge University Press},
year = 2000,
edition = {1st}
}
@incollection{ShmAguHoo2002:ants,
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and Gianni A. {Di Caro} and M. Sampels },
volume = 2463,
series = {Lecture Notes in Computer Science},
year = 2002,
booktitle = {Ant Algorithms, Third International Workshop, ANTS
2002},
author = {A. Shmygelska and R. Aguirre-Hern{\'a}ndez and Holger H. Hoos },
title = {An Ant Colony Optimization Algorithm for the {2D HP} Protein
Folding Problem},
pages = {40--52}
}
@book{Sid1982optimal,
title = {Optimal Engineering Design: Principles and Applications},
author = {Siddall, James N.},
year = 1982,
publisher = {Marcel Dekker Inc.},
address = { New York, NY}
}
@book{SieCas1988,
author = { Sydney Siegel and Castellan, Jr, N. John },
title = {Non Parametric Statistics for the Behavioral Sciences},
publisher = {McGraw Hill},
year = 1988,
edition = {2nd},
address = { New York, NY}
}
@incollection{SilCalFraBer2021,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2021},
address = { New York, NY},
year = 2021,
publisher = {ACM Press},
editor = { Chicano, Francisco and Krzysztof Krawiec },
author = { Silva-Mu\~noz, Mois\'es and Calderon, Gonzalo and Alberto Franzin and Hughes Bersini },
title = {Determining a consistent experimental setup for benchmarking
and optimizing databases},
pages = {1614--1621},
doi = {10.1145/3449726.3463180}
}
@incollection{SilRunSouPal2002:ants,
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and Gianni A. {Di Caro} and M. Sampels },
volume = 2463,
series = {Lecture Notes in Computer Science},
year = 2002,
booktitle = {Ant Algorithms, Third International Workshop, ANTS
2002},
author = {C. A. Silva and T. A. Runkler and J. M. Sousa and R. Palm},
title = {Ant Colonies as Logistic Processes Optimizers},
pages = {76--87}
}
@incollection{SimIzzHaas2017multi,
address = { Heidelberg, Germany},
publisher = {Springer},
doi = {10.1007/978-3-319-55453-2},
volume = 10197,
series = {Lecture Notes in Computer Science},
year = 2017,
booktitle = {Proceedings of EvoCOP 2017 -- 17th European Conference on Evolutionary Computation in Combinatorial Optimization },
editor = { Bin Hu and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
author = { Sim{\~o}es, Lu{\'i}s F. and Dario Izzo and Haasdijk, Evert and Agoston E. Eiben },
title = {Multi-rendezvous Spacecraft Trajectory Optimization with
{Beam} {P-ACO}},
pages = {141--156}
}
@incollection{Simpson99,
address = {Baldock, United Kingdom},
publisher = { Research Studies Press Ltd. },
volume = 2,
year = 1999,
booktitle = {Water Industry Systems: Modelling and Optimization
Applications},
editor = { Dragan A. Savic and Godfrey A. Walters },
author = { Angus R. Simpson and D. C. Sutton and D. S. Keane and S. J. Sherriff },
title = {Optimal control of pumping at a water filtration
plant using genetic algorithms}
}
@misc{Slo11emo,
title = {Inducing preference models from pairwise
comparisons: implications for preference-guided
{EMO}},
year = 2011,
author = { Roman S{\l}owi{\'n}ski },
howpublished = {Evolutionary Multi-Criterion Optimization, EMO 2011},
note = {Keynote talk}
}
@inproceedings{SmiEib2009cec,
address = {Piscataway, NJ},
publisher = {IEEE Press},
year = 2009,
booktitle = {Proceedings of the 2009 Congress on Evolutionary Computation (CEC 2009)},
key = {IEEE CEC},
author = { Smit, Selmar K. and Agoston E. Eiben },
title = {Comparing Parameter Tuning Methods for Evolutionary
Algorithms},
pages = {399--406}
}
@inproceedings{SmiEib2010cec,
year = 2010,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2010 Congress on Evolutionary Computation (CEC 2010)},
editor = { Ishibuchi, Hisao and others},
key = {IEEE CEC},
author = { Smit, Selmar K. and Agoston E. Eiben },
title = {Beating the 'world champion' evolutionary algorithm
via {REVAC} tuning},
pages = {1--8},
doi = {10.1109/CEC.2010.5586026}
}
@incollection{SmiEib2010evoapp,
year = 2010,
volume = 6024,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
booktitle = {Applications of Evolutionary Computation},
editor = {Cecilia Di Chio and Stefano Cagnoni and Carlos Cotta and Marc
Ebner and Anik{\'o} Ek{\'a}rt and Anna I. Esparcia{-}Alc{\'{a}}zar and Chi Keong Goh and Juan-Juli{\'a}n Merelo and Ferrante Neri and Mike Preuss and Julian Togelius and Georgios N. Yannakakis},
author = { Smit, Selmar K. and Agoston E. Eiben },
title = {Parameter Tuning of Evolutionary Algorithms: Generalist
vs. Specialist},
pages = {542--551},
doi = {10.1007/978-3-642-12239-2_56}
}
@incollection{SmiEib2011ae,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 7401,
booktitle = {Artificial Evolution: 10th International Conference, Evolution Artificielle, EA, 2011},
publisher = {Springer},
year = 2012,
editor = { Jin-Kao Hao and Legrand, Pierrick and Collet, Pierre and
Monmarch{\'e}, Nicolas and Lutton, Evelyne and Schoenauer,
Marc},
author = { Smit, Selmar K. and Agoston E. Eiben },
title = {Multi-Problem Parameter Tuning using {BONESA}},
pages = {222--233},
annote = {For some reason, this was not actually published in the LNCS
Proceedings of EA}
}
@inproceedings{SmiEibSzl2010ijcci,
year = 2010,
publisher = {SciTePress},
booktitle = {Proceedings of the International Joint Conference on
Computational Intelligence (IJCCI-2010)},
editor = { Filipe, J. and J. Kacprzyk },
author = { Smit, Selmar K. and Agoston E. Eiben and Szl\'{a}vik, Z. },
title = {An {MOEA}-based Method to Tune {EA} Parameters on
Multiple Objective Functions},
pages = {261--268}
}
@inproceedings{SmiSet92,
author = {Tobiah E. Smith and Dorothy E. Setliff},
booktitle = {Proceedings of the Seventh Knowledge-Based Software Engineering Conference},
title = {Knowledge-based constraint-driven software synthesis},
year = 1992,
pages = {18--27},
publisher = {IEEE},
doi = {10.1109/KBSE.1992.252912}
}
@incollection{SmiStoSer2016explo,
address = { New York, NY},
publisher = {ACM Press},
year = 2016,
editor = { Tobias Friedrich and Frank Neumann and Andrew M. Sutton },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2016},
doi = {10.1145/2908812.2908854},
author = {Jim Smith and Christopher Stone and Martin Serpell},
title = {Exploiting Diverse Distance Metrics for Surrogate-Based
Optimisation of Ordering Problems},
pages = {701--708}
}
@incollection{SmiVanLim2010,
doi = {10.1007/978-3-642-13800-3},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 6073,
booktitle = {Learning and Intelligent Optimization, 4th International Conference, LION 4},
publisher = {Springer},
year = 2010,
editor = { Christian Blum and Roberto Battiti },
author = { Kate Smith{-}Miles and van Hemert, Jano I. and Lim, Xin Yu},
title = {Understanding {TSP} difficulty by Learning from evolved instances},
pages = {266--280}
}
@inproceedings{Smith-Miles2008ijcnn,
publisher = {IEEE Press},
year = 2008,
editor = {Liu, Derong and others},
booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN 2008),
Hong Kong, China, June 1-6, 2008},
key = {IJCNN},
author = { Kate Smith{-}Miles },
title = {Towards insightful algorithm selection for optimisation using meta-learning concepts},
pages = {4118--4124}
}
@book{SneCoc1967stat,
title = {Statistical Methods},
author = {Snedecor, George W. and Cochran, William G.},
edition = {6th},
year = 1967,
publisher = {Iowa State University Press},
address = {Ames, IA, USA}
}
@incollection{SnoLarAda2012nips,
publisher = {Curran Associates, Red Hook, NY},
year = 2012,
editor = {Peter L. Bartlett and Fernando C. N. Pereira and Christopher
J. C. Burges and L{\'{e}}on Bottou and Kilian Q. Weinberger},
booktitle = {Advances in Neural Information Processing Systems (NIPS 25)},
author = { Jasper Snoek and Hugo Larochelle and Ryan P. Adams },
title = {Practical {Bayesian} Optimization of Machine Learning Algorithms},
pages = {2960--2968}
}
@inproceedings{SnoSweZemAda2014icml,
url = {http://jmlr.org/proceedings/papers/v32/},
publisher = {{PMLR}},
year = 2014,
volume = 32,
booktitle = {Proceedings of the 31st International Conference on Machine Learning, {ICML} 2014},
editor = {Xing, Eric P. and Jebara, Tony},
author = { Jasper Snoek and Kevin Swersky and Richard Zemel and Ryan P. Adams },
title = {Input Warping for {Bayesian} Optimization of Non-Stationary
Functions},
pages = {1674--1682},
abstract = {Bayesian optimization has proven to be a highly effective
methodology for the global optimization of unknown, expensive
and multimodal functions. The ability to accurately model
distributions over functions is critical to the effectiveness
of Bayesian optimization. Although Gaussian processes
provide a flexible prior over functions, there are various
classes of functions that remain difficult to model. One of
the most frequently occurring of these is the class of
non-stationary functions. The optimization of the
hyperparameters of machine learning algorithms is a problem
domain in which parameters are often manually transformed a
priori, for example by optimizing in "log-space", to mitigate
the effects of spatially-varying length scale. We develop a
methodology for automatically learning a wide family of
bijective transformations or warpings of the input space
using the Beta cumulative distribution function. We further
extend the warping framework to multi-task Bayesian
optimization so that multiple tasks can be warped into a
jointly stationary space. On a set of challenging benchmark
optimization tasks, we observe that the inclusion of warping
greatly improves on the state-of-the-art, producing better
results faster and more reliably.}
}
@incollection{SocKnoSam02:ants,
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and Gianni A. {Di Caro} and M. Sampels },
volume = 2463,
series = {Lecture Notes in Computer Science},
year = 2002,
booktitle = {Ant Algorithms, Third International Workshop, ANTS
2002},
author = { Krzysztof Socha and Joshua D. Knowles and M. Sampels },
title = {A {\MaxMinAntSystem} for the University Course Timetabling Problem},
pages = {1--13}
}
@incollection{SocSamMan03:evoworkshops,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 2611,
editor = {S. Cagnoni and others},
aeditor = {S. Cagnoni and J. J. {Romero Cardalda} and D. W. Corne
and J. Gottlieb and A. Guillot and E. Hart and
C. G. Johnson and E. Marchiori and J.-A. Meyer and Martin Middendorf and G{\"u}nther R. Raidl },
year = 2003,
booktitle = {Applications of Evolutionary Computing,
Proceedings of EvoWorkshops 2003},
author = { Krzysztof Socha and M. Sampels and M. Manfrin},
title = {Ant algorithms for the university course timetabling
problem with regard to the state-of-the-art},
pages = {334--345}
}
@incollection{Socha04:ants,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 3172,
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and L. M. Gambardella and Francesco Mondada and Thomas St{\"u}tzle and Mauro Birattari and Christian Blum },
year = 2004,
booktitle = {Ant Colony Optimization and Swarm Intelligence, 4th
International Workshop, ANTS 2004 },
author = { Krzysztof Socha },
title = {{ACO} for Continuous and Mixed-Variable Optimization},
pages = {25--36}
}
@book{Solnon2010,
author = { Christine Solnon },
title = {Ant Colony Optimization and Constraint Programming},
publisher = {Wiley},
year = 2010,
doi = {10.1002/9781118557563}
}
@incollection{SorSevGlo2017,
isbn = {978-3-319-07125-1},
publisher = {Springer International Publishing},
year = 2018,
booktitle = {Handbook of Heuristics},
editor = { Rafael Mart{\'i} and Panos M. Pardalos and Mauricio G. C. Resende },
title = {A history of metaheuristics},
author = { Kenneth S{\"o}rensen and Marc Sevaux and Fred Glover },
pages = {1--27}
}
@inproceedings{SotBasDol20016_ES,
year = 2001,
author = { Aldo Sotelo and Julio Basulado and Pedro Dold{\'a}n and Benjam{\'i}n Bar{\'a}n },
title = {Algoritmos Evolutivos Multiobjetivo Combinados para la
Optimizaci{\'o}n de la Programaci{\'o}n de Bombeo en Sistemas
de Suministro de Agua},
booktitle = {Congreso Internacional de Tecnolog{\'i}as y Aplicaciones
Inform\'aticas, JIT-CITA 2001, Asunci\'on, Paraguay},
note = {(In Spanish)}
}
@inproceedings{Sotelo02,
author = { Aldo Sotelo and C. von L{\"u}cken and Benjam{\'i}n Bar{\'a}n },
title = {Multiobjective Evolutionary Algorithms in Pump
Scheduling Optimisation},
booktitle = {Proceedings of the Third International Conference on
Engineering Computational Technology},
publisher = {Civil-Comp Press, Stirling, Scotland},
year = 2002,
editor = { Barry H. V. Topping and Zden{\'e}ek Bittnar },
abstract = {Operation of pumping stations represents high costs
to water supply companies. Therefore, reducing such
costs through an optimal pump scheduling becomes an
important issue. This work presents the use of
Multiobjective Evolutionary Algorithms (MOEAs) to
solve an optimal pump-scheduling problem. For the
first time, six different approaches were
implemented and compared. These algorithms aim to
minimise four objectives: electric energy cost,
pumps' maintenance cost, maximum power peak, and
level variation in the reservoir. In order to
consider hydraulic and technical constrains, a
heuristic constrain algorithm was developed and
combined with each MOEA utilised. Evaluation of
experimental results of a set of metrics shows that
the Strength {Pareto} Evolutionary Algorithm (SPEA)
achieves the best performance for this
problem. Moreover, SPEA's set of solutions provide
pumping station operation engineers with a wide
range of optimal pump schedules to chose from.}
}
@misc{SouRitLop2020capopt,
author = { Marcelo {De Souza} and Marcus Ritt and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {{CAPOPT}: Capping Methods for the Automatic Configuration of Optimization Algorithms},
howpublished = {\url{https://github.com/souzamarcelo/capopt}},
year = 2020
}
@misc{SouRitLop2021cap-supp,
author = { Marcelo {De Souza} and Marcus Ritt and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Capping Methods for the Automatic Configuration of
Optimization Algorithms -- Supplementary Material},
howpublished = {\url{https://github.com/souzamarcelo/supp-cor-capopt}},
year = 2021
}
@misc{Spark,
author = {{Apache Software Foundation}},
title = {Spark},
url = {https://spark.apache.org},
year = 2012
}
@book{SraNowWri2012,
title = {Optimization for machine learning},
author = {Sra, Suvrit and Nowozin, Sebastian and Wright, Stephen J.},
year = 2012,
publisher = {MIT Press},
address = {Cambridge, MA}
}
@incollection{Stadler1995landscapes,
author = {P. F. Stadler},
title = {Toward a theory of landscapes},
booktitle = {Complex Systems and Binary Networks},
year = 1995,
editor = {R. L{\'o}pez-Pe{\~n}a and R. Capovilla and
R. Garc{\'i}a-Pelayo and H. Waelbroeck and F. Zertruche},
pages = {77--163},
publisher = {Springer}
}
@book{Starr1963,
title = {Product design and decision theory},
author = {Starr, Martin Kenneth},
year = 1963,
series = {Prentice-Hall Series in Engineering Design, Fundamentals of
Engineering Design},
publisher = {Prentice-Hall},
address = {Englewood, Cliffs, NJ}
}
@incollection{SteAggBurGonRes2015gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2015,
editor = {Sara Silva and Anna I. Esparcia{-}Alc{\'{a}}zar },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015},
author = {Fernando Stefanello and Vaneet Aggarwal and Luciana Salete Buriol and Jos{\'e} Fernando Gon\c{c}alves and Mauricio G. C. Resende },
title = {A Biased Random-key Genetic Algorithm for Placement of Virtual Machines Across Geo-Separated Data Centers},
pages = {919--926},
doi = {10.1145/2739480.2754768},
keywords = {irace}
}
@incollection{SteGar1991computational,
author = { R. E. Steuer and Gardiner, Lorraine},
editor = {Fandel, G{\"u}nter and Gehring, Hermann},
title = {On the Computational Testing of Procedures for Interactive
Multiple Objective Linear Programming},
booktitle = {Operations Research},
year = 1991,
publisher = {Springer},
address = {Berlin\slash Heidelberg},
pages = {121--131},
isbn = {978-3-642-76537-7},
doi = {10.1007/978-3-642-76537-7_8},
annote = {Proposed difference between ad hoc and non-ad hoc interactive
multi-objective optimization methods}
}
@incollection{SteSmiJan2019autostat,
epub = {http://automl.org/book},
booktitle = {Automated Machine Learning},
publisher = {Springer},
year = 2019,
editor = { Frank Hutter and Kotthoff, Lars and Joaquin Vanschoren },
title = {The Automatic Statistician},
author = {Christian Steinruecken and Emma Smith and David Janz and
James Lloyd and Zoubin Ghahramani},
doi = {10.1007/978-3-030-05318-5_9},
pages = {161--173}
}
@book{Steuer1986,
author = { R. E. Steuer },
title = {Multiple Criteria Optimization: Theory, Computation and
Application},
publisher = {John Wiley \& Sons},
year = 1986,
series = {Wiley Series in Probability and Mathematical Statistics},
address = { New York, NY},
keywords = {Maximally dispersed weights}
}
@incollection{Stolfi2015,
volume = 9422,
series = {Lecture Notes in Computer Science},
editor = {Puerta, Jos{\'e} M. and G{\'a}mez, Jos{\'e} A. and
Dorronsoro, Bernabe and Barrenechea, Edurne and Troncoso,
Alicia and Baruque, Bruno and Galar, Mikel},
address = { Heidelberg, Germany},
publisher = {Springer},
year = 2015,
booktitle = {Advances in Artificial Intelligence, CAEPIA 2015},
abstract = {In this article we present a strategy based on an evolution-
ary algorithm to calculate the real vehicle flows in cities
according to data from sensors placed in the streets. We have
worked with a map imported from OpenStreetMap into the SUMO
traffic simulator so that the resulting scenarios can be used
to perform different optimizations with the confidence of
being able to work with a traffic distribution close to
reality. We have compared the results of our algorithm to
other competitors and achieved results that replicate the
real traffic distribution with a precision higher than
{90\%}.},
author = {Stolfi, Daniel H. and Alba, Enrique },
title = {An Evolutionary Algorithm to Generate Real Urban Traffic
Flows},
pages = {332--343},
doi = {10.1007/978-3-319-24598-0_30},
keywords = {Evolutionary algorithm,SUMO,Smart city,Smart mobility,Traffic
simulation}
}
@techreport{Stu1998:pfsp,
author = { Thomas St{\"u}tzle },
title = {Applying Iterated Local Search to the Permutation Flow Shop Problem},
institution = {FG Intellektik, FB Informatik, TU Darmstadt, Germany},
year = 1998,
number = {AIDA--98--04},
month = aug
}
@misc{Stu2002,
author = { Thomas St{\"u}tzle },
title = {{\softwarepackage{ACOTSP}}: A Software Package of Various Ant Colony
Optimization Algorithms Applied to the Symmetric
Traveling Salesman Problem},
url = {http://www.aco-metaheuristic.org/aco-code},
annote = {\url{http://www.aco-metaheuristic.org/aco-code}},
year = 2002
}
@inproceedings{Stu2009:eume,
editor = {Ana Viana and others},
year = 2009,
booktitle = {Proceedings of the EU/MEeting 2009: Debating the future: new
areas of application and innovative approaches},
author = { Thomas St{\"u}tzle },
title = {Some Thoughts on Engineering Stochastic Local Search
Algorithms},
pages = {47--52}
}
@techreport{Stu97:qap,
author = { Thomas St{\"u}tzle },
title = {{\MaxMinAntSystem} for the Quadratic Assignment
Problem},
institution = {FG Intellektik, FB Informatik, TU Darmstadt, Germany},
year = 1997,
number = {AIDA--97--4},
month = jul
}
@inproceedings{Stu98:eufit,
author = { Thomas St{\"u}tzle },
title = {An Ant Approach to the Flow Shop Problem},
booktitle = {Proceedings of the 6th European Congress on
Intelligent Techniques {\&} Soft Computing
(EUFIT'98)},
pages = {1560--1564},
year = 1998,
volume = 3,
publisher = {Verlag Mainz, Aachen, Germany}
}
@incollection{StuDor99:nio,
address = {London, UK},
year = 1999,
publisher = {McGraw Hill},
editor = { David Corne and Marco Dorigo and Fred Glover },
booktitle = {New Ideas in Optimization},
author = { Thomas St{\"u}tzle and Marco Dorigo },
title = {{ACO} Algorithms for the Quadratic Assignment Problem},
pages = {33--50},
anote = {Also available as Technical Report IRIDIA/99-2,
Universit{\'e} Libre de Bruxelles, Belgium}
}
@incollection{StuFer2004qap,
booktitle = {Proceedings of EvoCOP 2004 -- 4th European Conference on Evolutionary Computation in Combinatorial Optimization },
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 3004,
year = 2004,
publisher = {Springer},
editor = {Gottlieb, Jens and G{\"u}nther R. Raidl },
author = { Thomas St{\"u}tzle and Fernandes, Susana},
title = {New Benchmark Instances for the QAP and the Experimental
Analysis of Algorithms},
pages = {199--209},
doi = {10.1007/978-3-540-24652-7_20},
abstract = {The quadratic assignment problem arises in a variety of
practical settings. It is known to be among the hardest
combinatorial problems for exact algorithms. Therefore, a
large number of heuristic approaches have been proposed for
its solution. In this article we introduce a new, large set
of QAP instances that is intended to allow the systematic
study of the performance of metaheuristics in dependence of
QAP instance characteristics. Additionally, we give
computational results with several high performing algorithms
known from literature and give exemplary results on the
influence of instance characteristics on the performance of
these algorithms.}
}
@incollection{StuHoo01:mic,
author = { Thomas St{\"u}tzle and Holger H. Hoos },
title = {Analysing the Run-time Behaviour of Iterated Local
Search for the Travelling Salesman Problem},
booktitle = {Essays and Surveys on Metaheuristics},
pages = {589--611},
publisher = {Kluwer Academic Publishers, Boston, MA},
year = 2001,
editor = {P. Hansen and C. Ribeiro},
series = {Operations Research/Computer Science Interfaces
Series}
}
@techreport{StuHoo1996:aida,
author = { Thomas St{\"u}tzle and Holger H. Hoos },
title = {Improving the {Ant} {System}: A Detailed Report on
the {\MaxMinAntSystem}},
institution = {FG Intellektik, FB Informatik, TU Darmstadt, Germany},
year = 1996,
number = {AIDA--96--12},
month = aug
}
@incollection{StuHoo97:icec,
address = {Piscataway, NJ},
publisher = {IEEE Press},
year = 1997,
editor = { Thomas B{\"a}ck and Zbigniew Michalewicz and Xin Yao },
booktitle = {Proceedings of the 1997 IEEE International
Conference on Evolutionary Computation (ICEC'97)},
author = { Thomas St{\"u}tzle and Holger H. Hoos },
title = {The {\MaxMinAntSystem} and Local Search for the
Traveling Salesman Problem},
pages = {309--314}
}
@incollection{StuHoo99:mic,
author = { Thomas St{\"u}tzle and Holger H. Hoos },
title = {{\MaxMinAntSystem} and Local Search for Combinatorial
Optimization Problems},
booktitle = {Meta-Heuristics: Advances and Trends in Local Search
Paradigms for Optimization},
publisher = {Kluwer Academic Publishers, Dordrecht, The Netherlands},
year = 1999,
editor = { Stefan Vo{\ss} and Silvano Martello and Ibrahim H. Osman and C. Roucairol},
pages = {137--154}
}
@incollection{StuLop2015gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2015,
editor = { Jim{\'e}nez Laredo, Juan Luis and Sara Silva and Anna I. Esparcia{-}Alc{\'{a}}zar },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2015},
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Automatic (Offline) Configuration of Algorithms},
pages = {681--702},
doi = {10.1145/2739482.2756581}
}
@incollection{StuLop2017gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2017,
editor = { Peter A. N. Bosman },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2017},
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Automated Offline Design of Algorithms},
pages = {1038--1065},
doi = {10.1145/3067695.3067722}
}
@incollection{StuLop2019hb,
publisher = {Springer},
series = {International Series in Operations Research \& Management
Science},
volume = 272,
booktitle = {Handbook of Metaheuristics},
year = 2019,
editor = { Michel Gendreau and Jean-Yves Potvin },
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Automated Design of Metaheuristic Algorithms},
pages = {541--579},
doi = {10.1007/978-3-319-91086-4_17},
keywords = {automatic design, automatic configuration}
}
@incollection{StuLopDor2011eorms,
year = 2011,
publisher = {John Wiley \& Sons},
editor = {J. J. Cochran},
booktitle = {Wiley Encyclopedia of Operations Research and
Management Science},
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Marco Dorigo },
title = {A Concise Overview of Applications of Ant Colony
Optimization},
pages = {896--911},
volume = 2,
doi = {10.1002/9780470400531.eorms0001}
}
@incollection{StuLopPel2011autsea,
year = 2012,
address = { Berlin, Germany},
publisher = {Springer},
booktitle = {Autonomous Search},
editor = { Youssef Hamadi and E. Monfroy and F. Saubion},
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Paola Pellegrini and Michael Maur and Marco A. {Montes de Oca} and Mauro Birattari and Marco Dorigo },
title = {Parameter Adaptation in Ant Colony Optimization},
doi = {10.1007/978-3-642-21434-9_8},
pages = {191--215}
}
@incollection{StuRui2017ig,
isbn = {978-3-319-07125-1},
publisher = {Springer International Publishing},
year = 2018,
booktitle = {Handbook of Heuristics},
editor = { Rafael Mart{\'i} and Panos M. Pardalos and Mauricio G. C. Resende },
author = { Thomas St{\"u}tzle and Rub{\'e}n Ruiz },
title = {Iterated Greedy},
doi = {10.1007/978-3-319-07153-4_10-1},
pages = {1--31}
}
@incollection{StuRui2017ils,
isbn = {978-3-319-07125-1},
publisher = {Springer International Publishing},
year = 2018,
booktitle = {Handbook of Heuristics},
editor = { Rafael Mart{\'i} and Panos M. Pardalos and Mauricio G. C. Resende },
author = { Thomas St{\"u}tzle and Rub{\'e}n Ruiz },
title = {Iterated Local Search},
doi = {10.1007/978-3-319-07153-4_8-1},
pages = {1--27}
}
@phdthesis{StuetzlePhD,
author = { Thomas St{\"u}tzle },
title = {Local Search Algorithms for Combinatorial Problems --- Analysis, Improvements, and New Applications},
school = {FB Informatik, TU Darmstadt, Germany},
year = 1998
}
@incollection{StyHoo2013gecco,
isbn = {978-1-4503-1963-8},
address = { New York, NY},
publisher = {ACM Press},
year = 2013,
editor = { Christian Blum and Alba, Enrique },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2013},
author = {James Styles and Holger H. Hoos },
title = {Ordered racing protocols for automatically configuring algorithms
for scaling performance},
pages = {551--558},
doi = {10.1145/2463372.2463438}
}
@incollection{StyHooMul2012:lion,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 7219,
booktitle = {Learning and Intelligent Optimization, 6th International Conference, LION 6},
publisher = {Springer},
year = 2012,
editor = { Youssef Hamadi and Marc Schoenauer },
author = {James Styles and Holger H. Hoos and Martin M{\"{u}}ller},
title = {Automatically Configuring Algorithms for Scaling Performance},
pages = {205--219}
}
@techreport{SugHanLia2005cec,
author = { Ponnuthurai N. Suganthan and Nikolaus Hansen and J. J. Liang and Kalyanmoy Deb and Y. P. Chen and Anne Auger and S. Tiwari},
title = {Problem definitions and evaluation criteria for the
{CEC 2005} special session on real-parameter
optimization},
institution = {Nanyang Technological University, Singapore},
year = 2005,
keywords = {CEC'05 benchmark},
annote = {Also known as KanGAL Report Number 2005005 (Kanpur Genetic Algorithms
Laboratory, IIT Kanpur)}
}
@inproceedings{SuiGotBur2015icml,
publisher = {{PMLR}},
year = 2015,
volume = 37,
booktitle = {Proceedings of the 32nd International Conference on Machine Learning, {ICML} 2015},
editor = {Francis Bach and David Blei},
author = {Sui, Yanan and Alkis Gotovos and Burdick, Joel W. and Andreas
Krause},
title = {Safe Exploration for Optimization with {Gaussian} Processes},
pages = {997--1005},
epub = {http://proceedings.mlr.press/v37/sui15.html},
abstract = {We consider sequential decision problems under uncertainty,
where we seek to optimize an unknown function from noisy
samples. This requires balancing exploration (learning about
the objective) and exploitation (localizing the maximum), a
problem well-studied in the multi-armed bandit literature. In
many applications, however, we require that the sampled
function values exceed some prespecified "safety" threshold,
a requirement that existing algorithms fail to meet. Examples
include medical applications where patient comfort must be
guaranteed, recommender systems aiming to avoid user
dissatisfaction, and robotic control, where one seeks to
avoid controls causing physical harm to the platform. We
tackle this novel, yet rich, set of problems under the
assumption that the unknown function satisfies regularity
conditions expressed via a Gaussian process prior. We develop
an efficient algorithm called SafeOpt, and theoretically
guarantee its convergence to a natural notion of optimum
reachable under safety constraints. We evaluate SafeOpt on
synthetic data, as well as two real applications: movie
recommendation, and therapeutic spinal cord stimulation.},
keywords = {Safe Optimization, SafeOpt}
}
@inproceedings{SuiZhuBur2018stageopt,
year = 2018,
publisher = {{PMLR}},
volume = 80,
series = {Proceedings of Machine Learning Research},
booktitle = {Proceedings of the 35th International Conference on Machine Learning, {ICML} 2018},
editor = {Jennifer G. Dy and Andreas Krause},
author = {Sui, Yanan and Zhuang, Vincent and Burdick, Joel W. and Yue,
Yisong},
title = {Stagewise Safe {Bayesian} Optimization with {Gaussian}
Processes},
pages = {4788--4796},
epub = {http://proceedings.mlr.press/v80/sui18a.html},
keywords = {StageOpt}
}
@inproceedings{SunHan2010:ccie,
address = {Los Alamitos, CA},
publisher = {IEEE Computer Society Press},
year = 2010,
booktitle = {Proceedings of the 2010 International Conference on
Computing, Control and Industrial Engineering},
key = {CCIE},
author = { Zhaoxu Sun and Min Han },
title = {Multi-criteria Decision Making Based on {PROMETHEE} Method},
pages = {416--418}
}
@book{SutBar1998reinf,
author = {Richard S. Sutton and Andrew G. Barto},
title = {Reinforcement Learning: An Introduction},
publisher = {MIT Press, Cambridge, MA},
year = 1998
}
@book{SutBar2018reinf,
author = {Richard S. Sutton and Andrew G. Barto},
title = {Reinforcement Learning: An Introduction},
publisher = {MIT Press, Cambridge, MA},
edition = {2nd},
year = 2018
}
@mastersthesis{Sutton98,
author = { D. C. Sutton and D. S. Keane and S. J. Sherriff },
title = {Optimizing the Real Time Operation of a Pumping
Station at a Water Filtration Plant using Genetic
Algorithms},
school = {Department of Civil and Environmental Engineering,
The University of Adelaide},
year = 1998,
type = {Honors Thesis}
}
@incollection{SwaOzcKen2011lion,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 6683,
booktitle = {Learning and Intelligent Optimization, 5th International Conference, LION 5},
publisher = {Springer},
year = 2011,
editor = { Carlos A. {Coello Coello} },
author = { Jerry Swan and Ender {\"O}zcan and Graham Kendall },
title = {Hyperion: A Recursive Hyper-heuristic Framework},
pages = {616--630}
}
@inproceedings{Swan2015,
editor = { Talbi, El-Ghazali },
booktitle = {Proceedings of MIC 2015, the 11th Metaheuristics International
Conference},
year = 2015,
author = { Jerry Swan and others},
anauthor = { Jerry Swan and Steven Adriaensen and Mohamed Bishr and Edmund K. Burke and John A. Clark and Patrick {De Causmaecker} and Durillo, Juan J. and Kevin Hammond and Emma Hart and Colin
G. Johnson and Zoltan A. Kocsis and Ben Kovitz and Krzysztof Krawiec and Simon Martin and J. J. Merelo and Leandro
L. Minku and Ender {\"O}zcan and Gisele Pappa and Erwin
Pesch and Pablo Garc{\'i}a-S{\'a}nchez and Andrea Schaerf and Kevin
Sim and Jim E. Smith and Thomas St{\"u}tzle and Stefan Vo{\ss} and Stefan Wagner and Xin Yao },
title = {A Research Agenda for Metaheuristic Standardization}
}
@inproceedings{Syswerda89,
publisher = {Morgan Kaufmann Publishers, San Mateo, CA},
editor = { J. David Schaffer },
year = 1989,
booktitle = {Proceedings of the Third International Conference on Genetic Algorithms (ICGA'89)},
author = { Gilbert Syswerda },
title = {Uniform Crossover in Genetic Algorithms},
pages = {2--9},
keywords = {uniform crossover}
}
@inproceedings{TaeLeoClam2007spacecraft,
author = {Taeyoung Lee and Leok, Melvin and McClamroch, N. Harris},
title = {A combinatorial optimal control problem for spacecraft
formation reconfiguration},
booktitle = {2007 46th IEEE Conference on Decision and Control},
year = 2007,
publisher = {IEEE},
doi = {10.1109/cdc.2007.4434143},
keywords = {bilevel}
}
@incollection{TagShiNak2011ibde,
address = { New York, NY},
publisher = {ACM Press},
year = 2011,
editor = {Natalio Krasnogor and Pier Luca Lanzi},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011},
author = {Tagawa, Kiyoharu and Shimizu, Hidehito and Nakamura,
Hiroyuki},
title = {Indicator-based Differential Evolution Using Exclusive
Hypervolume Approximation and Parallelization for Multi-core
Processors},
pages = {657--664}
}
@inproceedings{TaiYanRanWol2014deepface,
title = {Deepface: Closing the gap to human-level performance in face
verification},
author = {Taigman, Yaniv and Yang, Ming and Ranzato, Marc'Aurelio and
Wolf, Lior},
booktitle = {Proceedings of the IEEE conference on computer vision and
pattern recognition},
pages = {1701--1708},
year = 2014
}
@incollection{TanOya2017gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2017,
editor = { Peter A. N. Bosman },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017},
title = {Benchmarking {MOEAs} for multi-and many-objective
optimization using an unbounded external archive},
author = {Tanabe, Ryoji and Oyama, Akira},
pages = {633--640}
}
@inproceedings{TasBuyPanSug2013,
publisher = {Springer International Publishing},
series = {Theoretical Computer Science and General Issues},
volume = 8298,
year = 2013,
editor = {B. K. Panigrahi and P. N. Suganthan and S. Das and S. S. Dash},
booktitle = {Swarm, Evolutionary, and Memetic Computing},
author = {M. Fatih Tasgetiren and Ozge Buyukdagli and Quan-Ke Pan and Ponnuthurai N. Suganthan },
title = {A general variable neighborhood search algorithm for the
no-idle permutation flowshop scheduling problem},
pages = {24--34}
}
@incollection{TavPer2012eurogp,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 7244,
series = {Lecture Notes in Computer Science},
editor = { A. Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta },
year = 2012,
booktitle = {Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012},
author = { Jorge Tavares and Francisco B. Pereira },
title = {Automatic Design of Ant Algorithms with Grammatical
Evolution },
pages = {206--217}
}
@inproceedings{TeiCovStuGas2009:eume,
editor = {Ana Viana and others},
year = 2009,
booktitle = {Proceedings of the EU/MEeting 2009: Debating the future: new
areas of application and innovative approaches},
author = {Cristina Teixeira and Jos\'e Covas and Thomas St{\"u}tzle and Ant{\'o}nio Gaspar{-}Cunha },
title = {Application of {Pareto} Local Search and Multi-Objective Ant
Colony Algorithms to the Optimization of Co-Rotating Twin
Screw Extruders},
pages = {115--120}
}
@misc{TensorFlow,
author = {Google},
title = {TensorFlow},
year = 2017,
howpublished = {\url{https://www.tensorflow.org}}
}
@inproceedings{Teo2010,
author = {Teo, K. T. K. and Kow, W. Y. and Chin, Y. K.},
booktitle = {Proceedings - 2nd International Conference on Computational
Intelligence, Modelling and Simulation, CIMSim 2010},
keywords = {Genetic algorithm,T-junction,Traffic control system,Traffic
flows},
pages = {172--177},
title = {Optimization of traffic flow within an urban traffic light
intersection with genetic algorithm},
year = 2010,
organization = {IEEE},
publisher = {IEEE Press}
}
@incollection{TerRosVal99gecco,
publisher = {Morgan Kaufmann Publishers, San Francisco, CA},
year = 1999,
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 1999},
shorteditor = {Wolfgang Banzhaf and others},
editor = {Wolfgang Banzhaf and Jason M. Daida and A. E. Eiben
and Max H. Garzon and Vasant Honavar and Mark
J. Jakiela and Robert E. Smith},
author = {Hugo Terashima-Mar\'{i}n and Peter Ross and Manuel
Valenzuela-Rend\'{o}n},
title = {Evolution of Constraint Satisfaction Strategies in
Examination Timetabling},
pages = {635--642}
}
@incollection{Thie2007adaptive,
address = { Berlin, Germany},
publisher = {Springer},
year = 2007,
booktitle = {Parameter Setting in Evolutionary Algorithms},
editor = {F. Lobo and C. F. Lima and Zbigniew Michalewicz },
title = {Adaptive strategies for operator allocation},
author = { Dirk Thierens },
pages = {77--90}
}
@incollection{Thie2009adaptive,
volume = 5752,
series = {Lecture Notes in Computer Science},
year = 2009,
address = { Heidelberg, Germany},
publisher = {Springer},
booktitle = {Engineering Stochastic Local Search
Algorithms. Designing, Implementing and Analyzing
Effective Heuristics. SLS~2009},
editor = { Thomas St{\"u}tzle and Mauro Birattari and Holger H. Hoos },
title = {Adaptive operator selection for iterated local search},
author = { Dirk Thierens },
pages = {140--144}
}
@incollection{Thierens2004:gecco,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 3103,
editor = { Kalyanmoy Deb and others},
year = 2004,
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2004, Part II},
author = { Dirk Thierens },
title = {Population-based Iterated Local Search: Restricting the Neighborhood
Search by Crossover},
pages = {234--245}
}
@incollection{Thierens2005:gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2005,
editor = { Hans-Georg Beyer and Una-May O'Reilly },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2005},
author = { Dirk Thierens },
title = {An Adaptive Pursuit Strategy for Allocating Operator Probabilities},
pages = {1539--1546}
}
@incollection{ThoHutHooLey2013:kdd,
editor = {Inderjit S. Dhillon and Yehuda Koren and Rayid Ghani and Ted
E. Senator and Paul Bradley and Rajesh Parekh and Jingrui He
and Robert L. Grossman and Ramasamy Uthurusamy},
year = 2013,
address = { New York, NY},
publisher = {ACM Press},
booktitle = {The 19th {ACM} {SIGKDD} International Conference on Knowledge
Discovery and Data Mining, {KDD} 2013},
author = {Chris Thornton and Frank Hutter and Holger H. Hoos and Kevin Leyton-Brown },
title = {{Auto-WEKA}: Combined Selection and Hyperparameter
Optimization of Classification Algorithms},
pages = {847--855}
}
@book{ThrunPratt1998,
title = {Learning to learn},
author = {Thrun, Sebastian and Pratt, Lorien},
year = 1998,
publisher = {Springer}
}
@incollection{TinWhiOch2014:gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2014,
editor = {Christian Igel and Dirk V. Arnold},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2014},
author = {Renato Tin\'os and Darrell Whitley and Gabriela Ochoa },
title = {Generalized Asymmetric Partition Crossover ({GAPX}) for the Asymmetric {TSP}},
pages = {501--508}
}
@incollection{TomKad2019iemoi,
isbn = {978-1-4503-6111-8},
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
author = { Tomczyk, Micha{\l} K and Kadzi{\'n}ski, Mi{\l}osz },
title = {Robust Indicator-Based Algorithm for Interactive Evolutionary
Multiple Objective Optimization},
doi = {10.1145/3321707.3321742},
abstract = {We propose a novel robust indicator-based algorithm, called
IEMO/I, for interactive evolutionary multiple objective
optimization. During the optimization run, IEMO/I selects at
regular intervals a pair of solutions from the current
population to be compared by the Decision Maker. The
successively provided holistic judgements are employed to
divide the population into fronts of potential
optimality. These fronts are, in turn, used to bias the
evolutionary search toward a subset of Pareto-optimal
solutions being most relevant to the Decision Maker. To
ensure a fine approximation of such a subset, IEMO/I employs
a hypervolume metric within a steady-state indicator-based
evolutionary framework. The extensive experimental evaluation
involving a number of benchmark problems confirms that IEMO/I
is able to construct solutions being highly preferred by the
Decision Maker after a reasonable number of interactions. We
also compare IEMO/I with some selected state-of-the-art
interactive evolutionary hybrids incorporating preference
information in form of pairwise comparisons, proving its
competitiveness.},
pages = {629--637},
numpages = 9,
keywords = {preference learning, indicator-based algorithms, interactive
algorithms, multiple objective optimization, pairwise
comparisons, evolutionary algorithms}
}
@book{TotVig2002vrp,
title = {The vehicle routing problem},
author = { Paolo Toth and Vigo, Daniele },
year = 2002,
publisher = {Society for Industrial and Applied Mathematics, Philadelphia, PA, USA}
}
@incollection{ToyShoMorMiy2012,
author = {Toyama, F. and Shoji, K. and Mori, H. and Miyamichi, J.},
title = {An Iterated Greedy Algorithm for the Binary Quadratic
Programming Problem},
booktitle = {Joint 6th International Conference on Soft Computing and
Intelligent Systems (SCIS) and 13th International Symposium
on Advanced Intelligent Systems (ISIS), 2012},
publisher = {IEEE Press},
year = 2012,
pages = {2183--2188}
}
@incollection{TraNikCen2022ngopt,
address = { Cham, Switzerland},
series = {Lecture Notes in Computer Science},
volume = 13398,
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XVII}},
publisher = {Springer},
year = 2022,
editor = { G{\"u}nther Rudolph and Anna V. Kononova and Aguirre, Hern\'{a}n E. and Pascal Kerschke and Gabriela Ochoa and Tea Tu{\v s}ar },
author = {Trajanov, Risto and Nikolikj, Ana and Cenikj, Gjorgjina and
Teytaud, Fabien and Videau, Mathurin and Olivier Teytaud and Tome Eftimov and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Carola Doerr },
title = {Improving {Nevergrad}'s Algorithm Selection Wizard {NGOpt}
Through Automated Algorithm Configuration},
pages = {18--31},
doi = {10.1007/978-3-031-14714-2_2},
abstract = {Algorithm selection wizards are effective and versatile tools
that automatically select an optimization algorithm given
high-level information about the problem and available
computational resources, such as number and type of decision
variables, maximal number of evaluations, possibility to
parallelize evaluations, etc. State-of-the-art algorithm
selection wizards are complex and difficult to improve. We
propose in this work the use of automated configuration
methods for improving their performance by finding better
configurations of the algorithms that compose them. In
particular, we use elitist iterated racing (irace) to find
CMA configurations for specific artificial benchmarks that
replace the hand-crafted CMA configurations currently used in
the NGOpt wizard provided by the Nevergrad platform. We
discuss in detail the setup of irace for the purpose of
generating configurations that work well over the diverse set
of problem instances within each benchmark. Our approach
improves the performance of the NGOpt wizard, even on
benchmark suites that were not part of the tuning by irace.}
}
@inproceedings{TreWag2019msr,
author = {Treude, Christoph and Markus Wagner },
title = {Predicting Good Configurations for GitHub and Stack Overflow
Topic Models},
booktitle = {Proceedings of the 16th International Conference on Mining
Software Repositories},
year = 2019,
series = {MSR '19},
pages = {84--95},
address = {Piscataway, NJ},
publisher = {IEEE Press},
location = {Montreal, Quebec, Canada},
numpages = 12,
doi = {10.1109/MSR.2019.00022},
acmid = 3341897,
keywords = {algorithm portfolio, corpus features, topic modelling}
}
@misc{Trick2018sup,
author = { Michael A. Trick },
title = {Graph Coloring Instances},
howpublished = {\url{https://mat.gsia.cmu.edu/COLOR/instances.html}},
year = 2018
}
@incollection{Tsu06,
address = { Heidelberg, Germany},
publisher = {Springer},
year = 2006,
volume = 4150,
series = {Lecture Notes in Computer Science},
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and L. M. Gambardella and Mauro Birattari and
Martinoli, A. and Poli, R. and Thomas St{\"u}tzle },
booktitle = {Ant Colony Optimization and Swarm Intelligence, 5th
International Workshop, ANTS 2006},
title = {An Enhanced Aggregation Pheromone System for Real-Parameter
Optimization in the {ACO} Metaphor},
author = {S. Tsutsui},
pages = {60--71}
}
@incollection{Tsutsui06:ppsn,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 4193,
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {IX}},
publisher = {Springer},
year = 2006,
editor = {Runarsson, Thomas Philip and Hans-Georg Beyer and Edmund K. Burke and Juan-Juli{\'a}n Merelo and Darrell Whitley and Xin Yao },
author = {S. Tsutsui},
title = {{cAS}: Ant Colony Optimization with Cunning Ants},
pages = {162--171}
}
@book{Tufte1982vis,
author = {Tufte, Edward R.},
title = {The Visual Display of Quantitative Information},
publisher = {Graphics Press},
abstract = {The classic book on statistical graphics, charts,
tables. Theory and practice in the design of data graphics,
250 illustrations of the best (and a few of the worst)
statistical graphics, with detailed analysis of how to
display data for precise, effective, quick analysis. Design
of the high-resolution displays, small multiples. Editing and
improving graphics. The data-ink ratio. Time-series,
relational graphics, data maps, multivariate
designs. Detection of graphical deception: design variation
vs. data variation. Sources of deception. Aesthetics and data
graphical displays. This new edition provides excellent color
reproductions of the many graphics of William Playfair, adds
color to other images, and includes all the changes and
corrections accumulated during 17 printings of the first
edition.},
address = {Cheshire, CT},
edition = {2nd},
isbn = {0-9613921-4-2},
year = 2001,
origyear = 1982,
keywords = {data visualization, information graphics, cognitive science}
}
@inproceedings{TurBerKra2016safemdp,
year = 2016,
editor = {D. D. Lee and M. Sugiyama and U. V. Luxburg and I. Guyon and
R. Garnett},
booktitle = {Advances in Neural Information Processing Systems (NIPS 29)},
title = {Safe Exploration in Finite {Markov} Decision Processes with
{Gaussian} Processes},
author = {Turchetta, Matteo and Berkenkamp, Felix and Krause, Andreas},
pages = {4312--4320},
keywords = {SafeMDP},
doi = {10.1109/TEVC.2014.2313407},
epub = {http://papers.nips.cc/paper/6357-safe-exploration-in-finite-markov-decision-processes-with-gaussian-processes}
}
@inproceedings{TurBerKra2019safe,
epub = {http://papers.nips.cc/book/advances-in-neural-information-processing-systems-32-2019},
year = 2019,
editor = {Hanna M. Wallach and Hugo Larochelle and Alina Beygelzimer
and Florence d'Alch{\'{e}}{-}Buc and Emily B. Fox and Roman
Garnett},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS 32)},
title = {Safe Exploration for Interactive Machine Learning},
author = {Turchetta, Matteo and Berkenkamp, Felix and Krause, Andreas},
pages = {2887--2897},
keywords = {Reinforcement Learning; Markov Decision Process; SafeML}
}
@book{TuringWay2019,
key = {TW2019},
author = {{The Turing Way Community} and Becky Arnold and Louise Bowler
and Sarah Gibson and Patricia Herterich and Rosie Higman and
Anna Krystalli and Alexander Morley and Martin O'Reilly and
Kirstie Whitaker},
title = {The {Turing} {Way}: A Handbook for Reproducible Data Science},
month = mar,
year = 2019,
annote = {Available from \url{https://the-turing-way.netlify.app}. This work was supported by The UKRI Strategic Priorities Fund
under the EPSRC Grant EP/T001569/1, particularly the "Tools,
Practices and Systems" theme within that grant, and by The
Alan Turing Institute under the EPSRC grant EP/N510129/1.},
publisher = {Zenodo},
version = {v0.0.4},
doi = {10.5281/zenodo.3233986}
}
@incollection{TusFil2007,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 4403,
fulleditor = {Obayashi, Shigeru and Kalyanmoy Deb and Poloni, Carlo and Hiroyasu, Tomoyuki and Murata, Tadahiko},
editor = {S. Obayashi and others},
year = 2007,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2007},
author = { Tea Tu{\v s}ar and Bogdan Filipi{\v c}},
title = {Differential Evolution versus Genetic Algorithms in
Multiobjective Optimization},
pages = {257--271}
}
@incollection{TusFil2011vis4d,
address = { New York, NY},
publisher = {ACM Press},
year = 2011,
editor = {Natalio Krasnogor and Pier Luca Lanzi},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011},
title = {Visualizing {4D} approximation sets of multiobjective
optimizers with prosections},
author = { Tea Tu{\v s}ar and Bogdan Filipi{\v c}},
pages = {737--744}
}
@mastersthesis{Tusar07master,
author = { Tea Tu{\v s}ar },
title = {Design of an Algorithm for Multiobjective Optimization with
Differential Evolution},
school = {Faculty of Computer and Information Science, University of
Ljubljana},
year = 2007,
type = {M.Sc. Thesis}
}
@incollection{UldAarBan1991,
doi = {10.1007/BFb0029723},
address = {Berlin\slash Heidelberg},
aseries = {Lecture Notes in Computer Science},
avolume = 496,
publisher = {Springer},
editor = { Hans-Paul Schwefel and R. M{\"a}nner},
year = 1991,
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {I}},
author = {N. L. J. Ulder and Emile H. L. Aarts and H.-J. Bandelt and Peter J. M. van Laarhoven and Erwin Pesch },
title = {Genetic Local Search Algorithms for the Travelling Salesman
Problem},
pages = {109--116}
}
@incollection{UlrBadThi2010ppsn,
volume = 6238,
year = 2010,
address = { Heidelberg, Germany},
publisher = {Springer},
editor = {Schaefer, Robert and Cotta, Carlos and Kolodziej,
Joanna and G{\"u}nther Rudolph },
series = {Lecture Notes in Computer Science},
booktitle = {Parallel Problem Solving from Nature, PPSN XI},
author = {Tamara Ulrich and Johannes Bader and Lothar Thiele },
title = {Defining and Optimizing Indicator-Based Diversity Measures in
Multiobjective Search},
doi = {10.1007/978-3-642-15844-5_71},
pages = {707--717},
annote = {Two archive; two populations; decision space diversity}
}
@inproceedings{ValDubStu13:cec,
year = 2013,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2013 Congress on Evolutionary Computation (CEC 2013)},
key = {IEEE CEC},
author = {Andrea Valsecchi and J{\'e}r{\'e}mie Dubois-Lacoste and Thomas St{\"u}tzle and Sergio Damas and Jos{\'e} Santamar{\'i}a and Linda Marrakchi-Kacem},
title = {Evolutionary Medical Image Registration using Automatic Parameter Tuning},
pages = {1326--1333}
}
@inproceedings{ValFawGerHoo2011icaps,
year = 2011,
booktitle = {Proceedings of ICAPS-PAL11},
editor = {Karpas, Erez and Jim{\'e}nez Celorrio, Sergio and Kambhampati, Subbarao},
author = {Mauro Vallati and Chris Fawcett and Alfonso E. Gerevini and Holger H. Hoos and Alessandro Saetti},
title = {Generating Fast Domain-Optimized Planners by Automatically
Configuring a Generic Parameterised Planner}
}
@book{VanAar1987,
title = {Simulated Annealing: Theory and Applications},
author = { Peter J. M. van Laarhoven and Emile H. L. Aarts },
volume = 37,
year = 1987,
publisher = {Springer}
}
@incollection{VanHut2018hyper,
key = {SIGKDD},
month = jul,
address = { New York, NY},
publisher = {ACM Press},
year = 2018,
editor = {Yike Guo and Faisal Farooq},
booktitle = {24th {ACM} {SIGKDD} International Conference on Knowledge
Discovery and Data Mining},
author = { van Rijn, Jan N. and Frank Hutter },
title = {Hyperparameter Importance Across Datasets},
pages = {2367--2376},
doi = {10.1145/3219819.3220058},
abstract = {With the advent of automated machine learning, automated
hyperparameter optimization methods are by now routinely used
in data mining. However, this progress is not yet matched by
equal progress on automatic analyses that yield information
beyond performance-optimizing hyperparameter settings. In
this work, we aim to answer the following two questions:
Given an algorithm, what are generally its most important
hyperparameters, and what are typically good values for
these? We present methodology and a framework to answer these
questions based on meta-learning across many datasets. We
apply this methodology using the experimental meta-data
available on OpenML to determine the most important
hyperparameters of support vector machines, random forests
and Adaboost, and to infer priors for all their
hyperparameters. The results, obtained fully automatically,
provide a quantitative basis to focus efforts in both manual
algorithm design and in automated hyperparameter
optimization. The conducted experiments confirm that the
hyperparameters selected by the proposed method are indeed
the most important ones and that the obtained priors also
lead to statistically significant improvements in
hyperparameter optimization.},
numpages = 10,
keywords = {hyperparameter optimization, meta-learning, hyperparameter
importance}
}
@incollection{VanKorBlo2018,
publisher = {{AAAI} Press},
month = feb,
year = 2018,
editor = {Sheila A. McIlraith and Kilian Q. Weinberger},
booktitle = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
title = {{MERCS}: multi-directional ensembles of regression and classification trees},
author = {Van Wolputte, Elia and Korneva, Evgeniya and Blockeel, Hendrik},
pages = {4276--4283}
}
@inproceedings{VedFul2010vlfeat,
title = {{VLFeat}: An open and portable library of computer vision
algorithms},
author = {Vedaldi, Andrea and Fulkerson, Brian},
booktitle = {Proceedings of the 18th ACM international conference on
Multimedia},
pages = {1469--1472},
year = 2010,
organization = {ACM}
}
@inproceedings{VelLam1998gp,
year = 1998,
publisher = {Stanford University Bookstore},
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key = {Van Veldhuizen and Lamont, 1998a},
title = {Evolutionary Computation and Convergence to a
{Pareto} Front},
author = { David A. {Van Veldhuizen} and Gary B. Lamont },
pages = {221--228},
keywords = {generational distance}
}
@phdthesis{Veldhuizen1999phd,
title = {Multiobjective evolutionary algorithms: Classifications,
analyses, and new innovations},
author = { David A. {Van Veldhuizen} },
school = {Department of Electrical and Computer Engineering, Graduate
School of Engineering, Air Force Institute of Technology},
year = 1999,
address = {Wright-Patterson AFB, Ohio}
}
@incollection{VerCarSte2022bias,
isbn = 9781450392686,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2022},
address = { New York, NY},
year = 2022,
publisher = {ACM Press},
editor = { Jonathan E. Fieldsend and Markus Wagner },
author = { Diederick Vermetten and Caraffini, Fabio and van Stein, Bas and Anna V. Kononova },
title = {Using Structural Bias to Analyse the Behaviour of Modular
{CMA-ES}},
pages = {1674--1682},
doi = {10.1145/3520304.3534035},
location = {Boston, Massachusetts}
}
@incollection{VerLieDha2011gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2011,
editor = {Natalio Krasnogor and Pier Luca Lanzi},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011},
author = { Verel, S{\'e}bastien and Arnaud Liefooghe and Dhaenens, Clarisse },
title = {Set-based Multiobjective Fitness Landscapes: A Preliminary
Study},
pages = {769--776},
doi = {10.1145/2001576.2001681},
acmid = 2001681
}
@incollection{VerWanDoeBac2020cash,
epub = {https://dl.acm.org/citation.cfm?id=3377930},
location = {Canc{\'u}n, Mexico},
isbn = {978-1-4503-7128-5},
address = { New York, NY},
publisher = {ACM Press},
year = 2020,
editor = { Carlos A. {Coello Coello} },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2020},
author = { Diederick Vermetten and Wang, Hao and Carola Doerr and Thomas B{\"a}ck },
title = {Integrated vs. Sequential Approaches for Selecting and Tuning
{CMA-ES} Variants},
doi = {10.1145/3377930.3389831}
}
@incollection{VerWanLopDoe2022undersampling,
location = {Boston, Massachusetts},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2022},
address = { New York, NY},
year = 2022,
publisher = {ACM Press},
editor = { Jonathan E. Fieldsend and Markus Wagner },
author = { Diederick Vermetten and Wang, Hao and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Carola Doerr and Thomas B{\"a}ck },
title = {Analyzing the Impact of Undersampling on the Benchmarking and
Configuration of Evolutionary Algorithms},
doi = {10.1145/3512290.3528799},
abstract = {The stochastic nature of iterative optimization heuristics
leads to inherently noisy performance measurements. Since
these measurements are often gathered once and then used
repeatedly, the number of collected samples will have a
significant impact on the reliability of algorithm
comparisons. We show that care should be taken when making
decisions based on limited data. Particularly, we show that
the number of runs used in many benchmarking studies, e.g.,
the default value of 15 suggested by the COCO environment,
can be insufficient to reliably rank algorithms on well-known
numerical optimization benchmarks.Additionally, methods for
automated algorithm configuration are sensitive to
insufficient sample sizes. This may result in the
configurator choosing a "lucky" but poor-performing
configuration despite exploring better ones. We show that
relying on mean performance values, as many configurators do,
can require a large number of runs to provide accurate
comparisons between the considered configurations. Common
statistical tests can greatly improve the situation in most
cases but not always. We show examples of performance losses
of more than 20\%, even when using statistical races to
dynamically adjust the number of runs, as done by irace. Our
results underline the importance of appropriately considering
the statistical distribution of performance values.},
pages = {867--875},
numpages = 9,
keywords = {parameter tuning, evolution strategies, algorithm
configuration, performance measures}
}
@incollection{VidLeiTey2022eurogp,
address = { Cham, Switzerland},
publisher = {Springer Nature},
year = 2022,
series = {Lecture Notes in Computer Science},
editor = {Eric Medvet and Gisele Pappa and Bing Xue},
booktitle = {Proceedings of the 25th European Conference on Genetic Programming, EuroGP 2022},
author = {Mathurin Videau and Alessandro Leite and Olivier Teytaud and Marc Schoenauer },
title = {Multi-Objective Genetic Programming for Explainable
Reinforcement Learning},
pages = {256--281},
keywords = {genetic algorithms, genetic programming: Poster}
}
@inproceedings{Vieira2021nas,
year = 2021,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2021 Congress on Evolutionary Computation (CEC 2021)},
key = {IEEE CEC},
author = {Vieira, Carlos and P{\'e}rez C{\'a}ceres, Leslie and Leonardo C. T. Bezerra },
title = {Evaluating Anytime Performance on {NAS}-{Bench}-101},
pages = {1249--1256},
doi = {10.1109/CEC45853.2021.9504902}
}
@phdthesis{Violin2014PhD,
author = {Alessia Violin},
title = {Mathematical Programming Approaches to Pricing Problems},
school = {Facult\'{e} de Sciences, Universit\'{e} Libre de Bruxelles and Dipartimento di Ingegneria e Architettura, Universit\`{a} degli studi di Trieste},
year = 2014,
annote = {Supervised by Dr. Martine Labb\'{e} and Dr. Lorenzo Castelli}
}
@incollection{VosHanIgel2010gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2010,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2010},
editor = {Martin Pelikan and J{\"u}rgen Branke },
title = {Improved Step Size Adaptation for the {MO-CMA-ES}},
author = { Vo{\ss}, Thomas and Nikolaus Hansen and Christian Igel },
pages = {487--494}
}
@incollection{VouTsa2003,
publisher = {Kluwer Academic Publishers, Norwell, MA},
year = 2002,
editor = { Fred Glover and Gary A. Kochenberger},
booktitle = {Handbook of Metaheuristics},
author = { Christos Voudouris and Edward P. K. Tsang },
title = {Guided Local Search},
pages = {185--218}
}
@book{WIS:MOA_vol2,
editor = { Dragan A. Savic and Godfrey A. Walters },
title = {Water Industry Systems: Modelling and Optimization
Applications},
booktitle = {Water Industry Systems: Modelling and Optimization
Applications},
year = 1999,
volume = 2,
publisher = { Research Studies Press Ltd. },
address = {Baldock, United Kingdom}
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@incollection{WacSuiYueOno2018aaai,
publisher = {{AAAI} Press},
month = feb,
year = 2018,
editor = {Sheila A. McIlraith and Kilian Q. Weinberger},
booktitle = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
author = {Akifumi Wachi and Yanan Sui and Yisong Yue and Masahiro Ono},
title = {Safe Exploration and Optimization of Constrained {MDP}s Using
{Gaussian} Processes},
pages = {6548--6556},
keywords = {Markov Decision Process, Gaussian Processes},
abstract = {We present a reinforcement learning approach to explore and
optimize a safety-constrained Markov Decision
Process(MDP). In this setting, the agent must maximize
discounted cumulative reward while constraining the
probability of entering unsafe states, defined using a safety
function being within some tolerance. The safety values of
all states are not known a priori, and we probabilistically
model them via a Gaussian Process (GP) prior. As such,
properly behaving in such an environment requires balancing a
three-way trade-off of exploring the safety function,
exploring the reward function, and exploiting acquired
knowledge to maximize reward. We propose a novel approach to
balance this trade-off. Specifically, our approach explores
unvisited states selectively; that is, it prioritizes the
exploration of a state if visiting that state significantly
improves the knowledge on the achievable cumulative
reward. Our approach relies on a novel information gain
criterion based on Gaussian Process representations of the
reward and safety functions. We demonstrate the effectiveness
of our approach on a range of experiments, including a
simulation using the real Martian terrain data.},
doi = {10.1609/aaai.v32i1.12103}
}
@incollection{WagBeuNau2007:many,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 4403,
fulleditor = {Obayashi, Shigeru and Kalyanmoy Deb and Poloni, Carlo and Hiroyasu, Tomoyuki and Murata, Tadahiko},
editor = {S. Obayashi and others},
year = 2007,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2007},
author = { Tobias Wagner and Nicola Beume and Boris Naujoks },
title = {{Pareto}-, Aggregation-, and Indicator-Based Methods in Many-Objective
Optimization},
pages = {742--756}
}
@inproceedings{WagFriLin2017vertex,
year = 2017,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2017 Congress on Evolutionary Computation (CEC 2017)},
key = {IEEE CEC},
author = { Markus Wagner and Tobias Friedrich and Marius Thomas Lindauer },
title = {Improving local search in a minimum vertex cover solver for
classes of networks},
pages = {1704--1711},
keywords = {graph theory;search problems;local search;minimum vertex
cover solver;network classes;straightforward alternative
approach;benchmark sets;graphs;algorithm portfolio;single
integrated approach;Training;Portfolios;Algorithm design and
analysis;Prediction algorithms;Machine learning
algorithms;Optimization;Benchmark testing,smac,paramils},
doi = {10.1109/CEC.2017.7969507}
}
@incollection{WagNeu2013,
address = { New York, NY},
publisher = {ACM Press},
year = 2015,
editor = {Sara Silva and Anna I. Esparcia{-}Alc{\'{a}}zar },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015},
title = {A Fast Approximation-guided Evolutionary Multi-objective
Algorithm},
author = { Markus Wagner and Frank Neumann },
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Constrained Global Optimization},
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}
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@inproceedings{WalIyeVen98,
publisher = {{AAAI} Press\slash {MIT} Press, Menlo Park, CA},
year = 1998,
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year = 2018,
address = {Piscataway, NJ},
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key = {IEEE CEC},
title = {Hierarchical surrogate-assisted evolutionary multi-scenario
airfoil shape optimization},
author = {Wang, Handing and Doherty, John and Yaochu Jin },
pages = {1--8},
keywords = {scenario-based}
}
@incollection{WanDonChenLin2014,
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@incollection{WanMeiZha2021mogp,
doi = {10.1145/3449639.3459373},
location = {Lille, France},
address = { New York, NY},
publisher = {ACM Press},
year = 2021,
editor = { Chicano, Francisco and Krzysztof Krawiec },
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title = {Two-stage multi-objective genetic programming with archive
for uncertain capacitated arc routing problem},
author = {Wang, Shaolin and Mei, Yi and Zhang, Mengjie },
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@incollection{Ward2008multivariate,
title = {Multivariate data glyphs: Principles and practice},
author = {Ward, Matthew O.},
booktitle = {Handbook of Data Visualization},
editor = {Chen, Chun-houh and H{\"a}rdle, Wolfgang Karl and Unwin, Antony},
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publisher = {Springer}
}
@inproceedings{Wau2017eternity,
author = {Wauters, Tony},
title = {10 years of Eternity II--from \$2 million puzzle to
challenging optimization problem},
booktitle = {International Workshop on Cutting, Packing and Related
Topics},
address = {Gent, Belgium},
year = 2017,
url = {https://lirias.kuleuven.be/1675982?limo=0},
abstract = {The Eternity II (EII) puzzle is a commercial edge matching
puzzle in which 256 square tiles with four coloured edges
must be arranged on a 16 by 16 grid such that all tile edges
are matched. In addition, a complete solution requires that
the `grey' patterns, which appear only on a subset of the
tiles, should be matched to the outer edges of the grid. The
puzzle belongs to the more general class of Edge Matching
Puzzles, which have been shown to be NP-complete. In July
2007, toy distributor Tomy UK Ltd. released this challenging
edge matching puzzle with a \$2 million prize. However, to
the best of our knowledge, no complete solution has ever been
found. Meanwhile, the final scrutiny date for the cash price,
31 December 2010, has passed, leaving the large money prize
unclaimed. In its 10 years of existence many people tried to
solve EII and some are still trying. Many approaches to Edge
Matching Puzzles are reported in the literature. Among these
approaches are constraint programming and backtracking,
metaheuristics, and evolutionary methods. Other approaches
translate the problem into SAT, MILP or max-clique and then
solve it with appropriate state of the art solvers. Some
approaches have also been implemented on parallel computing
or dedicated hardware.}
}
@incollection{Wegener2005,
year = 2005,
volume = 3580,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
booktitle = {Proceedings of the 32nd International Colloquium on Automata, Languages and Programming, {ICALP} 2005},
editor = {Lu{\'i}s Caires and Giuseppe F. Italiano and Lu{\'i}s Monteiro and Catuscia Palamidessi and Moti Yung},
title = {Simulated annealing beats metropolis in combinatorial optimization},
author = { Ingo Wegener },
pages = {589--601}
}
@inproceedings{Wegley00,
author = { Chad Wegley and Muzaffar Eusuff and Kevin E. Lansey },
title = {Determining Pump Operations Using Particle Swarm
Optimization},
booktitle = {Building Partnerships, Proceedings of the Joint
Conference on Water Resources Engineering and Water
Resources Planning and Management},
year = 2000,
editor = {Rollin H. Hotchkiss and Michael Glade},
address = {Minneapolis, USA}
}
@inproceedings{Wegner76research,
title = {Research paradigms in computer science},
author = {Peter Wegner},
booktitle = {ICSE'76: Proceedings of the 2nd international conference on
Software engineering},
month = oct,
year = 1976,
pages = {322--330}
}
@inproceedings{WeiSau2006introduction,
editor = {Anthony Cohn},
publisher = {{AAAI} Press\slash {MIT} Press, Menlo Park, CA},
year = 2006,
volume = 6,
booktitle = {Proceedings of the 21st National Conference on Artificial Intelligence},
author = {Weinberger, Kilian Q. and Saul, Lawrence K.},
title = {An Introduction to Nonlinear Dimensionality Reduction by
Maximum Variance Unfolding},
pages = {1683--1686}
}
@inproceedings{WeiShaSau2004dimension,
publisher = {ACM Press},
address = { New York, NY},
editor = {Carla E. Brodley},
booktitle = {Proceedings of the 21st International Conference on Machine Learning, {ICML} 2004},
year = 2004,
author = {Kilian Q. Weinberger and Fei Sha and Lawrence K. Saul},
title = {Learning a kernel matrix for nonlinear dimensionality
reduction},
doi = {10.1145/1015330.1015345},
abstract = {We investigate how to learn a kernel matrix for high
dimensional data that lies on or near a low dimensional
manifold. Noting that the kernel matrix implicitly maps the
data into a nonlinear feature space, we show how to discover
a mapping that "unfolds" the underlying manifold from which
the data was sampled. The kernel matrix is constructed by
maximizing the variance in feature space subject to local
constraints that preserve the angles and distances between
nearest neighbors. The main optimization involves an instance
of semidefinite programming---a fundamentally different
computation than previous algorithms for manifold learning,
such as Isomap and locally linear embedding. The optimized
kernels perform better than polynomial and Gaussian kernels
for problems in manifold learning, but worse for problems in
large margin classification. We explain these results in
terms of the geometric properties of different kernels and
comment on various interpretations of other manifold learning
algorithms as kernel methods.}
}
@incollection{WesBeuRud2010ppsn,
volume = 6238,
year = 2010,
address = { Heidelberg, Germany},
publisher = {Springer},
editor = {Schaefer, Robert and Cotta, Carlos and Kolodziej,
Joanna and G{\"u}nther Rudolph },
series = {Lecture Notes in Computer Science},
booktitle = {Parallel Problem Solving from Nature, PPSN XI},
author = { Simon Wessing and Nicola Beume and G{\"u}nther Rudolph and Boris Naujoks },
title = {Parameter Tuning Boosts Performance of Variation
Operators in Multiobjective Optimization},
pages = {728--737},
doi = {10.1007/978-3-642-15844-5_73}
}
@incollection{Whaley2011atlas,
doi = {10.1007/978-0-387-09766-4_244},
publisher = {Springer, US},
year = 2011,
editor = {David Padua},
booktitle = {Encyclopedia of Parallel Computing},
author = {Clint R. Whaley},
title = {{ATLAS}: Automatically Tuned Linear Algebra Software},
pages = {95--101}
}
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year = 2012,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2012 Congress on Evolutionary Computation (CEC 2012)},
key = {IEEE CEC},
author = {While, L. and Bradstreet, L.},
title = {Applying the {WFG} Algorithm to Calculate Incremental
Hypervolumes},
pages = {1--8}
}
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publisher = {CSREA Press},
year = 1998,
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Parallel and Distributed Processing Techniques and
Applications (PDPTA'98)},
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title = {Connection Management Using Adaptive Mobile Agents},
pages = {802--809}
}
@incollection{WieStu2006:ants,
address = { Heidelberg, Germany},
publisher = {Springer},
year = 2006,
volume = 4150,
series = {Lecture Notes in Computer Science},
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and L. M. Gambardella and Mauro Birattari and
Martinoli, A. and Poli, R. and Thomas St{\"u}tzle },
booktitle = {Ant Colony Optimization and Swarm Intelligence, 5th
International Workshop, ANTS 2006},
author = {W. Wiesemann and Thomas St{\"u}tzle },
title = {Iterated Ants: An Experimental Study for the
Quadratic Assignment Problem},
pages = {179--190}
}
@techreport{Wiegele2007biqmac,
title = {{Biq} {Mac} {Library} -- A collection of {Max}-{Cut} and
quadratic 0-1 programming instances of medium size},
author = {Wiegele, Angelika},
institution = {Institut f{\"u}r Mathematik, Alpen-Adria-Universit{\"a}t
Klagenfurt},
year = 2007,
url = {http://biqmac.aau.at/biqmaclib.pdf}
}
@misc{Wiegele2007sup,
author = {Wiegele, Angelika},
title = {{Biq} {Mac} {Library} -- Binary Quadratic and Max Cut
Library},
howpublished = {\url{http://biqmac.aau.at/biqmaclib.html}},
year = 2007
}
@incollection{Wierzbicki1980mcdmta,
address = { Heidelberg, Germany},
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series = {Lecture Notes in Economics and Mathematical Systems},
number = 177,
editor = {Fandel, G. and Gal, T.},
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@incollection{WolMer2009:evocop,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 5482,
year = 2009,
editor = { Carlos Cotta and P. Cowling},
booktitle = {Proceedings of EvoCOP 2009 -- 9th European Conference on Evolutionary Computation in Combinatorial Optimization },
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year = 2010,
publisher = {{AAAI} Press},
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editor = {Maria Fox and David Poole},
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title = {Hydra: Automatically Configuring Algorithms for
Portfolio-Based Selection},
keywords = {automated algorithm design; portfolio-based algorithm
selection; automated algorithm configuration; SAT; stochastic
local search},
doi = {10.1609/aaai.v24i1.7565}
}
@techreport{XuHutHoo2011tr01,
title = {{Hydra-MIP}: Automated Algorithm Configuration and
Selection for Mixed Integer Programming},
author = { Lin Xu and Frank Hutter and Holger H. Hoos and Kevin Leyton-Brown },
institution = {Department of Computer Science, University of British Columbia, Canada},
year = 2011,
number = {TR-2011-01},
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}
@incollection{XuKhHo2016,
address = { Cham, Switzerland},
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volume = 10079,
booktitle = {Learning and Intelligent Optimization, 10th International Conference, LION 10},
publisher = {Springer},
year = 2016,
editor = {Paola Festa and Meinolf Sellmann and Joaquin Vanschoren },
title = {Quantifying the similarity of algorithm configurations},
author = { Lin Xu and KhudaBukhsh, A. R. and Holger H. Hoos and Kevin Leyton-Brown },
pages = {203--217}
}
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publisher = {{IEEE}},
year = 2006,
booktitle = {Proceedings of the International Joint Conference on Neural
Networks, {IJCNN} 2006},
key = {IJCNN},
author = {Jian{-}Wu Xu and Puskal P. Pokharel and Ant{\'{o}}nio
R. C. Paiva and Jos{\'{e}} C. Pr{\'i}ncipe},
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}
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volume = 10199,
year = 2017,
publisher = {Springer},
editor = {Squillero, Giovanni and Sim, Kevin},
author = {Yaman, Anil and Hallawa, Ahmed and Coler, Matt and Iacca, Giovanni},
title = {Presenting the {ECO}: evolutionary computation ontology},
pages = {603--619}
}
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numpages = 360,
year = 1999,
language = {English},
keywords = {Evolutionary programming (Computer science); Neural networks
(Computer science); Evolutionary computation}
}
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Symposium on},
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title = {Heuristic Generation via Parameter Tuning for Online Bin Packing},
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keywords = {irace}
}
@incollection{YasAraMei2019comparison,
author = {Yasojima, Carlos and Ara{\'u}jo, Tiago and Meiguins, Bianchi
and Neto, Nelson and Morais, Jefferson},
booktitle = {Progress in Artificial Intelligence},
title = {A Comparison of Genetic Algorithms and Particle Swarm
Optimization to Estimate Cluster-Based Kriging Parameters},
year = 2019,
address = { Cham, Switzerland},
editor = {Moura Oliveira, Paulo and Novais, Paulo and Reis, Lu{\'i}s
Paulo },
pages = {750--761},
publisher = {Springer International Publishing},
abstract = {Kriging is one of the most used spatial estimation methods in
real-world applications. Some kriging parameters must be
estimated in order to reach a good accuracy in the
interpolation process, however, this task remains a
challenge. Various optimization methods have been tested to
find good parameters of the kriging process. In recent years,
many authors are using bio-inspired techniques and achieving
good results in estimating these parameters in comparison
with traditional techniques. This paper presents a comparison
between well known bio-inspired techniques such as Genetic
Algorithms and Particle Swarm Optimization in the estimation
of the essential kriging parameters: nugget, sill, range,
angle, and factor. In order to perform the tests, we proposed
a methodology based on the cluster-based kriging
method. Considering the Friedman test, the results showed no
statistical difference between the evaluated algorithms in
optimizing kriging parameters. On the other hand, the
Particle Swarm Optimization approach presented a faster
convergence, which is important in this high-cost
computational problem.},
isbn = {978-3-030-30241-2}
}
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address = {Piscataway, NJ},
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booktitle = {Proceedings of the 2016 Congress on Evolutionary Computation (CEC 2016)},
key = {IEEE CEC},
author = { G{\"{u}}rcan Yavuz and Do\v{g}an Ayd{\i}n and Thomas St{\"u}tzle },
title = {Self-adaptive Search Equation-based Artificial Bee Colony Algorithm on the {CEC} 2014 Benchmark Functions},
pages = {1173--1180}
}
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isbn = {978-3-642-14125-6},
address = { Heidelberg, Germany},
publisher = {Springer},
year = 2011,
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editor = {F{\"u}rnkranz, Johannes and Eyke H{\"u}llermeier },
author = {Yu, Philip L. H. and Wan, Wai Ming and Lee, Paul H.},
title = {Decision Tree Modeling for Ranking Data},
pages = {83--106},
doi = {10.1007/978-3-642-14125-6_5}
}
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address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 5296,
editor = { Mar{\'i}a J. Blesa and Christian Blum and Carlos Cotta and Antonio J. Fern{\'a}ndez and Jos\'e E. Gallardo and Andrea Roli and M. Sampels },
year = 2008,
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title = {Iterated Greedy Algorithms for a Real-World Cyclic Train
Scheduling Problem},
pages = {102--116}
}
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aeditor = { Xin Yao and Edmund K. Burke and Jos{\'e} A. Lozano and Smith, Jim and
Merelo-Guerv{\'o}s, Juan Juli{\'a}n and Bullinaria, John A.
and Rowe, Jonathan E. and Ti{\v{n}}o, Peter and Kab{\'a}n,
Ata and Schwefel, Hans-Paul},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 3242,
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {VIII}},
publisher = {Springer},
year = 2004,
editor = { Xin Yao and others},
author = {Yuan, Bo and Gallagher, Marcus},
title = {Statistical Racing Techniques for Improved Empirical
Evaluation of Evolutionary Algorithms},
pages = {172--181}
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address = { Berlin, Germany},
publisher = {Springer},
year = 2007,
booktitle = {Parameter Setting in Evolutionary Algorithms},
editor = {F. Lobo and C. F. Lima and Zbigniew Michalewicz },
author = {Yuan, Bo and Gallagher, Marcus},
title = {Combining {Meta}-{EAs} and racing for difficult {EA}
parameter tuning tasks},
pages = {121--142}
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editor = { Christian Blum and Alba, Enrique },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2013},
author = { Zhi Yuan and Marco A. {Montes de Oca} and Thomas St{\"u}tzle and Hoong Chuin Lau and Mauro Birattari },
title = {An Analysis of Post-selection in Automatic Configuration},
pages = {1557--1564}
}
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year = 2017,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2017 Congress on Evolutionary Computation (CEC 2017)},
key = {IEEE CEC},
author = {Lin Yuefeng and Wenli Du and Thomas St{\"u}tzle },
title = {Three {L-SHADE} Based Algorithms on Mixed-variables Optimization Problems},
pages = {2274--2281}
}
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year = 2012,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2012 Congress on Evolutionary Computation (CEC 2012)},
key = {IEEE CEC},
title = {An adaptive data structure for evolutionary multi-objective
algorithms with unbounded archives},
author = {Yuen, Joseph and Gao, Sophia and Markus Wagner and Frank Neumann },
pages = {1--8}
}
@incollection{YunEps2012learn,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 7219,
booktitle = {Learning and Intelligent Optimization, 6th International Conference, LION 6},
publisher = {Springer},
year = 2012,
editor = { Youssef Hamadi and Marc Schoenauer },
author = {Yun, Xi and Epstein, Susan L.},
title = {Learning Algorithm Portfolios for Parallel Execution},
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doi = {10.1007/978-3-642-34413-8_23}
}
@incollection{ZaeStoBar2014:ppsn,
year = 2014,
volume = 8672,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Thomas Bartz-Beielstein and J{\"u}rgen Branke and Bogdan Filipi{\v c} and Jim Smith},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XIII}},
author = { Martin Zaefferer and J. Stork and Thomas Bartz-Beielstein },
title = {Distance Measures for Permutations in Combinatorial Efficient
Global Optimization},
pages = {373--383},
doi = {10.1007/978-3-319-10762-2_37},
keywords = {CEGO, Bayesian optimization}
}
@incollection{ZaeStoFriFisNauBar2014,
address = { New York, NY},
publisher = {ACM Press},
year = 2014,
editor = {Christian Igel and Dirk V. Arnold},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2014},
author = { Martin Zaefferer and J. Stork and M. Friese and Andreas Fischbach and Boris Naujoks and Thomas Bartz-Beielstein },
title = {Efficient Global Optimization for Combinatorial Problems},
pages = {871--878},
doi = {10.1145/2576768.2598282},
keywords = {CEGO, Bayesian optimization},
annote = {Proposed CEGO algorithm}
}
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year = 2005,
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}
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isbn = {978-1-4503-1963-8},
address = { New York, NY},
publisher = {ACM Press},
year = 2013,
editor = { Christian Blum and Alba, Enrique },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2013},
author = {Tiantian Zhang and Michael Georgiopoulos and Georgios C. Anagnostopoulos},
title = {{S-Race}: A Multi-Objective Racing Algorithm},
pages = {1565--1572}
}
@incollection{ZhaGeoAna2015sprint,
address = { New York, NY},
publisher = {ACM Press},
year = 2015,
editor = {Sara Silva and Anna I. Esparcia{-}Alc{\'{a}}zar },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015},
author = {Zhang, Tiantian and Georgiopoulos, Michael and
Anagnostopoulos, Georgios C.},
title = {{SPRINT}: Multi-Objective Model Racing},
pages = {1383--1390},
numpages = 8,
doi = {10.1145/2739480.2754791},
keywords = {model selection, multi-objective optimization, racing
algorithm, sequential probability ratio test},
annote = {Extended version published as \cite{ZhaGeoAna2016}}
}
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address = {Piscataway, NJ},
publisher = {IEEE Press},
year = 2009,
booktitle = {Proceedings of the 2009 Congress on Evolutionary Computation (CEC 2009)},
key = {IEEE CEC},
author = { Zhang, Qingfu and Wudong Liu and Hui Li},
title = {The Performance of a New Version of {MOEA/D} on {CEC09}
Unconstrained {MOP} Test Instances},
pages = {203--208}
}
@techreport{ZhaZhoZha2009cec,
author = { Zhang, Qingfu and A. Zhou and S. Zhao and Ponnuthurai N. Suganthan and W. Liu and S. Tiwari},
title = {Multiobjective Optimization Test Instances for the {CEC} 2009
Special Session and Competition},
institution = {School of Computer Science and Electronic Engieering,
University of Essex},
type = {Working Report},
year = 2009,
number = {CES-487},
month = apr,
annote = {Proposed UF benchmark}
}
@misc{Zhang09moeacomp,
author = { Zhang, Qingfu and Ponnuthurai N. Suganthan },
title = {Special Session on Performance Assessment of
Multiobjective Optimization Algorithms/{CEC}'09
{MOEA} Competition},
howpublished = {\url{https://www3.ntu.edu.sg/home/epnsugan/index_files/CEC09-MOEA/CEC09-MOEA.htm}},
annote = {Previously available at \url{http://dces.essex.ac.uk/staff/qzhang/moeacompetition09.htm}},
year = 2009
}
@inproceedings{ZilIshMos2016mut,
year = 2016,
booktitle = {Computational Intelligence (SSCI), 2016 IEEE Symposium Series
on},
editor = {Chen, Xuewen and Stafylopatis, Andreas},
author = {Zille, Heiner and Ishibuchi, Hisao and Mostaghim, Sanaz and Nojima, Yusuke},
title = {Mutation operators based on variable grouping for
multi-objective large-scale optimization},
pages = {1--8},
doi = {10.1109/SSCI.2016.7850214},
annote = {linked polynomial mutation}
}
@incollection{ZitBroThi2007emo,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 4403,
fulleditor = {Obayashi, Shigeru and Kalyanmoy Deb and Poloni, Carlo and Hiroyasu, Tomoyuki and Murata, Tadahiko},
editor = {S. Obayashi and others},
year = 2007,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2007},
author = { Eckart Zitzler and Dimo Brockhoff and Lothar Thiele },
title = {The Hypervolume Indicator Revisited: On the Design
of {Pareto}-compliant Indicators Via Weighted
Integration},
pages = {862--876},
doi = {10.1007/978-3-540-70928-2_64},
supplement = {https://sop.tik.ee.ethz.ch/download/supplementary/weightedHypervolume/}
}
@incollection{ZitKnoThi2008quality,
editor = { J{\"u}rgen Branke and Kalyanmoy Deb and Kaisa Miettinen and Roman S{\l}owi{\'n}ski },
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 5252,
year = 2008,
booktitle = {Multiobjective Optimization: Interactive and Evolutionary
Approaches},
author = { Eckart Zitzler and Joshua D. Knowles and Lothar Thiele },
title = {Quality Assessment of {Pareto} Set Approximations},
pages = {373--404},
doi = {10.1109/TEVC.2009.2016569}
}
@incollection{ZitKun2004ppsn,
aeditor = { Xin Yao and Edmund K. Burke and Jos{\'e} A. Lozano and Smith, Jim and
Merelo-Guerv{\'o}s, Juan Juli{\'a}n and Bullinaria, John A.
and Rowe, Jonathan E. and Ti{\v{n}}o, Peter and Kab{\'a}n,
Ata and Schwefel, Hans-Paul},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 3242,
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {VIII}},
publisher = {Springer},
year = 2004,
editor = { Xin Yao and others},
author = { Eckart Zitzler and Simon K{\"u}nzli},
title = {Indicator-based Selection in Multiobjective Search},
pages = {832--842},
keywords = {IBEA}
}
@techreport{ZitLauThi2001tr,
author = { Eckart Zitzler and Marco Laumanns and Lothar Thiele },
title = {{SPEA2}: Improving the Strength {Pareto} Evolutionary
Algorithm},
institution = {Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Z{\"u}rich, Switzerland},
year = 2001,
number = 103,
annote = {Published as \cite{ZitLauThie2002spea2}}
}
@incollection{ZitLauThie2002spea2,
isbn = {84-89925-97-6},
shorteditor = {K. C. Giannakoglou and others},
booktitle = {Evolutionary Methods for Design, Optimisation and Control},
year = 2002,
publisher = {CIMNE, Barcelona, Spain},
editor = {K. C. Giannakoglou and D. T. Tsahalis and J. Periaux and
K. D. Papaliliou and T. Fogarty},
author = { Eckart Zitzler and Marco Laumanns and Lothar Thiele },
title = {{SPEA2}: Improving the Strength {Pareto} Evolutionary
Algorithm for Multiobjective Optimization},
pages = {95--100},
annote = {Proposed SPEA2}
}
@incollection{ZitThi1998ppsn,
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Agoston E. Eiben and Thomas B{\"a}ck and Marc Schoenauer and Hans-Paul Schwefel },
volume = 1498,
series = {Lecture Notes in Computer Science},
year = 1998,
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {V}},
author = { Eckart Zitzler and Lothar Thiele },
title = {Multiobjective Optimization Using Evolutionary Algorithms -
{A} Comparative Case Study},
pages = {292--301},
doi = {10.1007/BFb0056872},
annote = {Proposed hypervolume measure}
}
@incollection{ZitThiBad2008ppsn,
year = 2008,
volume = 5199,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { G{\"u}nther Rudolph and others},
aeditor = { G{\"u}nther Rudolph and Thomas Jansen and Simon Lucas and
Carlo Poloni and Nicola Beume},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {X}},
author = { Eckart Zitzler and Lothar Thiele and Johannes Bader },
title = {{SPAM}: {Set} Preference Algorithm for
Multiobjective Optimization},
pages = {847--858}
}
@incollection{TiwKochFad2008amga,
address = { New York, NY},
publisher = {ACM Press},
year = 2008,
editor = {Conor Ryan},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2008},
title = {{AMGA}: An archive-based micro genetic algorithm for
multi-objective optimization},
author = {Tiwari, Santosh and Koch, Patrick and Fadel, Georges and Kalyanmoy Deb },
pages = {729--736},
doi = {10.1145/1389095.1389235}
}
@phdthesis{ZitzlerPhD,
title = {Evolutionary Algorithms for Multiobjective Optimization:
Methods and Applications},
author = { Eckart Zitzler },
school = {ETH Z{\"u}rich, Switzerland},
year = 1999,
atype = {{Ph.D.} thesis}
}
@inproceedings{ZujEid2011newdm,
title = {New decision maker model for multiobjective optimization
interactive methods},
author = {Zujevs, Andrejs and Eiduks, Janis},
booktitle = {17th International Conference on Information and Software
Technologies, Kaunas, Lithuania},
year = 2011,
keywords = {Machine Decision Maker},
pages = {51--58},
epub = {https://isd.ktu.lt/it2011/material/menu/proceedings.html}
}
@incollection{ants2008-cminer,
volume = 5217,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and Mauro Birattari and Christian Blum and Clerc, Maurice and Thomas St{\"u}tzle and A. F. T. Winfield},
year = 2008,
booktitle = {Ant Colony Optimization and Swarm Intelligence, 6th
International Conference, ANTS 2008},
author = {F. E. B. Otero and A. A. Freitas and C. G. Johnson},
title = {{cAnt-Miner}: An Ant Colony Classification Algorithm
to Cope with Continuous Attributes},
pages = {48--59}
}
@incollection{dePDoeDoe2015:gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2015,
editor = {Sara Silva and Anna I. Esparcia{-}Alc{\'{a}}zar },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015},
author = {Axel {de Perthuis de Laillevault} and Benjamin Doerr and Carola Doerr },
title = {Money for Nothing: Speeding Up Evolutionary Algorithms
Through Better Initialization},
pages = {815--822}
}
@misc{nevergrad,
author = {Jeremy Rapin and Olivier Teytaud },
title = {Nevergrad: A gradient-free optimization platform},
year = 2018,
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/FacebookResearch/Nevergrad}}
}
@misc{oscar,
author = {{OscaR Team}},
title = {{OscaR}: {Scala} in {OR}},
year = 2012,
note = {Available from \url{https://bitbucket.org/oscarlib/oscar}}
}
@misc{poliastro,
author = {Cano Rodr{\'i}guez, Juan Luis and others},
title = {poliastro: Astrodynamics in {Python}},
year = 2015,
howpublished = {Zenodo},
doi = {10.5281/zenodo.593610}
}
@misc{url:ACOVEA,
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year = 2002
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year = 2004,
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key = {IEEE CEC},
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booktitle = {Proceedings of the 2007 Congress on Evolutionary Computation (CEC 2007)},
year = 2007,
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key = {IEEE CEC},
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booktitle = {Proceedings of the 2008 Congress on Evolutionary Computation (CEC 2008)},
year = 2008,
publisher = {IEEE Press},
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key = {IEEE CEC},
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key = {IEEE CEC},
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title = {Proceedings of the 2010 Congress on Evolutionary
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booktitle = {Proceedings of the 2010 Congress on Evolutionary Computation (CEC 2010)},
publisher = {IEEE Press},
address = {Piscataway, NJ},
year = 2010
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key = {IEEE CEC},
title = {Proceedings of the 2011 Congress on Evolutionary
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booktitle = {Proceedings of the 2011 Congress on Evolutionary Computation (CEC 2011)},
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year = 2011
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key = {IEEE CEC},
title = {Proceedings of the 2012 Congress on Evolutionary
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address = {Piscataway, NJ},
year = 2012
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key = {IEEE CEC},
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address = {Piscataway, NJ},
year = 2013
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key = {IEEE CEC},
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year = 2014
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key = {IEEE CEC},
title = {Proceedings of the 2015 Congress on Evolutionary
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address = {Piscataway, NJ},
year = 2015
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key = {IEEE CEC},
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booktitle = {Proceedings of the 2016 Congress on Evolutionary Computation (CEC 2016)},
publisher = {IEEE Press},
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isbn = {978-1-5090-0623-6},
year = 2016
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key = {IEEE CEC},
title = {Proceedings of the 2017 Congress on Evolutionary
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booktitle = {Proceedings of the 2017 Congress on Evolutionary Computation (CEC 2017)},
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address = {Piscataway, NJ},
year = 2017
}
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key = {IEEE CEC},
title = {Proceedings of the 2018 Congress on Evolutionary
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booktitle = {Proceedings of the 2018 Congress on Evolutionary Computation (CEC 2018)},
publisher = {IEEE Press},
address = {Piscataway, NJ},
year = 2018
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key = {IEEE CEC},
title = {Proceedings of the 2019 Congress on Evolutionary
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year = 2019
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year = 2020
}
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key = {IEEE CEC},
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publisher = {IEEE Press},
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year = 2021
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booktitle = { Evolutionary Multi-criterion Optimization, EMO 2001}
}
@book{EMO2003,
title = {Evolutionary Multi-Criterion Optimization, Second
International Conference, EMO 2003, Faro, Portugal,
April 2003: proceedings},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2003},
year = 2003,
editor = { Carlos M. Fonseca and Peter J. Fleming and Eckart Zitzler and Kalyanmoy Deb and Lothar Thiele },
series = {Lecture Notes in Computer Science},
volume = 2632,
publisher = {Springer},
address = { Heidelberg, Germany}
}
@book{EMO2005,
title = {Evolutionary Multi-Criterion Optimization, Third
International Conference, EMO 2005, Guanajuato, Mexico, March
9-11, 2005. Proceedings},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2005},
year = 2005,
editor = { Carlos A. {Coello Coello} and Hern{\'a}ndez Aguirre, Arturo and Eckart Zitzler },
series = {Lecture Notes in Computer Science},
volume = 3410,
publisher = {Springer},
address = {Berlin\slash Heidelberg}
}
@book{EMO2007,
title = {Evolutionary Multi-Criterion Optimization, 4th International
Conference, {EMO} 2007, Matsushima, Japan, March 5-8, 2007,
Proceedings},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2007},
year = 2007,
editor = {S. Obayashi and others},
fulleditor = {Obayashi, Shigeru and Kalyanmoy Deb and Poloni, Carlo and Hiroyasu, Tomoyuki and Murata, Tadahiko},
volume = 4403,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
address = { Heidelberg, Germany}
}
@book{EMO2009,
title = {Evolutionary Multi-Criterion Optimization. 5th International
Conference, EMO 2009},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2009},
editor = { Matthias Ehrgott and Carlos M. Fonseca and Xavier Gandibleux and Jin-Kao Hao and Marc Sevaux },
volume = 5467,
series = {Lecture Notes in Computer Science},
year = 2009,
publisher = {Springer},
address = { Heidelberg, Germany}
}
@book{EMO2011,
editor = { Takahashi, R. H. C. and Kalyanmoy Deb and Wanner, Elizabeth F. and Salvatore Greco },
title = {Evolutionary Multi-Criterion Optimization. 6th International
Conference, EMO 2011, Ouro Preto, Brazil, April 5-8, 2011,
Proceedings},
publisher = {Springer},
year = 2011,
volume = 6576,
series = {Lecture Notes in Computer Science},
address = {Berlin\slash Heidelberg},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2011}
}
@book{EMO2013,
editor = { Robin C. Purshouse and Peter J. Fleming and Carlos M. Fonseca and Salvatore Greco and Jane Shaw},
title = {Evolutionary Multi-Criterion Optimization -- 7th
International Conference, EMO 2013, Sheffield, UK, March
19-22, 2013. Proceedings},
publisher = {Springer},
year = 2013,
volume = 7811,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2013},
isbn = {978-3-642-37139-4}
}
@book{EMO2015_1,
editor = { Ant{\'o}nio Gaspar{-}Cunha and Carlos Henggeler Antunes and Carlos A. {Coello Coello} },
title = {Evolutionary Multi-Criterion Optimization -- 8th
International Conference, EMO 2015, Guimar{\~{a}}es,
Portugal, March 29 -- April 1, 2015. Proceedings, Part {I}},
publisher = {Springer},
year = 2015,
volume = 9018,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2015 Part {I}}
}
@book{EMO2015_2,
title = {Evolutionary Multi-Criterion Optimization -- 8th
International Conference, EMO 2015, Guimar{\~{a}}es,
Portugal, March 29 -- April 1, 2015. Proceedings, Part {II}
},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2015 Part {II}},
publisher = {Springer},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
year = 2015,
volume = 9019,
editor = { Ant{\'o}nio Gaspar{-}Cunha and Carlos Henggeler Antunes and Carlos A. {Coello Coello} }
}
@book{EMO2017,
title = {Evolutionary Multi-Criterion Optimization -- 9th
International Conference, EMO 2017, M{\"u}nster, Germany,
March 19 - 22, 2017. Proceedings},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2017},
publisher = {Springer International Publishing},
address = { Cham, Switzerland},
series = {Lecture Notes in Computer Science},
volume = 10173,
year = 2017,
editor = {Heike Trautmann and G{\"{u}}nter Rudolph and Kathrin Klamroth
and Oliver Sch{\"{u}}tze and Margaret M. Wiecek and Yaochu
Jin and Christian Grimme}
}
@book{EMO2019,
editor = { Kalyanmoy Deb and Erik D. Goodman and Carlos A. {Coello Coello} and Kathrin
Klamroth and Kaisa Miettinen and Sanaz Mostaghim and Patrick
Reed},
title = {Evolutionary Multi-Criterion Optimization -- 10th
International Conference, {EMO} 2019, East Lansing, MI, USA,
March 10-13, 2019, Proceedings},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2019},
series = {Lecture Notes in Computer Science},
volume = 11411,
publisher = {Springer International Publishing},
address = { Cham, Switzerland},
year = 2019,
doi = {10.1007/978-3-030-12598-1},
isbn = {978-3-030-12597-4}
}
@book{EMO2023,
editor = { Emmerich, Michael T. M. and others},
title = {Evolutionary Multi-Criterion Optimization -- 12th
International Conference, EMO 2023, Leiden, The Netherlands,
March 20-24, 2023, Proceedings},
year = 2023,
publisher = {Springer International Publishing},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2023},
volume = 13970,
series = {Lecture Notes in Computer Science},
address = { Cham, Switzerland}
}
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title = {Encyclopedia of Parallel Computing},
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title = {Wiley Encyclopedia of Operations Research and Management
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booktitle = {Wiley Encyclopedia of Operations Research and
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editor = {V. William Porto and N. Saravanan and Donald E. Waagen and Agoston E. Eiben },
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key = {ESANN},
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year = 2014,
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@proceedings{ESANN2015,
key = {ESANN},
title = {Proceedings of 23rd European Symposium on Artificial Neural
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@proceedings{EUME2009,
title = {Proceedings of the EU/MEeting 2009: Debating the future: new
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@book{EUROGEN2001,
editor = {K. C. Giannakoglou and D. T. Tsahalis and J. Periaux and
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title = {Genetic Programming, 15th European Conference on Genetic
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volume = 7244,
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@book{EUROGP2017,
editor = {James McDermott and Mauro Castelli and Luk{\'{a}}s Sekanina
and Evert Haasdijk and Pablo Garc{\'i}a-S{\'a}nchez },
booktitle = {Proceedings of the 20th European Conference on Genetic Programming, EuroGP 2017},
title = {Genetic Programming, 20th European Conference, EuroGP 2017,
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@book{EUROGP2022,
title = {Genetic Programming, 25th European Conference, EuroGP 2022,
Held as Part of EvoStar 2022, Madrid, Spain, April 20-22,
2022, Proceedings},
booktitle = {Proceedings of the 25th European Conference on Genetic Programming, EuroGP 2022},
editor = {Eric Medvet and Gisele Pappa and Bing Xue},
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year = 2022,
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@book{EVOAPP2010,
editor = {Cecilia Di Chio and Stefano Cagnoni and Carlos Cotta and Marc
Ebner and Anik{\'o} Ek{\'a}rt and Anna I. Esparcia{-}Alc{\'{a}}zar and Chi Keong Goh and Juan-Juli{\'a}n Merelo and Ferrante Neri and Mike Preuss and Julian Togelius and Georgios N. Yannakakis},
title = {Applications of Evolutionary Computation, EvoApplicatons
2010: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM,
and EvoSTOC, Istanbul, Turkey, April 7-9, 2010, Proceedings,
Part I},
booktitle = {Applications of Evolutionary Computation},
publisher = {Springer},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 6024,
year = 2010,
doi = {10.1007/978-3-642-12239-2}
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editor = {Di Chio, Cecillia and others},
title = {EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN,
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and EvoSTOC, Málaga, Spain, April 11-13, 2012, Proceedings},
booktitle = {Applications of Evolutionary Computation},
publisher = {Springer},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 7248,
year = 2012
}
@book{EVOAPP2014,
editor = { Anna I. Esparcia{-}Alc{\'{a}}zar and Antonio M. Mora},
title = {17th European Conference, EvoApplications 2014, Granada,
Spain, April 23-25, 2014, Revised Selected Papers},
booktitle = {Applications of Evolutionary Computation},
publisher = {Springer},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 8602,
year = 2014
}
@book{EVOAPP2015,
editor = {Antonio M. Mora and Squillero, Giovanni},
title = {Applications of Evolutionary Computation - 18th European
Conference, EvoApplications 2015, Copenhagen, Denmark, April
8 -- 10, 2015, Proceedings},
booktitle = {Applications of Evolutionary Computation},
publisher = {Springer},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 9028,
year = 2015
}
@book{EVOAPP2016_1,
editor = {Squillero, Giovanni and Burelli, Paolo},
title = {Applications of Evolutionary Computation: 19th European
Conference, EvoApplications 2016, Porto, Portugal, March 30
-- April 1, 2016, Proceedings, Part I},
year = 2016,
booktitle = {Applications of Evolutionary Computation},
publisher = {Springer},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 9597,
doi = {10.1007/978-3-319-31204-0}
}
@book{EVOAPP2017_1,
editor = {Squillero, Giovanni and Sim, Kevin},
title = {Applications of Evolutionary Computation: 20th European
Conference, EvoApplications 2017, Amsterdam, The Netherlands,
April 19-21, 2017, Proceedings, Part I},
publisher = {Springer},
year = 2017,
volume = 10199,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
booktitle = {Applications of Evolutionary Computation},
doi = {10.1007/978-3-319-55849-3}
}
@book{EVOAPP2021,
editor = {Pedro Castillo and Jim{\'e}nez Laredo, Juan Luis },
title = {Applications of Evolutionary Computation -- 24th
International Conference, EvoApplications 2021, Held as Part
of EvoStar 2021, Virtual Event, April 7-9, 2021, Proceedings},
year = 2021,
booktitle = {Applications of Evolutionary Computation},
publisher = {Springer},
address = { Cham, Switzerland},
series = {Lecture Notes in Computer Science},
volume = {12694}
}
@book{EVOAPP2022,
editor = { Jim{\'e}nez Laredo, Juan Luis and others},
title = {Applications of Evolutionary Computation -- 25th European
Conference, EvoApplications 2022, Held as Part of EvoStar
2022, Madrid, Spain, April 20-22, 2022, Proceedings},
year = 2022,
publisher = {Springer Nature},
booktitle = {EvoApplications 2022: Applications of Evolutionary Computation},
volume = 13224,
series = {Lecture Notes in Computer Science},
address = {Switzerland},
fulleditor = { Jim{\'e}nez Laredo, Juan Luis and Hidalgo Perez, J. Ignacio and Oluwatoyin Babaagba, Kehinde}
}
@book{EVOAPP2023,
editor = {Correia, Jo\~{a}o and Smith, Stephen and Qaddoura, Raneem},
title = {Applications of Evolutionary Computation -- 26th European
Conference, EvoApplications 2023, Held as Part of EvoStar
2023, Brno, Czech Republic, April 12-14, 2023, Proceedings},
year = 2023,
booktitle = {EvoApplications 2023: Applications of Evolutionary Computation},
publisher = {Springer Nature},
address = {Switzerland},
series = {Lecture Notes in Computer Science},
volume = 13989
}
@book{EVOCOP2003,
title = {Proceedings of EvoCOP 2003 -- 3rd European Conference on Evolutionary Computation in Combinatorial Optimization },
booktitle = {Proceedings of EvoCOP 2003 -- 3rd European Conference on Evolutionary Computation in Combinatorial Optimization },
volume = 2611,
editor = { G{\"u}nther R. Raidl and Gottlieb, Jens},
year = 2003,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
address = { Heidelberg, Germany}
}
@book{EVOCOP2004,
editor = {Gottlieb, Jens and G{\"u}nther R. Raidl },
title = {Proceedings of EvoCOP 2004 -- 4th European Conference on Evolutionary Computation in Combinatorial Optimization },
publisher = {Springer},
year = 2004,
volume = 3004,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
booktitle = {Proceedings of EvoCOP 2004 -- 4th European Conference on Evolutionary Computation in Combinatorial Optimization }
}
@book{EVOCOP2005,
title = {Proceedings of EvoCOP 2005 -- 5th European Conference on Evolutionary Computation in Combinatorial Optimization },
booktitle = {Proceedings of EvoCOP 2005 -- 5th European Conference on Evolutionary Computation in Combinatorial Optimization },
volume = 3448,
editor = { G{\"u}nther R. Raidl and Gottlieb, Jens},
year = 2005,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
address = { Heidelberg, Germany}
}
@book{EVOCOP2006,
title = {Proceedings of EvoCOP 2006 -- 6th European Conference on Evolutionary Computation in Combinatorial Optimization },
booktitle = {Proceedings of EvoCOP 2006 -- 6th European Conference on Evolutionary Computation in Combinatorial Optimization },
volume = 3906,
editor = {Gottlieb, Jens and G{\"u}nther R. Raidl },
year = 2006,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
address = { Heidelberg, Germany}
}
@book{EVOCOP2007,
title = {Proceedings of EvoCOP 2007 -- Seventh European Conference on
Evolutionary Computation in Combinatorial Optimisation},
booktitle = {Proceedings of EvoCOP 2007 -- Seventh European Conference on
Evolutionary Computation in Combinatorial Optimisation},
editor = { Carlos Cotta and others},
year = 2007,
volume = 4446,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
address = { Berlin, Germany}
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@book{EVOCOP2009,
title = {Proceedings of EvoCOP 2009 -- 9th European Conference on Evolutionary Computation in Combinatorial Optimization },
booktitle = {Proceedings of EvoCOP 2009 -- 9th European Conference on Evolutionary Computation in Combinatorial Optimization },
editor = { Carlos Cotta and P. Cowling},
year = 2009,
volume = 5482,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
address = { Heidelberg, Germany}
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@book{EVOCOP2011,
title = {Proceedings of EvoCOP 2011 -- 11th European Conference on Evolutionary Computation in Combinatorial Optimization },
booktitle = {Proceedings of EvoCOP 2011 -- 11th European Conference on Evolutionary Computation in Combinatorial Optimization },
editor = { Peter Merz and Jin-Kao Hao },
year = 2011,
volume = 6622,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
address = { Heidelberg, Germany}
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title = {Evolutionary Computation in Combinatorial Optimization --
12th European Conference, EvoCOP 2012, M{\'a}laga, Spain,
April 11-13, 2012, Proceedings},
booktitle = {Proceedings of EvoCOP 2012 -- 12th European Conference on Evolutionary Computation in Combinatorial Optimization },
editor = { Jin-Kao Hao and Martin Middendorf },
year = 2012,
volume = 7245,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
address = { Heidelberg, Germany}
}
@book{EVOCOP2013,
editor = { Martin Middendorf and Christian Blum },
title = {Evolutionary Computation in Combinatorial
Optimization -- 13th European Conference, EvoCOP
2013, Vienna, Austria, April 3-5, 2013, Proceedings},
booktitle = {Proceedings of EvoCOP 2013 -- 13th European Conference on Evolutionary Computation in Combinatorial Optimization },
volume = 7832,
year = 2013,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
address = { Heidelberg, Germany}
}
@book{EVOCOP2014,
editor = { Christian Blum and Gabriela Ochoa },
title = {Evolutionary Computation in Combinatorial Optimization -- 14th
European Conference, EvoCOP 2014, Granada, Spain, April
24-25, 2014, Proceedings},
booktitle = {Proceedings of EvoCOP 2014 -- 14th European Conference on Evolutionary Computation in Combinatorial Optimization },
year = 2014,
series = {Lecture Notes in Computer Science},
volume = 8600,
publisher = {Springer},
address = { Heidelberg, Germany}
}
@book{EVOCOP2017,
editor = { Bin Hu and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Evolutionary Computation in Combinatorial Optimization -- 17th
European Conference, EvoCOP 2017, Amsterdam, The Netherlands,
April 19-21, 2017, Proceedings},
booktitle = {Proceedings of EvoCOP 2017 -- 17th European Conference on Evolutionary Computation in Combinatorial Optimization },
year = 2017,
series = {Lecture Notes in Computer Science},
volume = 10197,
doi = {10.1007/978-3-319-55453-2},
publisher = {Springer},
address = { Heidelberg, Germany}
}
@book{EVOCOP2018,
editor = { Arnaud Liefooghe and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Evolutionary Computation in Combinatorial Optimization --
18th European Conference, EvoCOP 2018, Parma, Italy, April
4-6, 2018, Proceedings},
booktitle = {Proceedings of EvoCOP 2018 -- 18th European Conference on Evolutionary Computation in Combinatorial Optimization },
year = 2018,
series = {Lecture Notes in Computer Science},
volume = 10782,
doi = {10.1007/978-3-319-77449-7},
publisher = {Springer},
address = { Heidelberg, Germany}
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@book{EVOCOP2021,
editor = { Christine Zarges and Verel, S{\'e}bastien },
title = {Evolutionary Computation in Combinatorial Optimization --
21st European Conference, EvoCOP 2021, Held as Part of
EvoStar 2021, Virtual Event, April 7-9, 2021, Proceedings },
booktitle = {Proceedings of EvoCOP 2021 -- 21th European Conference on Evolutionary Computation in Combinatorial Optimization },
year = 2021,
series = {Lecture Notes in Computer Science},
volume = 12692,
publisher = {Springer},
address = { Cham, Switzerland}
}
@book{EVOCOP2022,
editor = { P{\'e}rez C{\'a}ceres, Leslie and Verel, S{\'e}bastien },
title = {Evolutionary Computation in Combinatorial Optimization --
22nd European Conference, EvoCOP 2022, Held as Part of
EvoStar 2022, April 20-22, 2022, Proceedings},
booktitle = {Proceedings of EvoCOP 2022 -- 22nd European Conference on Evolutionary Computation in Combinatorial Optimization },
year = 2022,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
address = { Cham, Switzerland}
}
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author = { Emmerich, Michael T. M. and Andr{\'{e}} Deutz and Oliver Sch{\"u}tze and Legrand,
Pierrick and Tantar, Emilia and Tantar,
Alexandru-Adrian},
title = {{EVOLVE} - A Bridge between Probability, Set Oriented
Numerics, and Evolutionary Computation {VII}},
publisher = {Springer},
year = 2017,
volume = 662,
series = {Studies in Computational Intelligence},
address = { Cham, Switzerland},
booktitle = {{EVOLVE} - A Bridge between Probability, Set Oriented
Numerics, and Evolutionary Computation {VII}}
}
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booktitle = {Evolutionary Programming VII},
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editor = {V. W. Porto and N. Saravanan and D. Waagen and Agoston E. Eiben },
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year = 1998,
doi = {10.1007/BFb0040753}
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booktitle = {Trends in Multiple Criteria Decision Analysis},
title = {Trends in Multiple Criteria Decision Analysis},
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volume = 142,
year = 2010
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editor = {Roman Bart{\'{a}}k and Keith W. Brawner},
title = {Proceedings of the Thirty-Second International Florida
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Sarasota, Florida, USA, May 19-22 2019},
booktitle = {Proceedings of the Thirty-Second International Florida Artificial
Intelligence Research Society Conference},
publisher = {{AAAI} Press},
year = 2019
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editor = {Jason Baumgartner and Mary Sheeran},
title = {{FMCAD'07}: Proceedings of the 7th International Conference
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Formal Methods in Computer Aided Design},
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address = {Austin, Texas, USA}
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editor = {Avrim Blum},
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editor = {G. Rawlins},
title = {Foundations of Genetic Algorithms},
booktitle = {Foundations of Genetic Algorithms (FOGA)},
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year = 1991
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@book{FOGA1992,
editor = { Darrell Whitley },
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booktitle = {Foundations of Genetic Algorithms (FOGA)},
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booktitle = {Foundations of Genetic Algorithms (FOGA)},
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year = 1996,
title = {Proceedings of the 4th Workshop on Foundations of Genetic
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booktitle = {Proceedings of the Seventh Workshop on Foundations of Genetic Algorithms (FOGA)},
year = 2002,
editor = { De Jong, Kenneth A. and Poli, Riccardo and Rowe, Jonathan E.},
title = {Foundations of Genetic Algorithms, 7th International Workshop,
{FOGA} 2002, Torremolinos, Spain, September 2-4, 2002, Proceedings},
publisher = {Morgan Kaufmann Publishers}
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booktitle = {Proceedings of the Tenth ACM SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA)},
year = 2009,
editor = {Ivan I. Garibay and Thomas Jansen and R. Paul Wiegand and
Annie S. Wu},
title = {Foundations of Genetic Algorithms, 10th {ACM} {SIGEVO}
International Workshop, {FOGA} 2009, Orlando, Florida, USA,
January 9-11, 2009, Proceedings},
publisher = {{ACM}},
isbn = {978-1-60558-414-0}
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booktitle = {Proceedings of the 15th {ACM}/{SIGEVO} Conference on Foundations of Genetic Algorithms},
year = 2019,
editor = { Tobias Friedrich and Carola Doerr and Arnold, Dirk V.},
title = {Foundations of Genetic Algorithms, 15th {ACM}/{SIGEVO}
International Workshop, {FOGA} 2019, Potsdam, Germany},
publisher = {{ACM}}
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booktitle = {Proceedings of the 17th {ACM}/{SIGEVO} Conference on Foundations of Genetic Algorithms},
year = 2023,
editor = { Chicano, Francisco and Tobias Friedrich and K{\"o}tzing, Timo and Franz Rothlauf },
title = {Foundations of Genetic Algorithms, 17th {ACM}/{SIGEVO}
International Workshop, {FOGA} 2023, Potsdam, Germany},
publisher = {{ACM}}
}
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}
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}
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editor = {Wolfgang Banzhaf and Jason M. Daida and A. E. Eiben
and Max H. Garzon and Vasant Honavar and Mark
J. Jakiela and Robert E. Smith},
shorteditor = {Wolfgang Banzhaf and others},
title = {Proceedings of the Genetic and Evolutionary
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booktitle = {Proceedings of the Genetic and Evolutionary
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year = 1999,
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}
@book{GECCO2000,
title = {Proceedings of the Genetic and Evolutionary Computation
Conference, GECCO 2000},
booktitle = {Proceedings of the Genetic and Evolutionary Computation
Conference, GECCO 2000},
year = 2000,
fulleditor = { Darrell Whitley and David E. Goldberg and E. Cantu-Paz and L. Spector and
I. Parmee and Hans-Georg Beyer },
editor = { Darrell Whitley and others},
publisher = {Morgan Kaufmann Publishers, San Francisco, CA}
}
@book{GECCO2001,
title = {Proceedings of the 3rd Annual Conference on Genetic and
Evolutionary Computation, GECCO 2001},
booktitle = {Proceedings of the 3rd Annual Conference on Genetic and
Evolutionary Computation, GECCO 2001},
year = 2001,
editor = {Erik D. Goodman},
publisher = {Morgan Kaufmann Publishers, San Francisco, CA}
}
@book{GECCO2002,
title = {Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2002},
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2002},
year = 2002,
editor = { Langdon, William B. and others},
publisher = {Morgan Kaufmann Publishers, San Francisco, CA}
}
@book{GECCO2003_1,
title = {Proceedings of the Genetic and Evolutionary Computation
Conference, GECCO 2003, Part I},
booktitle = {Proceedings of the Genetic and Evolutionary Computation
Conference, GECCO 2003, Part I},
year = 2003,
editor = {E. Cant\'u-Paz and others},
volume = 2723,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
address = { Heidelberg, Germany}
}
@book{GECCO2004_1,
title = {Genetic and Evolutionary Computation Conference,
GECCO 2004, Seattle, WA, USA, June 26-30, 2004,
Proceedings, Part I},
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2004, Part I},
year = 2004,
editor = { Kalyanmoy Deb and others},
volume = 3102,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
address = { Heidelberg, Germany}
}
@book{GECCO2004_2,
title = {Genetic and Evolutionary Computation Conference,
GECCO 2004, Seattle, WA, USA, June 26-30, 2004,
Proceedings, Part II},
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2004, Part II},
year = 2004,
editor = { Kalyanmoy Deb and others},
volume = 3103,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
address = { Heidelberg, Germany}
}
@book{GECCO2005,
title = {Proceedings of the Genetic and Evolutionary Computation
Conference, GECCO 2005},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2005},
editor = { Hans-Georg Beyer and Una-May O'Reilly },
year = 2005,
publisher = {ACM Press},
address = { New York, NY}
}
@book{GECCO2006,
title = {Proceedings of the Genetic and Evolutionary Computation
Conference, GECCO 2006},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2006},
editor = {M. Cattolico and others},
year = 2006,
publisher = {ACM Press},
address = { New York, NY}
}
@book{GECCO2007,
title = {Genetic and Evolutionary Computation Conference, {GECCO}
2007, Proceedings, London, England, UK, July 7-11, 2007},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007},
editor = {Dirk Thierens and others},
year = 2007,
publisher = {ACM Press},
address = { New York, NY}
}
@book{GECCO2008,
title = {Genetic and Evolutionary Computation Conference,
GECCO 2008, Proceedings, Atlanta, Georgia, USA
July 12-16, 2008},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2008},
editor = {Conor Ryan},
year = 2008,
publisher = {ACM Press},
address = { New York, NY}
}
@book{GECCO2009,
editor = { Franz Rothlauf },
title = {Genetic and Evolutionary Computation Conference, GECCO 2009,
Proceedings, Montreal, Qu{\'e}bec, Canada, July 8-12, 2009},
publisher = {ACM Press},
year = 2009,
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2009}
}
@book{GECCO2009c,
editor = { Franz Rothlauf },
title = {Genetic and Evolutionary Computation Conference, GECCO 2009,
Proceedings, Montreal, Qu{\'e}bec, Canada, July 8-12, 2009,
Companion Material},
publisher = {ACM Press},
year = 2009,
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2009}
}
@book{GECCO2010,
editor = {Martin Pelikan and J{\"u}rgen Branke },
title = {Genetic and Evolutionary Computation Conference,
GECCO 2010, Proceedings, Portland, Oregon, USA, July
7-11, 2010},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2010},
year = 2010,
publisher = {ACM Press},
address = { New York, NY}
}
@book{GECCO2010c,
editor = {Martin Pelikan and J{\"u}rgen Branke },
title = {Genetic and Evolutionary Computation Conference, GECCO 2010,
Companion Material Proceedings, Portland, Oregon, USA, July
7-11, 2010},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2010},
year = 2010,
publisher = {ACM Press},
address = { New York, NY}
}
@book{GECCO2011,
title = {Genetic and Evolutionary Computation Conference,
GECCO 2011, Proceedings, Dublin, Ireland, July
12-16, 2011},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011},
editor = {Natalio Krasnogor and Pier Luca Lanzi},
year = 2011,
publisher = {ACM Press},
address = { New York, NY}
}
@book{GECCO2011c,
editor = {Natalio Krasnogor and Pier Luca Lanzi},
title = {13th Annual Genetic and Evolutionary Computation Conference,
GECCO 2011, Companion Material Proceedings, Dublin, Ireland,
July 12-16, 2011},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2011},
publisher = {ACM Press},
address = { New York, NY},
year = 2011
}
@book{GECCO2012,
title = {Genetic and Evolutionary Computation Conference,
GECCO 2012, Proceedings, Philadelphia, PA, USA, July
7-11, 2012},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2012},
editor = {Terence Soule and Jason H. Moore},
year = 2012,
publisher = {ACM Press},
address = { New York, NY}
}
@book{GECCO2012c,
title = {Genetic and Evolutionary Computation Conference, GECCO 2012,
Companion Material Proceedings, Philadelphia, PA, USA, July
7-11, 2012},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2012},
editor = {Terence Soule and Jason H. Moore},
year = 2012,
publisher = {ACM Press},
address = { New York, NY}
}
@book{GECCO2013,
title = {Genetic and Evolutionary Computation Conference, GECCO 2013,
Proceedings, Amsterdam, The Netherlands, July 6-10, 2013},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2013},
editor = { Christian Blum and Alba, Enrique },
year = 2013,
publisher = {ACM Press},
address = { New York, NY},
isbn = {978-1-4503-1963-8}
}
@book{GECCO2013c,
editor = { Christian Blum and Alba, Enrique },
title = {Genetic and Evolutionary Computation
Conference, GECCO 2013, Companion Material
Proceedings, Amsterdam, The Netherlands, July 6-10, 2013},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2013},
publisher = {ACM Press},
address = { New York, NY},
year = 2013
}
@book{GECCO2014,
title = {Genetic and Evolutionary Computation Conference,
GECCO 2014, Proceedings, Vancouver, BC, Canada,
July 12-16, 2014},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2014},
editor = {Christian Igel and Dirk V. Arnold},
year = 2014,
publisher = {ACM Press},
address = { New York, NY}
}
@book{GECCO2015,
title = {Genetic and Evolutionary Computation Conference,
GECCO 2015, Proceedings, Madrid, Spain,
July 11-15, 2015},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015},
editor = {Sara Silva and Anna I. Esparcia{-}Alc{\'{a}}zar },
year = 2015,
publisher = {ACM Press},
address = { New York, NY}
}
@book{GECCO2015c,
title = {Genetic and Evolutionary Computation Conference, {GECCO} 2015, Madrid,
Spain, July 11-15, 2015, Companion Material Proceedings},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2015},
editor = { Jim{\'e}nez Laredo, Juan Luis and Sara Silva and Anna I. Esparcia{-}Alc{\'{a}}zar },
year = 2015,
publisher = {ACM Press},
address = { New York, NY}
}
@book{GECCO2016,
title = {Genetic and Evolutionary Computation Conference,
GECCO 2016, Proceedings, Denver, CO, USA,
July 20-24, 2016},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2016},
editor = { Tobias Friedrich and Frank Neumann and Andrew M. Sutton },
year = 2016,
publisher = {ACM Press},
address = { New York, NY}
}
@book{GECCO2016c,
title = {Genetic and Evolutionary Computation Conference, {GECCO} 2016, Denver, CO, USA, July 20-24, 2016, Companion Material Proceedings},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2016},
editor = { Tobias Friedrich and Frank Neumann and Andrew M. Sutton },
year = 2016,
publisher = {ACM Press},
address = { New York, NY}
}
@book{GECCO2017,
title = {Genetic and Evolutionary Computation Conference,
GECCO 2017, Berlin, Germany, July 15-19, 2017},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017},
editor = { Peter A. N. Bosman },
year = 2017,
publisher = {ACM Press},
address = { New York, NY}
}
@book{GECCO2017c,
title = {Genetic and Evolutionary Computation Conference, {GECCO} 2017, Berlin,
Germany, July 15-19, 2017},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2017},
editor = { Peter A. N. Bosman },
year = 2017,
publisher = {ACM Press},
address = { New York, NY}
}
@book{GECCO2018,
title = {Genetic and Evolutionary Computation Conference, GECCO 2018,
Kyoto, Japan, July 15-19, 2018},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018},
editor = { Aguirre, Hern\'{a}n E. and Keiki Takadama},
doi = {10.1145/3205455},
year = 2018,
publisher = {ACM Press},
address = { New York, NY}
}
@book{GECCO2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
title = {Proceedings of the Genetic and Evolutionary Computation
Conference, {GECCO} 2019, Prague, Czech Republic, July 13-17,
2019},
year = 2019,
publisher = {ACM Press},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019},
address = { New York, NY},
isbn = {978-1-4503-6111-8},
doi = {10.1145/3321707}
}
@book{GECCO2019c,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
title = {Genetic and Evolutionary Computation Conference Companion,
{GECCO} 2019, Prague, Czech Republic, July 13-17, 2019},
year = 2019,
publisher = {ACM Press},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019},
address = { New York, NY},
isbn = {978-1-4503-6748-6},
doi = {10.1145/3319619}
}
@book{GECCO2020,
title = {Proceedings of the Genetic and Evolutionary Computation
Conference, {GECCO} 2020, Canc{\'u}n, Mexico, July 8-12,
2020},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2020},
editor = { Carlos A. {Coello Coello} },
year = 2020,
publisher = {ACM Press},
address = { New York, NY},
isbn = {978-1-4503-7128-5},
doi = {10.1145/3377930},
location = {Canc{\'u}n, Mexico},
epub = {https://dl.acm.org/citation.cfm?id=3377930}
}
@book{GECCO2021,
title = {Proceedings of the Genetic and Evolutionary Computation
Conference, {GECCO} 2021, Lille, France, July 10-14, 2021},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021},
editor = { Chicano, Francisco and Krzysztof Krawiec },
year = 2021,
publisher = {ACM Press},
address = { New York, NY},
location = {Lille, France},
doi = {10.1145/3449639.3459373}
}
@book{GECCO2021c,
editor = { Chicano, Francisco and Krzysztof Krawiec },
title = {Genetic and Evolutionary Computation Conference Companion,
{GECCO} 2021, Lille, France, July 10-14, 2021},
publisher = {ACM Press},
year = 2021,
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2021}
}
@book{GECCO2022,
editor = { Jonathan E. Fieldsend and Markus Wagner },
title = {Proceedings of the Genetic and Evolutionary Computation
Conference, {GECCO} 2022, Boston, Massachusetts, July 9-13,
2022},
publisher = {ACM Press},
year = 2022,
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2022},
location = {Boston, Massachusetts},
doi = {10.1145/3512290}
}
@book{GECCO2022c,
editor = { Jonathan E. Fieldsend and Markus Wagner },
title = {Genetic and Evolutionary Computation Conference Companion,
{GECCO} 2022, Boston, Massachusetts, July 9-13, 2022},
publisher = {ACM Press},
year = 2022,
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2022},
location = {Boston, Massachusetts},
doi = {10.1145/3520304},
isbn = 9781450392686
}
@book{GECCO2023,
editor = {Silva, Sara and Lu{\'i}s Paquete },
title = {Proceedings of the Genetic and Evolutionary Computation
Conference, {GECCO} 2023, Lisbon, Portugal, July 15-19, 2023},
publisher = {ACM Press},
year = 2023,
address = { New York, NY},
annote = {ISBN: 9798400701191},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2023},
location = {Lisbon, Portugal},
doi = {10.1145/3583131}
}
@book{GECCO2023c,
editor = {Silva, Sara and Lu{\'i}s Paquete },
title = {Genetic and Evolutionary Computation Conference Companion,
{GECCO} 2023, Lisbon, Portugal, July 15-19, 2023},
publisher = {ACM Press},
year = 2023,
address = { New York, NY},
annote = {ISBN: 979-8-4007-0120-7},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2023},
location = {Lisbon, Portugal},
doi = {10.1145/3583133}
}
@book{GECCO2024,
editor = { Julia Handl and Li, Xiaodong },
title = {Proceedings of the Genetic and Evolutionary Computation
Conference, {GECCO} 2024, Melbourne, Australia, July 14-18,
2024},
year = 2024,
publisher = {ACM Press},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2024},
address = { New York, NY},
location = {Melbourne, Australia}
}
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address = { Heidelberg, Germany}
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address = { Cham, Switzerland}
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editor = { Roberto Battiti and Dmitri E. Kvasov and Yaroslav D. Sergeyev},
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address = { Cham, Switzerland}
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June 10-15, 2018},
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2012, Proceedings, Part {I}},
editor = { Carlos A. {Coello Coello} and others},
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booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XII}, Part {II}},
title = {Parallel Problem Solving from Nature - {PPSN} {XII} - 12th
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2012, Proceedings, Part {II}},
editor = { Carlos A. {Coello Coello} and others},
fulleditor = { Carlos A. {Coello Coello} and Vincenzo Cutello and Kalyanmoy Deb and Stephanie
Forrest and Giuseppe Nicosia and Mario Pavone},
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booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XIII}},
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booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XIV}},
title = {Parallel Problem Solving from Nature - PPSN XIV 14th
International Conference, Edinburgh, UK, September 17-21,
2016, Proceedings},
editor = { Julia Handl and Emma Hart and Lewis, P. R. and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Gabriela Ochoa and Ben Paechter },
series = {Lecture Notes in Computer Science},
publisher = {Springer},
address = { Heidelberg, Germany},
volume = 9921,
year = 2016,
doi = {10.1007/978-3-319-45823-6},
isbn = {978-3-319-45822-9}
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booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XV}},
title = {Parallel Problem Solving from Nature - PPSN XV 15th
International Conference, Coimbra, Portugal, September 8-12,
2018, Proceedings, Part {I}},
editor = { Anne Auger and Carlos M. Fonseca and Louren{\c c}o, N. and Penousal Machado and Lu{\'i}s Paquete and Darrell Whitley },
series = {Lecture Notes in Computer Science},
publisher = {Springer},
address = { Cham, Switzerland},
year = 2018,
volume = 11101
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booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XV}},
title = {Parallel Problem Solving from Nature - PPSN XV 15th
International Conference, Coimbra, Portugal, September 8-12,
2018, Proceedings, Part {II}},
editor = { Anne Auger and Carlos M. Fonseca and Louren{\c c}o, N. and Penousal Machado and Lu{\'i}s Paquete and Darrell Whitley },
series = {Lecture Notes in Computer Science},
publisher = {Springer},
address = { Cham, Switzerland},
year = 2018,
volume = 11102
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booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XVI}},
title = {Parallel Problem Solving from Nature - PPSN XVI 16th
International Conference, Leiden, The Netherlands, September
5-9, 2020, Proceedings},
editor = { Thomas B{\"a}ck and Mike Preuss and Deutz, Andr{\'e} and Wang, Hao and Carola Doerr and Emmerich, Michael T. M. and Heike Trautmann },
series = {Lecture Notes in Computer Science},
publisher = {Springer},
address = { Cham, Switzerland},
year = 2020,
volume = 12269
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editor = { G{\"u}nther Rudolph and Anna V. Kononova and Aguirre, Hern\'{a}n E. and Pascal Kerschke and Gabriela Ochoa and Tea Tu{\v s}ar },
title = {Parallel Problem Solving from Nature - PPSN XVII, 17th
International Conference, PPSN 2022, Dortmund, Germany,
September 10-14, 2022, Proceedings, Part I},
year = 2022,
publisher = {Springer},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XVII}},
volume = 13398,
series = {Lecture Notes in Computer Science},
address = { Cham, Switzerland}
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editor = {Michael Affenzeller and Stephan M. Winkler and Anna
V. Kononova and Heike Trautmann and Tea Tu{\v s}ar and Penousal Machado and Thomas B{\"a}ck },
title = {Parallel Problem Solving from Nature - PPSN XVIII, 18th
International Conference, PPSN 2024, Hagenberg, Austria,
September 14-18, 2024, Proceedings, Part II},
year = 2024,
publisher = {Springer},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XVIII}},
volume = 15149,
series = {Lecture Notes in Computer Science},
address = { Cham, Switzerland}
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