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.
You should not need to edit the entries directly unless you find mistakes. See the README for more details.
@comment{{This file has been generated by bib2bib 1.99}}
@comment{{Command line: bib2bib --warn-error --expand --expand-xrefs authors.bib abbrev.bib journals.bib articles.bib biblio.bib crossref.bib --remove pdf --remove alias -ob /tmp/tmpNSRAThIplt.bib -oc /tmp/citefileBgDTtYStn7}}
@preamble{{\providecommand{\MaxMinAntSystem}{{$\cal MAX$--$\cal MIN$} {Ant} {System}} } # {\providecommand{\rpackage}[1]{{#1}} } # {\providecommand{\softwarepackage}[1]{{#1}} } # {\providecommand{\proglang}[1]{{#1}} }}
@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},
journal = {European Journal of Operational Research},
volume = 167,
number = 3,
pages = {717--738},
year = 2005,
keywords = {Multicriteria Scheduling}
}
@article{ArrLeu2017,
author = { Jos{\'e} Elias C. Arroyo and Joseph Y.-T. Leung },
title = {An Effective Iterated Greedy Algorithm for Scheduling Unrelated Parallel Batch Machines with Non-identical Capacities and Unequal Ready Times},
journal = {Computers and Industrial Engineering},
year = 2017,
volume = 105,
pages = {84--100}
}
@article{ArzCebIru2022jcgs,
author = {Arza, Etor and Josu Ceberio and Irurozki, Ekhine and P{\'e}rez,
Aritz},
title = {Comparing Two Samples Through Stochastic Dominance: A
Graphical Approach},
journal = {Journal of Computational and Graphical Statistics},
year = 2022,
pages = {1--38},
month = jun,
doi = {10.1080/10618600.2022.2084405}
}
@article{Asch01tsptw,
author = { N. Ascheuer and Matteo Fischetti and M. Gr{\"o}tschel },
title = {Solving asymmetric travelling salesman problem with
time windows by branch-and-cut},
journal = {Mathematical Programming},
year = 2001,
volume = 90,
pages = {475--506}
}
@article{AssWanFre2014hetero,
author = {John{-}Alexander M. Assael and Ziyu Wang and Nando de Freitas },
title = {Heteroscedastic Treed Bayesian Optimisation},
journal = {Arxiv preprint arXiv:1410.7172},
doi = {10.48550/arXiv.1410.7172},
year = 2014,
eprinttype = {arXiv},
eprint = {1410.7172},
keywords = {Treed-GP}
}
@article{Ata2003mik,
author = { Alper Atamt{\"u}rk },
title = {On the facets of the mixed--integer knapsack polyhedron},
journal = {Mathematical Programming},
year = 2003,
volume = 98,
number = 1,
pages = {145--175},
doi = {10.1007/s10107-003-0400-z}
}
@article{AudDanOrb2014,
author = { Charles Audet and Cong-Kien Dang and Dominique Orban },
title = {Optimization of Algorithms with {OPAL}},
journal = {Mathematical Programming Computation},
year = 2014,
volume = 6,
number = 3,
pages = {233--254}
}
@article{AudEgla1977,
title = {New approach to the design of multifactor experiments},
author = {Audze, P. and Egl{\~a}js, Vilnis},
journal = {Problems of Dynamics and Strengths},
year = 1977,
note = {(in Russian)},
pages = {104--107},
volume = 35,
publisher = {Zinatne Publishing House, Riga}
}
@article{AudOrb06:mads,
author = { Charles Audet and Dominique Orban },
title = {Finding Optimal Algorithmic Parameters Using Derivative-Free
Optimization},
journal = {SIAM Journal on Optimization},
year = 2006,
volume = 17,
number = 3,
pages = {642--664},
keywords = {mesh adaptive direct search; pattern search},
doi = {10.1137/0406208}
}
@article{Aue2002using,
title = {Using Confidence Bounds for Exploitation-Exploration
Trade-offs},
author = {Auer, Peter},
journal = {Journal of Machine Learning Research},
volume = 3,
month = nov,
pages = {397--422},
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 $O(T^{1/2})$.}
}
@article{AueCesFis2002finite,
title = {Finite-time analysis of the multiarmed bandit problem},
author = {Auer, Peter and Cesa-Bianchi, Nicolo and Fischer, Paul},
journal = {Machine Learning},
volume = 47,
number = {2-3},
pages = {235--256},
year = 2002
}
@article{AugBadBroZit2012tcs,
author = { Anne Auger and Johannes Bader and Dimo Brockhoff and Eckart Zitzler },
title = {Hypervolume-based multiobjective optimization:
Theoretical foundations and practical implications},
journal = {Theoretical Computer Science},
volume = 425,
year = 2012,
pages = {75--103},
doi = {10.1016/j.tcs.2011.03.012}
}
@article{AvcTop2017:cor,
author = {Mustafa Avci and Seyda Topaloglu},
title = {A Multi-start Iterated Local Search Algorithm for the Generalized Quadratic Multiple Knapsack Problem},
journal = {Computers \& Operations Research},
year = 2017,
volume = 83,
pages = {54--65}
}
@article{AvrAllLop2021arxiv,
author = { Andreea Avramescu and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Managing Manufacturing and Delivery of Personalised Medicine:
Current and Future Models},
year = 2021,
journal = {Arxiv preprint arXiv:2105.12699 [econ.GN]},
url = {https://arxiv.org/abs/2105.12699}
}
@article{AydYavStu2017:si,
author = { Do\v{g}an Ayd{\i}n and G{\"{u}}rcan Yavuz and Thomas St{\"u}tzle },
title = {{ABC-X:} A Generalized, Automatically Configurable Artificial Bee
Colony Framework},
journal = {Swarm Intelligence},
year = 2017,
volume = 11,
number = 1,
pages = {1--38}
}
@article{AyoAllLopPar2022scalarisation,
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},
journal = {Arxiv preprint arXiv:2210.11321},
year = 2022,
doi = {10.48550/arXiv.2210.11321}
}
@article{AziTay2014eaai,
author = {Mahdi Aziz and {Tayarani-N}, Mohammad-H.},
title = {An adaptive memetic Particle Swarm Optimization algorithm for finding large-scale Latin hypercube designs},
journal = {Engineering Applications of Artificial Intelligence},
volume = 36,
pages = {222--237},
year = 2014,
doi = {10.1016/j.engappai.2014.07.021},
keywords = {F-race}
}
@article{BacHelPic2020gaussian,
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.}
}
@article{BadZit2011ec,
author = { Johannes Bader and Eckart Zitzler },
title = {{HypE}: An Algorithm for Fast Hypervolume-Based
Many-Objective Optimization},
journal = {Evolutionary Computation},
volume = 19,
number = 1,
year = 2011,
pages = {45--76},
doi = {10.1162/EVCO_a_00009}
}
@article{BahComLau2019tre,
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.}
}
@article{Baker2016reprod,
title = {Is there a reproducibility crisis?},
author = {Monya Baker},
journal = {Nature},
volume = 533,
pages = {452--454},
year = 2016
}
@article{Baker83:tsptw,
author = {Edward K. Baker},
title = {An Exact Algorithm for the Time-Constrained
Traveling Salesman Problem},
volume = 31,
doi = {10.1287/opre.31.5.938},
number = 5,
journal = {Operations Research},
year = 1983,
pages = {938--945},
anote = {makespan optimization}
}
@article{BalBea2008,
title = {Facility location in humanitarian relief},
author = {Balcik, Burcu and Beamon, Benita M.},
journal = {International Journal of Logistics},
volume = 11,
number = 2,
pages = {101--121},
year = 2008,
publisher = {Taylor \& Francis}
}
@article{BalBirStuDor2009ejor,
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},
journal = {European Journal of Operational Research},
year = 2009,
volume = 199,
number = 1,
pages = {98--110}
}
@article{BalBirStuDor2010cor,
author = { Prasanna Balaprakash and Mauro Birattari and Thomas St{\"u}tzle and Marco Dorigo },
title = {Estimation-based Metaheuristics for the Probabilistic Travelling Salesman Problem},
journal = {Computers \& Operations Research},
year = 2010,
volume = 37,
number = 11,
pages = {1939--1951},
doi = {10.1016/j.cor.2009.12.005}
}
@article{BalBirStuDor2015coa,
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},
journal = {Computational Optimization and Applications},
year = 2015,
volume = 61,
number = 2,
pages = {463--487},
doi = {10.1007/s10589-014-9719-z}
}
@article{BalBirStuYuaDor09,
author = { Prasanna Balaprakash and Mauro Birattari and Thomas St{\"u}tzle and Zhi Yuan and Marco Dorigo },
title = {Estimation-based Ant Colony Optimization Algorithms
for the Probabilistic Travelling Salesman Problem},
journal = {Swarm Intelligence},
volume = 3,
number = 3,
year = 2009,
pages = {223--242}
}
@article{BalCar1996,
author = { Egon Balas and M. C. Carrera},
title = {A Dynamic Subgradient-based Branch and Bound
Procedure for Set Covering},
journal = {Operations Research},
year = 1996,
volume = 44,
number = 6,
pages = {875--890}
}
@article{BalMar1980,
author = { Egon Balas and C. Martin},
title = {Pivot and Complement--A Heuristic for 0--1 Programming},
journal = {Management Science},
year = 1980,
volume = 26,
number = 1,
pages = {86--96}
}
@article{BalPad1976,
author = { Egon Balas and M. W. Padberg},
title = {Set Partitioning: A Survey},
journal = {SIAM Review},
year = 1976,
volume = 18,
pages = {710--760}
}
@article{BalSim01tsptw,
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},
year = 2001,
keywords = {tsptw},
pages = {56--75}
}
@article{BalVaz1998,
author = { Egon Balas and A. Vazacopoulos},
title = {Guided Local Search with Shifting Bottleneck for Job
Shop Scheduling},
journal = {Management Science},
year = 1998,
volume = 44,
number = 2,
pages = {262--275}
}
@article{Bankes2002,
title = {Tools and techniques for developing policies for complex and
uncertain systems},
author = { Bankes, Steven C. },
volume = 99,
number = {suppl 3},
pages = {7263--7266},
year = 2002,
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},
doi = {10.1073/pnas.092081399}
}
@article{BarBatSenSil2015ijem,
title = {Improving the Performance of Metaheuristics: An Approach
Combining Response Surface Methodology and Racing Algorithms},
author = {Eduardo Batista de Moraes Barbosa and Edson Luiz
Franc{\c{c}}a Senne and Messias Borges Silva},
journal = {International Journal of Engineering Mathematics},
year = 2015,
volume = 2015,
pages = {Article ID 167031},
doi = {10.1155/2015/167031},
keywords = {F-race}
}
@article{BarDiaSerBen2020xai,
doi = {10.1016/j.inffus.2019.12.012},
year = 2020,
month = jun,
publisher = {Elsevier {BV}},
volume = 58,
pages = {82--115},
author = {Alejandro Barredo Arrieta and Natalia
D{\'{i}}az-Rodr{\'{i}}guez and Javier Del Ser and Adrien
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}
}
@article{BarDoeBer2020benchmarking,
title = {Benchmarking in Optimization: Best Practice and Open Issues},
author = { Thomas Bartz-Beielstein and Carola Doerr and Daan van den Berg and Jakob Bossek and Sowmya Chandrasekaran and Tome Eftimov and Andreas Fischbach and Pascal Kerschke and William {La
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},
year = 2020,
journal = {Arxiv preprint arXiv:2007.03488 [cs.NE]},
url = {https://arxiv.org/abs/2007.03488}
}
@article{BarGolKel1995,
author = {Richard S. Barr and Bruce L. Golden and James P. Kelly and Mauricio G. C. Resende and William R. Stewart, Jr.},
title = {Designing and Reporting on Computational Experiments with
Heuristic Methods},
journal = {Journal of Heuristics},
year = 1995,
volume = 1,
number = 1,
pages = {9--32},
doi = {10.1007/BF02430363}
}
@article{BarJohNem1998or,
title = {Branch-and-price: Column generation for solving huge integer
programs},
author = {Barnhart, Cynthia and Johnson, Ellis L. and Nemhauser, George
L. and Martin W. P. Savelsbergh and Vance, Pamela H.},
journal = {Operations Research},
volume = 46,
number = 3,
pages = {316--329},
year = 1998
}
@article{BarKwa2020,
title = {On considering robustness in the search phase of Robust
Decision Making: A comparison of Many-Objective Robust
Decision Making, multi-scenario Many-Objective Robust
Decision Making, and Many Objective Robust Optimization},
author = {Bartholomew, Erin and Kwakkel, Jan H.},
journal = {Environmental Modelling \& Software},
pages = 104699,
year = 2020,
publisher = {Elsevier},
volume = 127,
doi = {10.1016/j.envsoft.2020.104699}
}
@article{BarPea2016,
author = { Elias Bareinboim and Judea Pearl },
title = {Causal inference and the data-fusion problem},
volume = 113,
number = 27,
pages = {7345--7352},
year = 2016,
doi = {10.1073/pnas.1510507113},
journal = {Proceedings of the National Academy of Sciences}
}
@article{BarZae2017model,
author = { Thomas Bartz-Beielstein and Martin Zaefferer },
title = {Model-based methods for continuous and discrete global
optimization},
doi = {10.1016/j.asoc.2017.01.039},
year = 2017,
month = jun,
volume = 55,
pages = {154--167},
journal = {Applied Soft Computing}
}
@article{BasFra1990,
author = {Basu, Atanu and Frazer, L. Neil},
title = {Rapid Determination of the Critical Temperature in Simulated Annealing Inversion},
journal = {Science},
year = 1990,
volume = 249,
number = 4975,
pages = {1409--1412}
}
@article{BatPas2010tec,
author = { Roberto Battiti and Andrea Passerini },
title = {Brain-Computer Evolutionary Multiobjective Optimization: A
Genetic Algorithm Adapting to the Decision Maker},
journal = {IEEE Transactions on Evolutionary Computation},
volume = 14,
number = 5,
year = 2010,
pages = {671--687},
doi = {10.1109/TEVC.2010.2058118},
keywords = {BC-EMOA},
annote = {Errata: DTLZ6 and DTLZ7 in the paper are actually DTLZ7 and
DTLZ8 in \cite{DebThiLau2005dtlz}}
}
@article{BatPro1996:jea,
author = { Roberto Battiti and M. Protasi},
title = {Reactive Search, A History-Based Heuristic for
{MAX}-{SAT}},
journal = {ACM Journal of Experimental Algorithmics},
year = 1997,
volume = 2
}
@article{BatSchUrl2017,
author = {Michele Battistutta and Andrea Schaerf and Tommaso Urli },
title = {Feature-based Tuning of Single-stage Simulated Annealing for
Examination Timetabling},
journal = {Annals of Operations Research},
year = 2017,
volume = 252,
number = 2,
pages = {239--254}
}
@article{BatTec1994:cma,
title = {Simulated annealing and Tabu search in the long run: A
comparison on {QAP} tasks},
author = { Roberto Battiti and Tecchiolli, Giampietro },
journal = {Computer and Mathematics with Applications},
volume = 28,
number = 6,
pages = {1--8},
year = 1994,
publisher = {Elsevier},
doi = {10.1016/0898-1221(94)00147-2}
}
@article{BatTec1994:orsa,
author = { Roberto Battiti and Tecchiolli, Giampietro },
title = {The Reactive Tabu Search},
journal = {ORSA Journal on Computing},
year = 1994,
volume = 6,
number = 2,
pages = {126--140}
}
@article{BatTec1996aor,
author = { Roberto Battiti and Tecchiolli, Giampietro },
title = {The continuous reactive tabu search: blending combinatorial
optimization and stochastic search for global optimization},
journal = {Annals of Operations Research},
year = 1996,
volume = 63,
number = 2,
pages = {151--188},
doi = {10.1007/BF02125453}
}
@article{BauPer2007:ejor,
author = { J. Bautista and J. Pereira },
title = {Ant algorithms for a time and space constrained
assembly line balancing problem},
journal = {European Journal of Operational Research},
year = 2007,
volume = 177,
number = 3,
pages = {2016--2032},
doi = {10.1016/j.ejor.2005.12.017}
}
@article{Baumol1962,
author = {Baumol, William J.},
title = {Management models and industrial applications of linear
programming},
journal = {Naval Research Logistics Quarterly},
volume = 9,
number = 1,
doi = {10.1002/nav.3800090109},
pages = {63--64},
year = 1962
}
@article{Baxter1981,
author = {John Baxter},
title = {Local Optima Avoidance in Depot Location},
journal = {Journal of the Operational Research Society},
year = 1981,
volume = 32,
number = 9,
pages = {815--819}
}
@article{BeaChu1996,
author = { John E. Beasley and P. C. Chu},
title = {A Genetic Algorithm for the Set Covering Problem},
journal = {European Journal of Operational Research},
year = 1996,
volume = 94,
number = 2,
pages = {392--404}
}
@article{BeaChu1998,
author = { John E. Beasley and P. C. Chu},
title = {A Genetic Algorithm for the Multidimensional Knapsack Problem},
journal = {Journal of Heuristics},
year = 1998,
volume = 4,
number = 1,
pages = {63--86}
}
@article{BeaShaSmiLop2018review,
author = {Bealt, Jennifer and Shaw, Duncan and Smith, Chris M. and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
year = 2019,
title = {Peer Reviews for Making Cities Resilient: A Systematic
Literature Review},
journal = {International Journal of Emergency Management},
volume = 15,
number = 4,
pages = {334--359},
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}
}
@article{Beasley1990orlib,
author = { John E. Beasley },
title = {{OR}-{Library:} distributing test problems by electronic
mail},
journal = {Journal of the Operational Research Society},
year = 1990,
pages = {1069--1072},
note = {Currently available from
\url{http://people.brunel.ac.uk/~mastjjb/jeb/info.html}}
}
@article{BehFat2011,
author = {J. Behnamian and S. M. T. {Fatemi Ghomi}},
title = {Hybrid Flowshop Scheduling with Machine and Resource-dependent Processing Times},
journal = {Applied Mathematical Modelling},
year = 2011,
volume = 35,
number = 3,
pages = {1107--1123}
}
@article{Bel1954,
author = {Richard Bellman},
title = {The theory of dynamic programming},
journal = {Bulletin of the American Mathematical Society},
volume = 60,
year = 1954,
pages = {503--515}
}
@article{BelCesDigSchUrl2016,
author = {Ruggero Bellio and Sara Ceschia and Luca {Di Gaspero} and Andrea Schaerf and Tommaso Urli },
title = {Feature-based tuning of simulated annealing applied to
the curriculum-based course timetabling problem},
journal = {Computers \& Operations Research},
volume = 65,
pages = {83--92},
year = 2016,
publisher = {Elsevier}
}
@article{Ben92,
author = { Jon Louis Bentley },
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},
doi = {10.1162/evco_a_00044},
year = 2012,
volume = 20,
number = 1,
pages = {63--89},
journal = {Evolutionary Computation},
keywords = {one-, two-, or three-dimensional knapsack and bin packing
problems}
}
@article{BurHydKen2012tec,
author = { Edmund K. Burke and Matthew R. Hyde and Graham Kendall },
title = {Grammatical Evolution of Local Search Heuristics},
journal = {IEEE Transactions on Evolutionary Computation},
volume = 16,
number = 7,
year = 2012,
pages = {406--417},
doi = {10.1109/TEVC.2011.2160401}
}
@article{BurKarRen1997,
title = {{QAPLIB}--a Quadratic Assignment Problem Library},
author = { Burkard, Rainer E. and Stefan E. Karisch and Franz Rendl },
journal = {Journal of Global Optimization},
volume = 10,
number = 4,
pages = {391--403},
year = 1997,
publisher = {Springer}
}
@article{BurRen1984,
author = { Burkard, Rainer E. and Franz Rendl },
title = {A Thermodynamically Motivated Simulation Procedure for
Combinatorial Optimization Problems},
journal = {European Journal of Operational Research},
year = 1984,
volume = 17,
number = 2,
pages = {169--174},
doi = {10.1016/0377-2217(84)90231-5},
keywords = {2-exchange delta evaluation for QAP}
}
@article{BusRobTot2014,
author = {Erika Buson and Roberto Roberti and Paolo Toth },
title = {A Reduced-Cost Iterated Local Search Heuristic for the Fixed-Charge Transportation Problem},
journal = {Operations Research},
year = 2014,
volume = 62,
number = 5,
pages = {1095--1106}
}
@article{CabLuqMol2002promoin,
author = {Caballero, R. and Mariano Luque and Molina, Juli{\'a}n and Francisco Ruiz },
title = {{PROMOIN}: An Interactive System for Multiobjective
Programming},
journal = {Information Technologies and Decision Making},
year = 2002,
volume = 1,
pages = {635--656},
keywords = {preferences, multi interactive methods framework}
}
@article{CacStu2017:endm,
author = { P{\'e}rez C{\'a}ceres, Leslie and Thomas St{\"u}tzle },
title = {Exploring Variable Neighborhood Search for Automatic
Algorithm Configuration},
journal = {Electronic Notes in Discrete Mathematics},
year = 2017,
volume = 58,
pages = {167--174},
doi = {10.1016/j.endm.2017.03.022}
}
@article{CahMelTal2004,
author = {Cahon, Sebastien and Melab, Nordine and Talbi, El-Ghazali },
title = {{ParadisEO}: A Framework for the Reusable Design of
Parallel and Distributed Metaheuristics},
doi = {10.1023/B:HEUR.0000026900.92269.ec},
journal = {Journal of Heuristics},
number = 3,
pages = {357--380},
volume = 10,
year = 2004
}
@article{CaiHuaQuiMa2009,
author = {Zhaoquan Cai and Han Huang and Yong Qin and Xianheng
Ma},
title = {Ant Colony Optimization Based on Adaptive Volatility
Rate of Pheromone Trail},
year = 2009,
journal = {International Journal of Communications, Network and System Sciences},
volume = 2,
number = 8,
pages = {792--796}
}
@article{CaiLiFan2015archive,
title = {An external archive guided multiobjective evolutionary
algorithm based on decomposition for combinatorial
optimization},
author = {Cai, Xinye and Li, Yexing and Fan, Zhun and Zhang, Qingfu },
journal = {IEEE Transactions on Evolutionary Computation},
year = 2015,
number = 4,
pages = {508--523},
volume = 19
}
@article{CaiXiaLiHu2021grid,
title = {A grid-based inverted generational distance for
multi/many-objective optimization},
author = {Cai, Xinye and Xiao, Yushun and Li, Miqing and Hu,
Han and Ishibuchi, Hisao and Li, Xiaoping},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2021,
number = 1,
pages = {21--34},
volume = 25,
publisher = {IEEE},
annote = {weakly Pareto-compliant indicator}
}
@article{CaiXiaLiSun2021kernel,
title = {A kernel-based indicator for multi/many-objective
optimization},
author = {Cai, Xinye and Xiao, Yushun and Li, Zhenhua and Sun, Qi and
Xu, Hanchuan and Li, Miqing and Ishibuchi, Hisao },
journal = {IEEE Transactions on Evolutionary Computation},
year = 2021
}
@article{Calvo00tsptw,
author = { Roberto {Wolfler Calvo} },
title = {A New Heuristic for the Traveling Salesman Problem
with Time Windows},
journal = {Transportation Science},
volume = 34,
number = 1,
year = 2000,
pages = {113--124},
doi = {10.1287/trsc.34.1.113.12284},
publisher = {{INFORMS}}
}
@article{CamBatAra2020jss,
title = {The \rpackage{MOEADr} Package: A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition},
author = {Felipe Campelo and Lucas S. Batista and Claus Aranha},
year = 2020,
volume = 92,
issue = 6,
journal = {Journal of Statistical Software},
doi = {10.18637/jss.v092.i06}
}
@article{CamDorStu2019si,
author = {Camacho-Villal\'{o}n, Christian Leonardo and Marco Dorigo and Thomas St{\"u}tzle },
title = {The intelligent water drops algorithm: why it cannot be considered a novel algorithm},
journal = {Swarm Intelligence},
year = 2019,
volume = 13,
pages = {173--192}
}
@article{CamDorStu2022cuckooSearch,
title = {An analysis of why cuckoo search does not bring any novel ideas to optimization},
author = {Camacho-Villal\'{o}n, Christian Leonardo and Marco Dorigo and Thomas St{\"u}tzle },
journal = {Computers \& Operations Research},
pages = {105747},
year = 2022
}
@article{CamDorStu2022exposing,
author = {Camacho-Villal\'{o}n, Christian Leonardo and Marco Dorigo and Thomas St{\"u}tzle },
title = {Exposing the grey wolf, moth-flame, whale, firefly, bat, and antlion algorithms: six misleading optimization techniques inspired by bestial metaphors},
journal = {International Transactions in Operational Research},
doi = {10.1111/itor.13176},
year = 2022
}
@article{CamJon2011,
title = {Prepositioning supplies in preparation for disasters},
author = {Campbell, Ann Melissa and Jones, Philip C.},
journal = {European Journal of Operational Research},
volume = 209,
number = 2,
pages = {156--165},
year = 2011,
publisher = {Elsevier}
}
@article{CamSchXia2013sentim,
author = {Cambria, E and Schuller, B and Xia, Y and Havasi, C},
year = 2013,
title = {New avenues in opinion mining and sentiment analysis},
journal = {IEEE Intelligent Systems},
volume = 28,
number = 2,
pages = {15--21},
doi = {10.1109/MIS.2013.30}
}
@article{CamStuDor2021psox,
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},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2021,
volume = 26,
number = 3,
pages = {402--416},
doi = {10.1109/TEVC.2021.3102863}
}
@article{CamWan2020sample,
author = {Felipe Campelo and Wanner, Elizabeth F. },
title = {Sample size calculations for the experimental comparison of
multiple algorithms on multiple problem instances},
journal = {Journal of Heuristics},
year = 2020,
volume = 26,
number = 6,
pages = {851--883},
doi = {10.1007/s10732-020-09454-w}
}
@article{CaoJiaZhan2017its,
author = {Cao, Z. and Jiang, S. and Zhang, J. and Guo, H.},
title = {A unified framework for vehicle rerouting and traffic light
control to reduce traffic congestion},
journal = {IEEE Transactions on Intelligent Transportation Systems},
pages = {1958--1973},
volume = 18,
number = 7,
year = 2017
}
@article{Cap2017,
title = {Variable Neighborhood Search for Extremal Vertices : The {AutoGraphiX-III} System},
author = {Gilles Caporossi},
journal = {Computers \& Operations Research},
volume = 78,
pages = {431--438},
year = 2017
}
@article{Car1982:ejor,
author = {J. Carlier},
title = {The One-machine Sequencing Problem},
journal = {European Journal of Operational Research},
year = 1982,
volume = 11,
number = 1,
pages = {42--47}
}
@article{CarBar96tabusearch_tsptw,
author = { William B. Carlton and J. Wesley Barnes },
title = {Solving the traveling-salesman problem with time
windows using tabu search},
journal = {IIE Transactions},
year = 1996,
volume = 28,
pages = {617--629}
}
@article{CarKonCor2019inf,
author = {Fabio Caraffini and Anna V. Kononova and David Corne },
title = {Infeasibility and structural bias in differential evolution},
journal = {Information Sciences},
volume = 496,
pages = {161--179},
year = 2019,
doi = {10.1016/j.ins.2019.05.019}
}
@article{CasLab99,
title = {Heuristics for large constrained vehicle routing problems},
author = {Caseau, Yves and Laburthe, Fran{\c{c}}ois},
journal = {Journal of Heuristics},
volume = 5,
number = 3,
pages = {281--303},
year = 1999
}
@article{CasSilLab2001tplp,
author = {Yves Caseau and Glenn Silverstein and Fran{\c{c}}ois
Laburthe},
title = {Learning Hybrid Algorithms for Vehicle Routing
Problems},
journal = {Theory and Practice of Logic Programming},
volume = 1,
number = 6,
year = 2001,
pages = {779--806},
epub = {http://arxiv.org/abs/cs/0405092}
}
@article{CatAbsFeiVig2014:cor,
author = {Diego Cattaruzza and Nabil Absi and Dominique Feillet and Vigo, Daniele },
title = {An Iterated Local Search for the Multi-commodity Multi-trip Vehicle Routing Problem with Time Windows},
journal = {Computers \& Operations Research},
year = 2014,
volume = 51,
pages = {257--267}
}
@article{CauNiePok2012,
title = {Optimization models in emergency logistics: A literature review},
author = {Caunhye, Aakil M. and Nie, Xiaofeng and Pokharel, Shaligram},
journal = {Socio-Economic Planning Sciences},
volume = 46,
number = 1,
pages = {4--13},
year = 2012,
publisher = {Elsevier}
}
@article{CebIruMen2014eda,
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
solve the permutation flowshop scheduling problem. A hybrid
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
tested, the proposed hybrid approach obtains new best known
results in 152 cases. An in-depth study of the results
suggests that the successful performance of the introduced
approach is due to the ability of the generalized Mallows
estimation of distribution algorithm to discover promising
regions in the search space.},
doi = {10.1109/TEVC.2013.2260548},
journal = {IEEE Transactions on Evolutionary Computation},
keywords = {Estimation of distribution algorithms,Generalized Mallows
model,Permutation flowshop scheduling
problem,Permutations-based optimization problems},
number = 2,
pages = {286--300},
volume = 18,
year = 2014
}
@article{Cer85,
author = {Vladim{\'i}r \v{C}ern\'y},
title = {A Thermodynamical Approach to the Traveling Salesman Problem: An Efficient Simulation Algorithm},
journal = {Journal of Optimization Theory and Applications},
year = 1985,
volume = 45,
number = 1,
pages = {41--51}
}
@article{CerVas2010mga,
author = {Ceriotti, Matteo and Vasile, Massimiliano},
title = {Automated Multigravity Assist Trajectory Planning with a
Modified Ant Colony Algorithm},
journal = {Journal of Aerospace Computing, Information, and
Communication},
year = 2010,
volume = 7,
number = 9,
pages = {261--293},
doi = {10.2514/1.48448}
}
@article{CesDiGSch2012,
title = {Design, Engineering, and Experimental Analysis of a Simulated Annealing Approach to the Post-Enrolment Course Timetabling Problem},
author = { Sara Ceschia and Luca {Di Gaspero} and Andrea Schaerf},
journal = {Computers \& Operations Research},
volume = 39,
number = 7,
pages = {1615--1624},
year = 2012,
publisher = {Elsevier}
}
@article{CesSch2012aim,
author = { Sara Ceschia and Andrea Schaerf},
title = {Modeling and solving the dynamic patient admission scheduling problem under uncertainty},
journal = {Artificial Intelligence in Medicine},
volume = 56,
number = 3,
pages = {199--205},
year = 2012,
doi = {10.1016/j.artmed.2012.09.001},
keywords = {F-race}
}
@article{CesSchStu2013:cie,
author = { Sara Ceschia and Andrea Schaerf and Thomas St{\"u}tzle },
title = {Local Search Techniques for a Routing-packing Problem},
journal = {Computers and Industrial Engineering},
year = 2013,
volume = 66,
number = 4,
pages = {1138--1149}
}
@article{ChaMeaBea2000cor,
author = {T.-J. Chang and N. Meade and John E. Beasley and Y. M. Sharaiha},
title = {Heuristics for cardinality constrained portfolio
optimisation},
journal = {Computers \& Operations Research},
year = 2000,
volume = 27,
number = 13,
pages = {1271--1302},
keywords = {Portfolio optimisation, CCMVPOP, Efficient frontier},
abstract = {In this paper we consider the problem of finding the
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.}
}
@article{ChaWag2015many,
author = {Shelvin Chand and Markus Wagner },
title = {Evolutionary many-objective optimization: A quick-start
guide},
journal = {Surveys in Operations Research and Management Science},
volume = 20,
number = 2,
pages = {35--42},
year = 2015,
doi = {10.1016/j.sorms.2015.08.001}
}
@article{Chase93,
author = { Donald V. Chase and Lindell E. Ormsbee },
title = {Computer-generated pumping schedules for satisfying
operation objectives},
journal = {J. Am. Water Works Assoc.},
year = 1993,
volume = 85,
number = 7,
pages = {54--61}
}
@article{ChauDeb2010asoft,
title = {An interactive evolutionary multi-objective optimization and
decision making procedure},
author = {Chaudhuri, Shamik and Kalyanmoy Deb },
journal = {Applied Soft Computing},
volume = 10,
number = 2,
pages = {496--511},
year = 2010
}
@article{CheChiSto2012,
title = {Business Intelligence and Analytics: From Big Data to Big
Impact},
author = {Chen, Hsinchun and Chiang, Roger H. L. and Storey, Veda C.},
journal = {MIS Quarterly},
volume = 36,
number = 4,
pages = {1165--1188},
year = 2012
}
@article{CheHaoGlo2016,
title = {A hybrid metaheuristic approach for the capacitated arc
routing problem},
author = {Yuning Chen and Jin-Kao Hao and Fred Glover },
journal = {European Journal of Operational Research},
volume = 553,
number = 1,
pages = {25--39},
year = 2016,
doi = {10.1016/j.ejor.2016.02.015},
keywords = {irace}
}
@article{CheHsi2014,
author = {Chen, Ruey-Maw and Hsieh, Fu-Ren},
title = {An exchange local search heuristic based scheme for
permutation flow shop problems},
journal = {Applied Mathematics \& Information Sciences},
volume = 8,
number = 1,
pages = {209--215},
year = 2014
}
@article{CheLi1999gen,
author = { F. Y. Cheng and X. S. Li },
title = {Generalized center method for multiobjective engineering
optimization},
journal = {Engineering Optimization},
volume = 31,
number = 5,
pages = {641--661},
year = 1999,
doi = {10.1080/03052159908941390}
}
@article{CheLiYao2017dynamic,
author = {Chen, Renzhi and Li, Ke and Xin Yao },
title = {Dynamic Multiobjectives Optimization With a Changing Number
of Objectives},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2017,
volume = 22,
number = 1,
pages = {157--171},
annote = {two co-evolving populations (two archive)},
doi = {10.1109/TEVC.2017.2669638}
}
@article{CheSia2000ejor,
title = {Tabu search applied to global optimization},
author = {Rachid Chelouah and Patrick Siarry},
journal = {European Journal of Operational Research},
volume = 123,
number = 2,
pages = {256--270},
year = 2000
}
@article{ChenChenGon2014tcyb,
title = {An evolutionary algorithm with double-level archives for
multiobjective optimization},
author = {Chen, Ni and Chen, Wei-Neng and Gong, Yue-Jiao and Zhan,
Zhi-Hui and Zhang, Jun and Li, Yun and Tan, Yu-Song},
journal = {IEEE Transactions on Cybernetics},
year = 2014,
number = 9,
pages = {1851--1863},
volume = 45,
publisher = {IEEE}
}
@article{ChengMao07:acs_tsptw,
author = {Chin-Bin Cheng and Chun-Pin Mao},
title = {A modified ant colony system for solving the
travelling salesman problem with time windows},
journal = {Mathematical and Computer Modelling},
year = 2007,
volume = 46,
pages = {1225--1235},
doi = {10.1016/j.mcm.2006.11.035}
}
@article{ChiBirSocRos2006jos,
author = { Marco Chiarandini and Mauro Birattari and Krzysztof Socha and O. Rossi-Doria },
title = {An Effective Hybrid Algorithm for University Course
Timetabling},
journal = {Journal of Scheduling},
year = 2006,
volume = 9,
pages = {403--432},
number = 5,
month = oct,
doi = {10.1007/s10951-006-8495-8},
keywords = {2003 international timetabling competition, F-race}
}
@article{ChiCorDamBau11,
author = {Chica, Manuel and Oscar Cord{\'o}n and Damas,
Sergio and Bautista, Joaqu{\'i}n},
year = 2011,
issn = {1865-9284},
journal = {Memetic Computing},
volume = 3,
number = 1,
title = {A New Diversity Induction Mechanism for a
Multi-objective Ant Colony Algorithm to Solve a
Real-world time and Space Assembly Line Balancing
Problem},
pages = {15--24}
}
@article{ChiUlySha2016,
author = {D. S. Chivilikhin and V. I. Ulyantsev and A. A. Shalyto},
title = {Modified ant colony algorithm for constructing finite state machines from execution scenarios and temporal formulas},
journal = {Automation and Remote Control},
year = 2016,
volume = 77,
number = 3,
pages = {473--484},
doi = {10.1134/S0005117916030097},
keywords = {irace}
}
@article{Chicano2011ecj,
author = { Chicano, Francisco and Darrell Whitley and Alba, Enrique },
title = {A Methodology to Find the Elementary Landscape Decomposition
of Combinatorial Optimization Problems},
journal = {Evolutionary Computation},
year = 2011,
volume = 19,
pages = {597--637},
number = 4
}
@article{Chicano2012aml,
author = { Chicano, Francisco and Gabriel J. Luque and Alba, Enrique },
title = {Autocorrelation Measures for the Quadratic Assignment
Problem},
journal = {Applied Mathematics Letters},
year = 2012,
volume = 25,
pages = {698--705},
doi = {10.1016/j.aml.2011.09.053}
}
@article{ChrMinTot81tsptw,
author = { Christofides, Nicos and A. Mingozzi and Paolo Toth },
title = {State-space relaxation procedures for the
computation of bounds to routing problems},
volume = 11,
number = 2,
journal = {Networks},
year = 1981,
pages = {145--164},
doi = {10.1002/net.3230110207},
anote = {makespan optimization}
}
@article{ChuJinMie2018surrogate,
author = { Tinkle Chugh and Yaochu Jin and Kaisa Miettinen and Hakanen,
Jussi and Sindhya, Karthik},
journal = {IEEE Transactions on Evolutionary Computation},
title = {A Surrogate-Assisted Reference Vector Guided Evolutionary
Algorithm for Computationally Expensive Many-Objective
Optimization},
year = 2018,
volume = 22,
number = 1,
pages = {129--142},
month = feb
}
@article{ChuSinHak2019surv,
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}
}
@article{CirHoe2013mdd,
author = { Cire, Andr{\'e} A. and van Hoeve, Willem-Jan},
title = {Multivalued Decision Diagrams for Sequencing Problems},
journal = {Operations Research},
year = 2013,
volume = 61,
number = 6,
pages = {1259--1462},
doi = {10.1287/opre.2013.1221}
}
@article{Clark95,
author = { R. M. Clark and L. A. Rossman and L. J. Wymer },
title = {Modeling distribution system water quality:
regulatory implications},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
year = 1995,
volume = 121,
number = 6,
pages = {423--428},
note = {}
}
@article{CleKen2022tec,
author = { Clerc, Maurice and J. Kennedy },
title = {The particle swarm - explosion, stability, and convergence in
a multidimensional complex space},
journal = {IEEE Transactions on Evolutionary Computation},
volume = 6,
number = 1,
pages = {58--73},
month = feb,
year = 2002,
doi = {10.1109/4235.985692}
}
@article{CocDraBes2020threats,
author = {Cockburn, Andy and Dragicevic, Pierre and Besan\c{c}on, Lonni
and Gutwin, Carl},
title = {Threats of a Replication Crisis in Empirical Computer
Science},
year = 2020,
volume = 63,
number = 8,
doi = {10.1145/3360311},
abstract = {Research replication only works if there is confidence built
into the results.},
journal = {Communications of the ACM},
month = jul,
pages = {70--79},
numpages = 10
}
@article{CodManMarRes96:informs,
author = {B. Codenotti and G. Manzini and L. Margara and G. Resta},
title = {Perturbation: An Efficient Technique for the
Solution of Very Large Instances of the
{Euclidean} {TSP}},
journal = {INFORMS Journal on Computing},
year = 1996,
volume = 8,
number = 2,
pages = {125--133}
}
@article{Coe2002constraint,
author = { Carlos A. {Coello Coello} },
title = {Theoretical and numerical constraint-handling techniques used
with evolutionary algorithms: a survey of the state of the
art},
journal = {Computer Methods in Applied Mechanics and Engineering},
year = 2002,
volume = 191,
number = {11-12},
pages = {1245--1287},
doi = {10.1016/S0045-7825(01)00323-1}
}
@article{Coello2003tec-emo,
title = {Special Issue on {Evolutionary} {Multiobjective}
{Optimization}},
author = { Carlos A. {Coello Coello} },
journal = {IEEE Transactions on Evolutionary Computation},
year = 2003,
volume = 7,
number = 2
}
@article{Coello2006evolutionary,
title = {Evolutionary multi-objective optimization: a historical view
of the field},
author = { Carlos A. {Coello Coello} },
journal = {IEEE Computational Intelligence Magazine},
volume = 1,
number = 1,
pages = {28--36},
year = 2006
}
@article{CohFie1999:siamo,
author = { Harry Cohn and Mark J. Fielding },
title = {Simulated Annealing: Searching for an Optimal Temperature},
journal = {SIAM Journal on Optimization},
year = 1999,
volume = 9,
number = 3,
pages = {779--802}
}
@article{ColBry05:jwrpm,
author = { Andrew F. Colombo and Bryan W. Karney },
title = {Impacts of Leaks on Energy Consumption in Pumped
Systems with Storage},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
year = 2005,
volume = 131,
number = 2,
pages = {146--155},
month = mar
}
@article{ColDorManTru1994:jorbel,
author = { Alberto Colorni and Marco Dorigo and Vittorio Maniezzo and M. Trubian},
title = {{Ant} {System} for Job-shop Scheduling},
journal = {JORBEL --- Belgian Journal of Operations Research, Statistics and Computer Science},
year = 1994,
volume = 34,
number = 1,
pages = {39--53}
}
@article{ColSchPae2010setting,
title = {Setting the Research Agenda in Automated Timetabling: The
Second International Timetabling Competition},
author = { Barry McCollum and Andrea Schaerf and Ben Paechter and Paul McMullan and Lewis, Rhyd M. R. and Andrew J. Parkes and Luca {Di Gaspero} and Rong Qu and Edmund K. Burke },
doi = {10.1287/ijoc.1090.0320},
year = 2010,
month = feb,
volume = 22,
number = 1,
pages = {120--130},
journal = {{INFORMS}}
}
@article{ConPotVel02,
author = {Richard K. Congram and Chris N. Potts and Steve van de Velde},
title = {An Iterated Dynasearch Algorithm for the
Single-Machine Total Weighted Tardiness Scheduling
Problem},
journal = {INFORMS Journal on Computing},
year = 2002,
volume = 14,
number = 1,
pages = {52--67}
}
@article{Connolly1990,
author = { David T. Connolly },
title = {An Improved Annealing Scheme for the {QAP}},
journal = {European Journal of Operational Research},
volume = 46,
number = 1,
pages = {93--100},
year = 1990,
publisher = {Elsevier}
}
@article{CooFar1996mul,
author = {Cook, Richard J. and Farewell, Vern T.},
year = 1996,
title = {Multiplicity Considerations in the Design and Analysis of
Clinical Trials},
journal = {Journal of the Royal Statistical Society: Series A},
volume = 159,
pages = {93--110},
annote = {multiplicity; multiple endpoints; multiple treatments;
p-value adjustment; type I error; argues that if results are
intended to be interpreted marginally, there may be no need
for controlling experimentwise error rate}
}
@article{CopFleRur2010ordering,
title = {Ordering by Weighted Number of Wins Gives a Good Ranking for
Weighted Tournaments},
author = {Coppersmith, Don and Fleischer, Lisa K. and Rurda, Atri},
doi = {10.1145/1798596.1798608},
journal = {{ACM} Transactions on Algorithms },
keywords = {Approximation algorithms,Borda's method,feedback arc set
problem,rank aggregation,tournaments},
month = jul,
number = 3,
volume = 6,
pages = {1--13},
publisher = {ACM},
year = 2010
}
@article{CopGilSch2024decision,
author = {Copp{\'e}, Vianney and Gillard, Xavier and Schaus, Pierre},
title = {Decision Diagram-Based Branch-and-Bound with Caching for
Dominance and Suboptimality Detection},
journal = {INFORMS Journal on Computing},
year = 2024,
doi = {10.1287/ijoc.2022.0340}
}
@article{CorBuc1997ejor,
author = {Corner, James L. and Buchanan, John T. },
title = {Capturing decision maker preference: Experimental comparison
of decision analysis and {MCDM} techniques},
journal = {European Journal of Operational Research},
year = 1997,
volume = 98,
number = 1,
pages = {85--97}
}
@article{CorDam2006,
author = { Oscar Cord{\'o}n and Sergio Damas},
title = {Image Registration with Iterated Local Search},
journal = {Journal of Heuristics},
year = 2006,
volume = 12,
number = {1--2},
pages = {73--94}
}
@article{CorDanDep2019,
author = {Jeroen Corstjens and Nguyen Dang and Depaire, Beno{\^i}t and Caris, An and Patrick {De Causmaecker}},
title = {A combined approach for analysing heuristic algorithms},
journal = {Journal of Heuristics},
year = 2019,
volume = 25,
number = 4,
pages = {591--628},
doi = {10.1007/s10732-018-9388-7}
}
@article{CorDepCarSor2020,
title = {A multilevel evaluation method for heuristics with an application to the {VRPTW}},
author = {Jeroen Corstjens and Depaire, Beno{\^i}t and Caris, An and Kenneth S{\"o}rensen },
journal = {International Transactions in Operational Research},
year = 2020,
volume = 27,
number = 1,
pages = {168--196},
doi = {10.1111/itor.12631}
}
@article{CorKoz04:compu,
author = {P. Corry and E. Kozan},
title = {Ant Colony Optimisation for Machine Layout Problems},
journal = {Computational Optimization and Applications},
year = 2004,
volume = 28,
number = 3,
pages = {287--310}
}
@article{CorLapMer2001,
author = {Jean{-}Fran{\c{c}}ois Cordeau and Gilbert Laporte and A. Mercier},
title = {A unified tabu search heuristic for vehicle routing problems with time windows},
journal = {Journal of the Operational Research Society},
year = 2001,
volume = 52,
number = 8,
pages = {928--936}
}
@article{CorMai2012:cor,
author = {Jean{-}Fran{\c{c}}ois Cordeau and Mirko Maischberger},
title = {A Parallel Iterated Tabu Search Heuristic for Vehicle Routing Problems},
journal = {Computers \& Operations Research},
year = 2012,
volume = 39,
number = 9,
pages = {2033--2050}
}
@article{CosGolGol2012:esa,
title = {Hybridizing {VNS} and path-relinking on a particle swarm
framework to minimize total flowtime },
journal = {Expert Systems with Applications},
volume = 39,
number = 18,
pages = {13118--13126},
year = 2012,
author = {Wagner Emanoel Costa and Goldbarg, Marco Cesar and Goldbarg, Elizabeth Ferreira Gouv{\^e}a }
}
@article{CosHer97:jors,
author = {D. Costa and A. Hertz},
title = {Ants can color graphs},
journal = {Journal of the Operational Research Society},
year = 1997,
volume = 48,
pages = {295--305}
}
@article{CoyGolRunWas2001,
author = {S. P. Coy and B. L. Golden and G. C. Runger and E. A. Wasil},
title = {Using Experimental Design to Find Effective
Parameter Settings for Heuristics},
journal = {Journal of Heuristics},
year = 2001,
volume = 7,
number = 1,
pages = {77--97}
}
@article{Crabtree1995,
title = {Resource Scheduling: Comparing Simulated Annealing with Constraint Programming},
author = { I. Barry Crabtree },
journal = {BT Technology Journal},
volume = 13,
number = 1,
pages = {121--127},
year = 1995,
publisher = {Springer}
}
@article{CriFliVer1996rankings,
author = {Critchlow, Douglas Edward and Fligner, Michael A. and
Verducci, Joseph S.},
journal = {Journal of Mathematical Psychology},
pages = {294--318},
title = {Probability Models on Rankings},
volume = 35,
year = 1991
}
@article{Cro1958,
author = {G. A. Croes},
title = {A Method for Solving Traveling Salesman Problems},
journal = {Operations Research},
year = 1958,
volume = 6,
pages = {791--812}
}
@article{CroDemMul1978reporting,
author = {Harlan P. Crowder and Ron S. Dembo and John M. Mulvey},
title = {Reporting computational experiments in mathematical
programming},
journal = {Mathematical Programming},
volume = 15,
number = 1,
pages = {316--329},
year = 1978,
doi = {10.1007/BF01609036},
keywords = {reproducibility}
}
@article{CruGonPel2011,
author = {Carlos Cruz and Juan Ram{\'{o}}n Gonz{\'{a}}lez and David A. Pelta},
title = {Optimization in Dynamic Environments: A Survey on Problems, Methods
and Measures},
journal = {Soft Computing},
year = 2011,
volume = 15,
number = 7,
pages = {1427--1448}
}
@article{CruSubBruOir2017:cor,
author = {F{\'a}bio Cruz and Anand Subramanian and Bruno P. Bruck and Manuel Iori },
title = {A Heuristic Algorithm for a Single Vehicle Static Bike Sharing Rebalancing Problem},
journal = {Computers \& Operations Research},
volume = 79,
year = 2017,
pages = {19--33}
}
@article{Cul1998ec,
author = { Joseph C. Culberson },
title = {On the Futility of Blind Search: An Algorithmic View of ``No
Free Lunch''},
journal = {Evolutionary Computation},
year = 1998,
volume = 6,
number = 2,
pages = {109--127},
doi = {10.1162/evco.1998.6.2.109},
keywords = {NFL}
}
@article{CzyJas1998,
title = {{Pareto} simulated annealing -- a metaheuristic
technique for multiple-objective combinatorial
optimization},
author = {Czyz{\.z}ak, P. and Andrzej Jaszkiewicz },
journal = {Journal of Multi-Criteria Decision Analysis},
volume = 7,
number = 1,
pages = {34--47},
year = 1998
}
@article{DamHickRago2010,
title = {On Energy, Discrepancy and Group Invariant Measures on
Measurable Subsets of {Euclidean} Space},
author = {Damelin, Steven B. and Hickernell, Fred J. and Ragozin, David
L. and Zeng, Xiaoyan},
journal = {Journal of Fourier Analysis and Applications},
year = 2010,
number = 6,
pages = {813--839},
volume = 16,
keywords = {Capacity; Cubature; Discrepancy; Distribution; Group
invariant kernel; Group invariant measure; Energy minimizer;
Equilibrium measure; Numerical integration; Positive
definite; Potential field; Riesz kernel; Reproducing Hilbert
space; Signed measure},
publisher = {SP Birkhäuser Verlag Boston}
}
@article{Damas:2001:PDW,
author = {M. Damas and M. Salmer{\'o}n and J. Ortega and
G. Olivares and H. Pomares},
title = {Parallel Dynamic Water Supply Scheduling in a
Cluster of Computers},
journal = {Concurrency and Computation: Prac\-tice and Experience},
volume = 13,
number = 15,
pages = {1281--1302},
day = 25,
month = dec,
year = 2001,
coden = {CCPEBO},
issn = {1532-0626 (print), 1532-0634 (electronic)}
}
@article{DanPoz2020,
author = {Augusto Dantas and Aurora Pozo},
title = {On the use of fitness landscape features in meta-learning
based algorithm selection for the quadratic assignment
problem},
journal = {Theoretical Computer Science},
year = 2020,
volume = 805,
pages = {62--75},
doi = {10.1016/j.tcs.2019.10.033}
}
@article{DanRotLep2005,
title = {Exploring relaxation induced neighborhoods to improve {MIP} solutions},
author = {Danna, Emilie and Rothberg, Edward and Le Pape, Claude},
journal = {Mathematical Programming},
volume = 102,
number = 1,
pages = {71--90},
year = 2005,
publisher = {Springer}
}
@article{DanWol1960,
title = {Decomposition Principle for Linear Programs},
author = {George B. Dantzig and Philip Wolfe},
journal = {Operations Research},
volume = 8,
number = 1,
pages = {101--111},
year = 1960
}
@article{DaoLieVerAgu2017ec,
author = {Fabio Daolio and Arnaud Liefooghe and Verel, S{\'e}bastien and Aguirre, Hern\'{a}n E. and Tanaka, Kiyoshi },
journal = {Evolutionary Computation},
number = 4,
pages = {555--585},
title = {Problem Features versus Algorithm Performance on Rugged
Multiobjective Combinatorial Fitness Landscapes},
volume = 25,
year = 2017,
doi = {10.1162/evco_a_00193}
}
@article{DasDen1997,
author = {Das, Indraneel and Dennis, John E.},
title = {A closer look at drawbacks of minimizing weighted
sums of objectives for {Pareto} set generation in
multicriteria optimization problems},
journal = {Structural Optimization},
year = 1997,
volume = 14,
number = 1,
pages = {63--69},
doi = {10.1007/BF01197559}
}
@article{DasDen1998normal,
author = {Das, Indraneel and Dennis, John E.},
title = {Normal-boundary intersection: A new method for generating the
{Pareto} surface in nonlinear multicriteria optimization
problems},
journal = {SIAM Journal on Optimization},
year = 1998,
volume = 8,
number = 3,
pages = {631--657},
keywords = {simplex lattice design}
}
@article{DasMulSug2016de,
title = {Recent advances in differential evolution--{An} updated
survey},
author = { Swagatam Das and Mullick, Sankha Subhra and Ponnuthurai N. Suganthan },
journal = {Swarm and Evolutionary Computation},
volume = 27,
pages = {1--30},
year = 2016
}
@article{DasSug2011:tec,
author = { Swagatam Das and Ponnuthurai N. Suganthan },
title = {Differential Evolution: A Survey of the State-of-the-art},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2011,
volume = 15,
number = 1,
month = feb
}
@article{Dash05,
author = {Sanjeeb Dash},
title = {Exponential Lower Bounds on the Lengths of Some Classes
of Branch-and-Cut Proofs},
journal = {Mathematics of Operations Research},
year = 2005,
volume = 30,
number = 3,
pages = {678--700}
}
@article{DasDiaYan2016chord,
title = {How good is the {Chord} algorithm?},
author = {Daskalakis, Constantinos and Diakonikolas, Ilias and Mihalis Yannakakis },
journal = {SIAM Journal on Computing},
year = 2016,
number = 3,
pages = {811--858},
volume = 45
}
@article{DauDouPesKie2010:po2,
author = { Jean Daunizeau and Hanneke E. M. den Ouden and Matthias Pessiglione and Stefan J. Kiebel and Karl J. Friston and Klaas E. Stephan },
title = {Observing the observer ({II}): deciding when to decide},
journal = {PLoS One},
year = 2010,
volume = 5,
number = 12,
pages = {e15555},
doi = {10.1371/journal.pone.0015555}
}
@article{DauDouPesSte2010:po1,
author = { Jean Daunizeau and Hanneke E. M. den Ouden and Matthias Pessiglione and Klaas E. Stephan and Stefan J. Kiebel and Karl J. Friston },
title = {Observing the observer ({I}): meta-{Bayesian} models of learning and decision-making},
journal = {PLoS One},
year = 2010,
volume = 5,
number = 12,
pages = {e15554},
doi = {10.1371/journal.pone.0015554}
}
@article{Deb00constraint,
author = { Kalyanmoy Deb },
title = {An efficient constraint handling method for genetic
algorithms},
journal = {Computer Methods in Applied Mechanics and Engineering},
year = 2000,
volume = 186,
number = {2/4},
pages = {311--338},
doi = {10.1016/S0045-7825(99)00389-8}
}
@article{Deb02nsga2,
author = { Kalyanmoy Deb and A. Pratap and S. Agarwal and T. Meyarivan},
title = {A fast and elitist multi-objective genetic
algorithm: {NSGA-II}},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2002,
volume = 6,
number = 2,
pages = {182--197},
doi = {10.1109/4235.996017}
}
@article{Deb1999ec,
author = { Kalyanmoy Deb },
title = {Multi-objective genetic algorithms: problem
difficulties and construction of test problems},
journal = {Evolutionary Computation},
year = 1999,
volume = 7,
number = 3,
pages = {205--230},
annote = {Naive definition of PLO-set}
}
@article{DebAgr1995sbx,
author = { Kalyanmoy Deb and Ram Bhushan Agrawal },
title = {Simulated binary crossover for continuous search
spaces},
journal = {Complex Systems},
volume = 9,
number = 2,
pages = {115--148},
year = 1995,
epub = {http://www.complex-systems.com/abstracts/v09_i02_a02.html},
keywords = {SBX}
}
@article{DebDeb2014,
author = { Kalyanmoy Deb and Debayan Deb},
title = {Analysing mutation schemes for real-parameter genetic
algorithms},
journal = {International Journal of Artificial Intelligence and Soft Computing},
year = 2014,
volume = 4,
number = 1,
pages = {1--28},
annote = {Proposed Gaussian mutation}
}
@article{DebGupDauBran2009reliab,
author = { Kalyanmoy Deb and S. Gupta and D. Daum and J{\"u}rgen Branke and A. Mall and D. Padmanabhan},
title = {Reliability-based optimization using evolutionary algorithms},
journal = {IEEE Transactions on Evolutionary Computation},
volume = 13,
number = 5,
pages = {1054--1074},
month = oct,
year = 2009,
doi = {10.1109/TEVC.2009.2014361}
}
@article{DebJain2014:nsga3-part1,
author = { Kalyanmoy Deb and Himanshu Jain },
journal = {IEEE Transactions on Evolutionary Computation},
title = {An Evolutionary Many-Objective Optimization Algorithm Using
Reference-Point-Based Nondominated Sorting Approach, Part
{I}: Solving Problems With Box Constraints},
year = 2014,
volume = 18,
number = 4,
pages = {577--601},
annote = {Proposed NSGA-III}
}
@article{DebKok2010tec-ged,
author = { Kalyanmoy Deb and Murat K{\"o}ksalan },
title = {Guest Editorial: Special Issue on Preference-based
Multiobjective Evolutionary Algorithms},
journal = {IEEE Transactions on Evolutionary Computation},
volume = 14,
number = 5,
month = oct,
year = 2010,
pages = {669--670},
doi = {10.1109/TEVC.2010.2070371}
}
@article{DebMohMis2005epsilon,
title = {Evaluating the {$\epsilon$}-domination based multi-objective
evolutionary algorithm for a quick computation of
{Pareto}-optimal solutions},
author = { Kalyanmoy Deb and Mohan, Manikanth and Mishra, Shikhar},
journal = {Evolutionary Computation},
year = 2005,
month = dec,
number = 4,
pages = {501--525},
volume = 13,
doi = {10.1162/106365605774666895},
keywords = {$\epsilon$-dominance, $\epsilon$-MOEA}
}
@article{DebTiw2008omni,
author = { Kalyanmoy Deb and Santosh Tiwari },
title = {Omni-optimizer: {A} generic evolutionary algorithm for single
and multi-objective optimization},
journal = {European Journal of Operational Research},
year = 2008,
volume = 185,
number = 3,
pages = {1062--1087},
annote = {Archiving method with epsilon dominance and density in the
decision and objective spaces},
keywords = {epsilon-dominance, archiving},
doi = {10.1016/j.ejor.2006.06.042}
}
@article{DebZhuKul2018tec,
author = { Kalyanmoy Deb and Zhu, Ling and Kulkarni, Sandeep},
title = {Handling Multiple Scenarios in Evolutionary Multi-Objective
Numerical Optimization},
doi = {10.1109/TEVC.2017.2776921},
abstract = {Solutions to most practical numerical optimization problems
must be evaluated for their performance over a number of
different loading or operating conditions, which we refer
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
optimize for the worst-case scenario. In this paper, we
review existing methodologies in this direction and set our
goal to suggest a new and potential population-based method
for handling multiple scenarios by defining scenario-wise
domination principle and scenario-wise diversity-preserving
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
application must be spent.},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2018,
volume = 22,
number = 6,
pages = {920--933},
keywords = {scenario-based}
}
@article{DecSor2012ejor,
author = {Annelies De Corte and Kenneth S{\"o}rensen },
title = {Optimisation of gravity-fed water distribution network
design: A critical review},
journal = {European Journal of Operational Research},
volume = 228,
number = 1,
pages = {1--10},
doi = {10.1016/j.ejor.2012.11.046},
year = 2013
}
@article{DecSor2016,
author = {Annelies De Corte and Kenneth S{\"o}rensen },
title = {An Iterated Local Search Algorithm for Water Distribution
Network Design Optimization},
journal = {Networks},
year = 2016,
volume = 67,
number = 3,
pages = {187--198}
}
@article{DecSor2016water,
author = {Annelies De Corte and Kenneth S{\"o}rensen },
title = {An Iterated Local Search Algorithm for multi-period water
distribution network design optimization},
journal = {Water},
volume = 8,
number = 8,
pages = 359,
doi = {10.3390/w8080359},
year = 2016
}
@article{Dek1981:cad,
author = { V. Dekhtyarenko },
title = {Verification of weight coefficients in multicriteria optimization problems},
journal = {Computer-Aided Design},
volume = 13,
number = 6,
pages = {339--344},
year = 1981
}
@article{DelGanDeg2010,
title = {Evolutionary, constructive and hybrid procedures for
the bi-objective set packing problem},
author = {Delorme, X. and Xavier Gandibleux and Degoutin, F.},
journal = {European Journal of Operational Research},
volume = 204,
number = 2,
pages = {206--217},
year = 2010,
annote = {This paper cannot be found on internet!! Does it exist?}
}
@article{DelGarGro2012:cor,
author = { Federico {Della Croce} and Thierry Garaix and Andrea Grosso },
title = {Iterated Local Search and Very Large Neighborhoods for the Parallel-machines
Total Tardiness Problem},
journal = {Computers \& Operations Research},
year = 2012,
volume = 39,
number = 6,
pages = {1213--1217}
}
@article{DelIorMar2016binpack,
title = {Bin packing and cutting stock problems: Mathematical models
and exact algorithms},
author = {Delorme, Maxence and Manuel Iori and Silvano Martello },
journal = {European Journal of Operational Research},
volume = 255,
number = 1,
pages = {1--20},
year = 2016,
publisher = {Elsevier},
doi = {10.1016/j.ejor.2016.04.030}
}
@article{DelIorMarMon2016,
author = {Mauro Dell'Amico and Manuel Iori and Silvano Martello and Monaci, Michele },
title = {Heuristic and Exact Algorithms for the Identical Parallel Machine Scheduling Problem},
journal = {INFORMS Journal on Computing},
year = 2016,
volume = 20,
number = 3,
pages = {333--344}
}
@article{DelIorMarc2018bpplib,
title = {{BPPLIB}: a library for bin packing and cutting stock problems},
author = {Delorme, Maxence and Manuel Iori and Silvano Martello },
journal = {Optimization Letters},
volume = 12,
number = 2,
pages = {235--250},
year = 2018,
doi = {10.1007/s11590-017-1192-z}
}
@article{DelIorNovStu2016,
author = {Mauro Dell'Amico and Manuel Iori and Stefano Novellani and Thomas St{\"u}tzle },
title = {A destroy and repair algorithm for the Bike sharing Rebalancing Problem},
journal = {Computers \& Operations Research},
volume = 71,
pages = {146--162},
year = 2016,
doi = {10.1016/j.cor.2016.01.011},
keywords = {irace}
}
@article{DelKar1990interactive,
title = {An interactive {MCDM} weight space reduction method utilizing
a {Tchebycheff} utility function},
author = {Dell, Robert F. and Karwan, Mark H.},
journal = {Naval Research Logistics},
volume = 37,
number = 2,
pages = {263--277},
year = 1990
}
@article{DelTru1993,
author = {Mauro Dell'Amico and Marco Trubian},
title = {Applying Tabu Search to the Job Shop Scheduling Problem},
journal = {Annals of Operations Research},
year = 1993,
volume = 41,
pages = {231--252}
}
@article{DemEichFli2015effects,
title = {On the effects of combining objectives in multi-objective
optimization},
author = {Dempe, Stephan and Eichfelder, Gabriele and Fliege, J{\"o}rg},
journal = {Mathematical Methods of Operations Research},
volume = 82,
number = 1,
pages = {1--18},
year = 2015,
publisher = {Springer}
}
@article{DenAroGosPas1990,
author = { Jean-Louis Deneubourg and S. Aron and S. Goss and
J.-M. Pasteels},
title = {The Self-Organizing Exploratory Pattern of the
{Argentine} Ant},
journal = {Journal of Insect Behavior},
year = 1990,
volume = 3,
number = 2,
pages = {159--168},
doi = {10.1007/BF01417909}
}
@article{DenZha2019approxhv,
author = {Deng, Jingda and Zhang, Qingfu },
title = {Approximating Hypervolume and Hypervolume Contributions Using
Polar Coordinate},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2019,
volume = 23,
number = 5,
pages = {913--918},
month = oct,
annote = {Proposed approximating the hypervolume using scalarizations},
doi = {10.1109/tevc.2019.2895108}
}
@article{DerGarMolHer2011stats,
title = {A practical tutorial on the use of nonparametric statistical
tests as a methodology for comparing evolutionary and swarm
intelligence algorithms},
author = {Derrac, Joaqu{\'i}n and Garc{\'i}a, Salvador and Daniel Molina and Francisco Herrera },
journal = {Swarm and Evolutionary Computation},
volume = 1,
number = 1,
pages = {3--18},
year = 2011
}
@article{DerVog2014:joh,
author = {Ulrich Derigs and Ulrich Vogel},
title = {Experience with a Framework for Developing Heuristics for
Solving Rich Vehicle Routing Problems},
journal = {Journal of Heuristics},
year = 2014,
volume = 20,
number = 1,
pages = {75--106}
}
@article{DesBelDop2021bops,
author = {Aryan Deshwal and Syrine Belakaria and Janardhan Rao Doppa
and Dae Hyun Kim},
title = {Bayesian Optimization over Permutation Spaces},
journal = {Arxiv preprint arXiv:2112.01049},
year = 2021,
doi = {10.48550/arXiv.2112.01049},
keywords = {BOPS, CEGO}
}
@article{DesRitLopPer2021acviz,
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}}},
journal = {Operations Research Perspectives},
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.},
volume = 8,
pages = 100186
}
@article{DetPapZab2017omega,
title = {A multi-depot dial-a-ride problem with heterogeneous vehicles
and compatibility constraints in healthcare},
author = {Paolo Detti and Francesco Papalini and Garazi Zabalo {Manrique
de Lara}},
journal = {Omega},
volume = 70,
pages = {1--14},
year = 2017,
doi = {10.1016/j.omega.2016.08.008}
}
@article{DevVoh2003informs,
title = {Combinatorial Auctions: A Survey},
author = { Sven {De Vries} and Rakesh V. Vohra },
journal = {INFORMS Journal on Computing},
volume = 15,
number = 3,
pages = {284--309},
year = 2003,
publisher = {{INFORMS}}
}
@article{DiaHanXu2017,
title = {Evolutionary robust optimization in production planning:
interactions between number of objectives, sample size and
choice of robustness measure},
journal = {Computers \& Operations Research},
volume = 79,
pages = {266--278},
year = 2017,
doi = {10.1016/j.cor.2016.06.020},
author = { Juan Esteban Diaz and Julia Handl and Dong-Ling Xu },
keywords = {Evolutionary multi-objective optimization, Production
planning, Robust optimization, Simulation-based optimization,
Uncertainty modelling}
}
@article{DiaHanXu2018,
title = {Integrating meta-heuristics, simulation and exact techniques
for production planning of a failure-prone manufacturing
system},
journal = {European Journal of Operational Research},
volume = 266,
number = 3,
pages = {976--989},
year = 2018,
issn = {0377-2217},
doi = {10.1016/j.ejor.2017.10.062},
author = { Juan Esteban Diaz and Julia Handl and Dong-Ling Xu },
keywords = {Genetic algorithms, Combinatorial optimization, Production
planning, Simulation-based optimization, Uncertainty
modelling}
}
@article{DiaLop2020ejor,
author = { Juan Esteban Diaz and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Incorporating Decision-Maker's Preferences into the Automatic
Configuration of Bi-Objective Optimisation Algorithms},
journal = {European Journal of Operational Research},
year = 2021,
volume = 289,
number = 3,
pages = {1209--1222},
doi = {10.1016/j.ejor.2020.07.059},
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}
}
@article{DiaMouFigCli2002:ejor,
author = { L. C. Dias and Vincent Mousseau and Jos{\'e} Rui Figueira and J. N. Cl{\'i}maco },
title = {An aggregation/disaggregation approach to obtain robust conclusions with {ELECTRE TRI}},
year = 2002,
journal = {European Journal of Operational Research},
volume = 138,
number = 2,
month = apr,
pages = {332--348 }
}
@article{DiaYan2009small,
title = {Small approximate {Pareto} sets for biobjective shortest
paths and other problems},
author = {Diakonikolas, Ilias and Mihalis Yannakakis },
journal = {SIAM Journal on Computing},
year = 2009,
number = 4,
pages = {1340--1371},
volume = 39
}
@article{DicDor1998:jair,
author = { Gianni A. {Di Caro} and Marco Dorigo },
title = {Ant{Net}: Distributed Stigmergetic Control for Communications
Networks},
journal = {Journal of Artificial Intelligence Research},
volume = 9,
pages = {317--365},
year = 1998
}
@article{DicDucGam2005,
author = { Gianni A. {Di Caro} and F. Ducatelle and L. M. Gambardella },
title = {{AntHocNet}: An adaptive nature-inspired algorithm for routing in mobile ad hoc networks},
journal = {European Transactions on Telecommunications},
year = 2005,
volume = 16,
number = 5,
pages = {443--455}
}
@article{DigSch2003,
author = {Luca {Di Gaspero} and Andrea Schaerf},
citations = 36,
journal = {Software --- Practice \& Experience},
keywords = {software engineering, local search, easylocal},
month = jul,
number = 8,
pages = {733--765},
publisher = {John Wiley \& Sons},
title = {\textsc{EasyLocal++}: An object-oriented framework
for flexible design of local search algorithms},
epub = {http://www.diegm.uniud.it/satt/papers/DiSc03.pdf},
volume = 33,
year = 2003
}
@article{DilKhaNem2017comments,
title = {Comments on: On learning and branching: a survey},
author = {Dilkina, Bistra and Khalil, Elias B. and Nemhauser, George
L.},
journal = {TOP},
volume = 25,
pages = {242--246},
year = 2017,
publisher = {Springer},
annote = {Comments on \cite{LodZar2017learning}.}
}
@article{DinDonHeLi2019twoarch,
title = {A novel two-archive strategy for evolutionary many-objective
optimization algorithm based on reference points},
author = {Ding, Rui and Dong, Hongbin and He, Jun and Li, Tao},
journal = {Applied Soft Computing},
year = 2019,
pages = {447--464},
volume = 78,
doi = {10.1016/j.asoc.2019.02.040},
publisher = {Elsevier}
}
@article{DinSonGup2015,
author = {Ding, J.-Y. and Song, S. and Gupta, J. N. D. and Zhang, R. and Chiong, R. and Wu, C.},
title = {An Improved Iterated Greedy Algorithm with a Tabu-based Reconstruction Strategy for the No-wait Flowshop Scheduling Problem},
journal = {Applied Soft Computing},
year = 2015,
volume = 30,
pages = {604--613}
}
@article{DoeDoeEbe2015,
author = { Benjamin Doerr and Carola Doerr and Franziska Ebel},
title = {From black-box complexity to designing new genetic algorithms},
journal = {Theoretical Computer Science},
volume = 567,
pages = {87--104},
year = 2015,
doi = {10.1016/j.tcs.2014.11.028}
}
@article{DoeDoeYan2020,
author = { Benjamin Doerr and Carola Doerr and Yang, Jing},
title = {Optimal parameter choices via precise black-box analysis},
journal = {Theoretical Computer Science},
volume = 801,
pages = {1--34},
year = 2020,
doi = {10.1016/j.tcs.2019.06.014}
}
@article{DoeFueGro06,
author = { Karl F. Doerner and Guenther Fuellerer and Manfred Gronalt and Richard F. Hartl and Manuel Iori },
title = {Metaheuristics for the Vehicle Routing Problem with Loading Constraints},
journal = {Networks},
year = 2006,
volume = 49,
number = 4,
pages = {294--307}
}
@article{DoeGieWitYan2019,
title = {The ({1+\(\lambda\)}) evolutionary
algorithm with self-adjusting mutation rate},
author = { Benjamin Doerr and Gie{\ss}en, Christian and Carsten Witt and Yang, Jing},
journal = {Algorithmica},
volume = 81,
number = 2,
pages = {593--631},
year = 2019,
publisher = {Springer}
}
@article{DoeGutHar08,
author = { Karl F. Doerner and Gutjahr, Walter J. and Richard F. Hartl and Christine Strauss and Christian Stummer },
title = {Nature-Inspired Metaheuristics in Multiobjective
Activity Crashing},
journal = {Omega},
year = 2008,
volume = 36,
number = 6,
pages = {1019--1037}
}
@article{DoeGutHarStrStu04:aor,
author = { Karl F. Doerner and Gutjahr, Walter J. and Richard F. Hartl and Christine Strauss and Christian Stummer },
title = {{Pareto} Ant Colony Optimization: A Metaheuristic
Approach to Multiobjective Portfolio Selection},
journal = {Annals of Operations Research},
year = 2004,
volume = 131,
pages = {79--99}
}
@article{DoeGutHarStrStu06:ejor,
author = { Karl F. Doerner and Gutjahr, Walter J. and Richard F. Hartl and Christine Strauss and Christian Stummer },
title = {{Pareto} ant colony optimization with ILP preprocessing in
multiobjective project portfolio selection},
journal = {European Journal of Operational Research},
year = 2006,
volume = 171,
pages = {830--841}
}
@article{DoeHarRei03,
author = { Karl F. Doerner and Richard F. Hartl and Marc Reimann },
title = {Are {COMPETants} more competent for problem solving?
{The} case of a multiple objective transportation
problem},
journal = {Central European Journal for Operations Research and Economics},
pages = {115--141},
volume = 11,
number = 2,
year = 2003
}
@article{DoeJohWin2012,
title = {Multiplicative drift analysis},
author = { Benjamin Doerr and Johannsen, Daniel and Winzen, Carola},
journal = {Algorithmica},
volume = 64,
number = 4,
pages = {673--697},
year = 2012
}
@article{DoeKotLenWin2013,
title = {Black-box complexities of combinatorial problems},
author = { Benjamin Doerr and K{\"o}tzing, Timo and Lengler, Johannes and Winzen, Carola},
journal = {Theoretical Computer Science},
volume = 471,
pages = {84--106},
year = 2013
}
@article{DoeMerStu2009:swarm,
author = { Karl F. Doerner and D. Merkle and Thomas St{\"u}tzle },
title = {Special issue on Ant Colony Optimization},
journal = {Swarm Intelligence},
year = 2009,
volume = 3,
number = 1
}
@article{DoeNeuSudWit2011:tcs,
author = { Benjamin Doerr and Frank Neumann and Dirk Sudholt and Carsten Witt },
title = {Runtime analysis of the 1-{ANT} ant colony optimizer},
journal = {Theoretical Computer Science},
year = 2011,
volume = 412,
number = 1,
pages = {1629--1644}
}
@article{Dog2015asoco,
author = { Do\v{g}an Ayd{\i}n },
title = {Composite artificial bee colony algorithms: From
component-based analysis to high-performing algorithms},
journal = {Applied Soft Computing},
volume = 32,
pages = {266--285},
year = 2015,
doi = {10.1016/j.asoc.2015.03.051},
keywords = {irace}
}
@article{DoiPekReg2004rank,
author = {Jean-Paul Doignon and Aleksandar Peke{\v{c}} and Michel
Regenwetter},
title = {The repeated insertion model for rankings: Missing link
between two subset choice models},
doi = {10.1007/bf02295838},
year = 2004,
month = mar,
volume = 69,
number = 1,
pages = {33--54},
journal = {Psychometrika},
abstract = {Several probabilistic models for subset choice have been
proposed in the literature, for example, to explain approval
voting data. We show that Marley et al.'s latent scale model
is subsumed by Falmagne and Regenwetter's size-independent
model, in the sense that every choice probability
distribution generated by the former can also be explained by
the latter. Our proof relies on the construction of a
probabilistic ranking model which we label the ``repeated
insertion model''. This model is a special case of Marden's
orthogonal contrast model class and, in turn, includes the
classical Mallows $\varphi$-model as a special case. We
explore its basic properties as well as its relationship to
Fligner and Verducci's multistage ranking model.}
}
@article{DolMor2002benchmarking,
author = {Dolan, Elizabeth D. and Mor{\'e}, Jorge J.},
journal = {Mathematical Programming},
number = 2,
pages = {201--213},
title = {Benchmarking optimization software with performance profiles},
volume = 91,
year = 2002,
keywords = {performance profiles; convergence},
annote = {This methodology has been criticized in \url{https://doi.org/10.1145/2950048}}
}
@article{DonCheHua2013,
author = {Xingye Dong and Ping and Houkuan Huang and Maciek Nowak},
title = {A Multi-restart Iterated Local Search Algorithm for the Permutation Flow Shop Problem Minimizing Total Flow Time},
journal = {Computers \& Operations Research},
year = 2013,
volume = 40,
number = 2,
pages = {627--632}
}
@article{DonHuaChe2009:cor,
author = {X. Dong and H. Huang and P. Chen},
title = {An Iterated Local Search Algorithm for the Permutation Flowshop Problem with Total Flowtime Criterion},
journal = {Computers \& Operations Research},
year = 2009,
volume = 36,
number = 5,
pages = {1664--1669}
}
@article{DonMonCasRizGam08,
author = {A. V. Donati and Roberto Montemanni and N. Casagrande and A. E. Rizzoli and L. M. Gambardella },
title = {Time dependent vehicle routing problem with a multi ant
colony system},
journal = {European Journal of Operational Research},
volume = 185,
number = 3,
year = 2008,
pages = {1174--1191}
}
@article{Dor2007:scholarpedia,
author = { Marco Dorigo },
title = {Ant {Colony} {Optimization}},
year = 2007,
journal = {Scholarpedia},
volume = 2,
number = 3,
pages = 1461,
doi = {10.4249/scholarpedia.1461}
}
@article{Dor2016sipolicy,
author = { Marco Dorigo },
title = {Swarm intelligence: A few things you need to know if you want
to publish in this journal},
journal = {Swarm Intelligence},
year = 2016,
month = nov,
url = {https://static.springer.com/sgw/documents/1593723/application/pdf/Additional_submission_instructions.pdf}
}
@article{DorBirLiLop2017si,
author = { Marco Dorigo and Mauro Birattari and Li, Xiaodong and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Kazuhiro Ohkura and Carlo Pinciroli and Thomas St{\"u}tzle },
title = {{ANTS} 2016 Special Issue: Editorial},
journal = {Swarm Intelligence},
year = 2017,
month = nov,
doi = {10.1007/s11721-017-0146-5}
}
@article{DorBirStu06:ci,
author = { Marco Dorigo and Mauro Birattari and Thomas St{\"u}tzle },
title = {Ant Colony Optimization: Artificial Ants as a Computational Intelligence Technique},
journal = {IEEE Computational Intelligence Magazine},
year = 2006,
volume = 1,
number = 4,
pages = {28--39}
}
@article{DorBlu2005:tcs,
author = { Marco Dorigo and Christian Blum },
title = {Ant colony optimization theory: A survey},
journal = {Theoretical Computer Science},
volume = 344,
number = {2-3},
year = 2005,
pages = {243--278}
}
@article{DorDicGam99:al,
author = { Marco Dorigo and Gianni A. {Di Caro} and L. M. Gambardella },
title = {Ant Algorithms for Discrete Optimization},
journal = {Artificial Life},
volume = 5,
number = 2,
pages = {137--172},
year = 1999
}
@article{DorGam1997:biosys,
author = { Marco Dorigo and L. M. Gambardella },
title = {Ant Colonies for the Traveling Salesman Problem},
journal = {BioSystems},
year = 1997,
volume = 43,
number = 2,
pages = {73--81},
doi = {10.1016/S0303-2647(97)01708-5}
}
@article{DorGam1997:tec,
key = {DorGam97:tec},
author = { Marco Dorigo and L. M. Gambardella },
title = {{Ant} {Colony} {System}: A Cooperative Learning
Approach to the Traveling Salesman Problem},
journal = {IEEE Transactions on Evolutionary Computation},
year = 1997,
volume = 1,
number = 1,
pages = {53--66},
keywords = {Ant Colony System}
}
@article{DorGamMidStu2002:tec,
doi = {10.1109/TEVC.2002.802446},
author = { Marco Dorigo and L. M. Gambardella and Martin Middendorf and Thomas St{\"u}tzle },
title = {Guest Editorial: Special Section on Ant Colony
Optimization},
year = 2002,
journal = {IEEE Transactions on Evolutionary Computation},
volume = 6,
number = 4,
pages = {317--320},
keywords = {ant colony optimization, swarm intelligence}
}
@article{DorManCol1996:tsmcb,
author = { Marco Dorigo and Vittorio Maniezzo and Alberto Colorni },
title = {{Ant} {System}: Optimization by a Colony of
Cooperating Agents},
journal = {IEEE Transactions on Systems, Man, and Cybernetics -- Part B},
year = 1996,
volume = 26,
number = 1,
pages = {29--41}
}
@article{DorStuDic2000:fgcs,
author = { Marco Dorigo and Thomas St{\"u}tzle and Gianni A. {Di Caro} },
title = {Special Issue on ``{Ant} {Algorithms}''},
year = 2000,
journal = {Future Generation Computer Systems},
volume = 16,
number = 8,
keywords = {swarm intelligence, ant colony optimization}
}
@article{DouZop2010:ejor,
author = { Michael Doumpos and Constantin Zopounidis },
title = {Preference disaggregation and statistical learning for multicriteria decision support: A review},
journal = {European Journal of Operational Research},
volume = 209,
number = 3,
pages = {203--214},
year = 2011
}
@article{DovTusFil2010ec,
author = {Erik Dovgan and Tea Tu{\v s}ar and Bogdan Filipi{\v c}},
title = {Parameter tuning in an evolutionary algorithm for commodity
transportation optimization},
journal = {Evolutionary Computation},
year = 2010,
pages = {1--8}
}
@article{DreSia2004,
author = { Johann Dr{\'e}o and P. Siarry},
title = {Continuous interacting ant colony algorithm based on dense
heterarchy},
journal = {Future Generation Computer Systems},
volume = 20,
number = 5,
year = 2004,
pages = {841--856}
}
@article{DroJanWeg2006,
title = {Upper and lower bounds for randomized search heuristics in black-box optimization},
author = {Droste, Stefan and Jansen, Thomas and Ingo Wegener },
journal = {Theory of Computing Systems},
volume = 39,
number = 4,
pages = {525--544},
year = 2006,
publisher = {Springer}
}
@article{DruThi2012,
author = { M{\u{a}}d{\u{a}}lina M. Drugan and Dirk Thierens },
title = {Stochastic {Pareto} local search: {Pareto} neighbourhood
exploration and perturbation strategies},
journal = {Journal of Heuristics},
volume = 18,
number = 5,
year = 2012,
pages = {727--766}
}
@article{DuLeu90,
author = { J. Du and Joseph Y.-T. Leung },
title = {Minimizing Total Tardiness on One Machine is
{NP}-Hard},
journal = {Mathematics of Operations Research},
year = 1990,
volume = 15,
number = 3,
pages = {483--495}
}
@article{DubLopStu2011amai,
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},
journal = {Annals of Mathematics and Artificial Intelligence},
year = 2011,
volume = 61,
number = 2,
pages = {125--154},
doi = {10.1007/s10472-011-9235-0}
}
@article{DubLopStu2011cor,
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},
journal = {Computers \& Operations Research},
year = 2011,
volume = 38,
number = 8,
pages = {1219--1236},
doi = {10.1016/j.cor.2010.10.008},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2010-001/}
}
@article{DubLopStu2015ejor,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Anytime {Pareto} Local Search},
journal = {European Journal of Operational Research},
year = 2015,
volume = 243,
number = 2,
pages = {369--385},
doi = {10.1016/j.ejor.2014.10.062},
keywords = {Pareto local search}
}
@article{DubPagStu2017cor,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Federico Pagnozzi and Thomas St{\"u}tzle },
title = {An Iterated Greedy Algorithm with Optimization of Partial
Solutions for the Permutation Flowshop Problem},
journal = {Computers \& Operations Research},
year = 2017,
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2013-006},
volume = 81,
pages = {160--166},
doi = {10.1016/j.cor.2016.12.021}
}
@article{Dud2008carbodies,
author = {Fabian Duddeck},
title = {Multidisciplinary optimization of car bodies},
journal = {Structural and Multidisciplinary Optimization},
year = 2008,
volume = 35,
number = 4,
pages = {375--389},
doi = {10.1007/s00158-007-0130-6},
annote = {Evolutionary optimization of car bodies at General Motors}
}
@article{DueSch90,
author = { Gunter Dueck and T. Scheuer},
title = {Threshold Accepting: A General Purpose Optimization
Algorithm Appearing Superior to Simulated Annealing},
journal = {Journal of Computational Physics},
year = 1990,
volume = 90,
number = 1,
pages = {161--175}
}
@article{Dueck1993,
title = {New Optimization Heuristics: the Great Deluge Algorithm and the Record-To-Record Travel},
author = { Gunter Dueck },
journal = {Journal of Computational Physics},
volume = 104,
number = 1,
pages = {86--92},
year = 1993,
publisher = {Elsevier}
}
@article{DuiBerCar2017constrained,
title = {Constrained {Bayesian} Optimization with Particle Swarms for
Safe Adaptive Controller Tuning},
journal = {{IFAC}-{PapersOnLine}},
volume = 50,
number = 1,
pages = {11800--11807},
year = 2017,
annote = {20th IFAC World Congress},
doi = {10.1016/j.ifacol.2017.08.1991},
author = {Rikky R. P. R. Duivenvoorden and Felix Berkenkamp and Nicolas
Carion and Andreas Krause and Angela P. Schoellig},
keywords = {Adaptive Control, Constrained Bayesian Optimization, Safety,
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
the field of adaptive control, where good performance is
typically only achieved after significant tuning. Recently,
it has been shown that constrained Bayesian optimization is a
promising approach to automate the tuning process without
risking system failures during the optimization
process. However, this approach is computationally too
expensive for tuning more than a couple of parameters. In
this paper, we provide a heuristic in order to efficiently
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.}
}
@article{DuiVos1999,
author = {Cees Duin and Stefan Vo{\ss} },
title = {The Pilot Method: A Strategy for Heuristic Repetition with Application to the {Steiner} Problem in Graphs},
journal = {Networks},
year = 1999,
volume = 34,
number = 3,
pages = {181--191}
}
@article{Dumas95tsptw,
author = { Y. Dumas and J. Desrosiers and E. Gelinas and M. M. Solomon },
title = {An Optimal Algorithm for the Traveling Salesman Problem with
Time Windows},
journal = {Operations Research},
year = 1995,
volume = 43,
number = 2,
pages = {367--371},
doi = {10.1287/opre.43.2.367}
}
@article{Dun1964tech,
title = {Multiple Comparisons Using Rank Sums},
author = { Olive Jean Dunn },
journal = {Technometrics},
volume = 6,
number = 3,
pages = {241--252},
year = 1964,
publisher = {Taylor \& Francis Group}
}
@article{Dunn1961jasa,
title = {Multiple Comparisons Among Means},
author = { Olive Jean Dunn },
journal = {Journal of the American Statistical Association},
volume = 56,
number = 293,
pages = {52--64},
year = 1961,
publisher = {Taylor \& Francis Group}
}
@article{DurNeb2011jmetal,
title = {{jMetal}: A {Java} framework for multi-objective
optimization},
journal = {Advances in Engineering Software},
volume = 42,
number = 10,
pages = {760--771},
year = 2011,
issn = {0965-9978},
doi = {10.1016/j.advengsoft.2011.05.014},
author = { Durillo, Juan J. and Nebro, Antonio J. }
}
@article{EggLinHut2019jair,
title = {Pitfalls and best practices in algorithm configuration},
author = { Katharina Eggensperger and Marius Thomas Lindauer and Frank Hutter },
journal = {Journal of Artificial Intelligence Research},
volume = 64,
pages = {861--893},
year = 2019
}
@article{Eglese1990,
title = {Simulated Annealing: a Tool for Operational Research},
author = { Richard W. Eglese },
journal = {European Journal of Operational Research},
volume = 46,
number = 3,
pages = {271--281},
year = 1990,
publisher = {Elsevier}
}
@article{EhmCamTho2016tdvrp,
author = {Ehmke, Jan Fabian and Campbell, Ann Melissa and Barrett W. Thomas },
title = {Vehicle routing to minimize time-dependent emissions in urban
areas},
journal = {European Journal of Operational Research},
year = 2016,
volume = 251,
number = 2,
pages = {478--494},
month = jun,
doi = {10.1016/j.ejor.2015.11.034}
}
@article{Ehr2006discussion,
title = {A discussion of scalarization techniques for multiple
objective integer programming},
author = { Matthias Ehrgott },
journal = {Annals of Operations Research},
volume = 147,
number = 1,
pages = {343--360},
year = 2006,
publisher = {Springer}
}
@article{EhrGan04,
author = { Matthias Ehrgott and Xavier Gandibleux },
title = {Approximative Solution Methods for Combinatorial
Multicriteria Optimization},
journal = {TOP},
year = 2004,
volume = 12,
number = 1,
pages = {1--88}
}
@article{EhrKla1997connectedness,
author = { Matthias Ehrgott and Kathrin Klamroth },
journal = {European Journal of Operational Research},
number = 1,
pages = {159--166},
title = {Connectedness of Efficient Solutions in Multiple Criteria
Combinatorial Optimization},
volume = 97,
year = 1997,
doi = {10.1016/S0377-2217(96)00116-6}
}
@article{EibHinMich1999tec,
author = { Agoston E. Eiben and Robert Hinterding and Zbigniew Michalewicz },
title = {Parameter Control in Evolutionary Algorithms},
journal = {IEEE Transactions on Evolutionary Computation},
volume = 3,
number = 2,
pages = {124--141},
year = 1999
}
@article{EibRud1999,
title = {Theory of evolutionary algorithms: A bird's eye view},
author = { Agoston E. Eiben and G{\"u}nther Rudolph },
journal = {Theoretical Computer Science},
volume = 229,
number = {1-2},
pages = {3--9},
year = 1999
}
@article{EibSmi2011swec,
author = { Agoston E. Eiben and Smit, Selmar K. },
title = {Parameter Tuning for Configuring and Analyzing
Evolutionary Algorithms},
journal = {Swarm and Evolutionary Computation},
year = 2011,
volume = 1,
number = 1,
pages = {19--31},
doi = {10.1016/j.swevo.2011.02.001}
}
@article{EkeKwa2018ems,
title = {Including robustness considerations in the search phase of
Many-Objective Robust Decision Making},
author = {Eker, Sibel and Kwakkel, Jan H. },
journal = {Environmental Modelling \& Software},
volume = 105,
pages = {201--216},
year = 2018,
keywords = {scenario-based}
}
@article{Elm1991rnn,
title = {Distributed representations, simple recurrent networks, and
grammatical structure},
author = {Elman, Jeffrey L},
journal = {Machine Learning},
volume = 7,
number = {2-3},
pages = {195--225},
year = 1991,
publisher = {Springer}
}
@article{EmePer1991,
author = {V. A. Emelichev and V. A. Perepelitsa},
title = {Complexity of Vector Optimization Problems on Graphs},
journal = {Optimization},
year = 1991,
volume = 22,
number = 6,
doi = {10.1080/02331939108843732},
pages = {906--918}
}
@article{EmePer1992,
author = {V. A. Emelichev and V. A. Perepelitsa},
title = {On the Cardinality of the Set of Alternatives in Discrete Many-criterion Problems},
journal = {Discrete Mathematics and Applications},
year = 1992,
volume = 2,
number = 5,
pages = {461--471}
}
@article{EmmDeuw2018tutorial,
title = {A tutorial on multiobjective optimization: Fundamentals and
evolutionary methods},
author = { Emmerich, Michael T. M. and Andr{\'{e}} H. Deutz },
journal = {Natural Computing},
year = 2018,
number = 3,
pages = {585--609},
volume = 17,
publisher = {Springer}
}
@article{EmmGiaNau2006,
author = { Emmerich, Michael T. M. and K. C. Giannakoglou and Boris Naujoks },
journal = {IEEE Transactions on Evolutionary Computation},
title = {Single- and multiobjective evolutionary optimization assisted
by {Gaussian} random field metamodels},
year = 2006,
volume = 10,
number = 4,
pages = {421--439},
doi = {10.1109/TEVC.2005.859463}
}
@article{EngWie2007twod,
title = {{2D} decision-making for multicriteria design optimization},
author = {Engau, Alexander and Margaret M. Wiecek },
journal = {Structural and Multidisciplinary Optimization},
volume = 34,
pages = {301--315},
year = 2007,
doi = {10.1007/s00158-006-0078-y},
publisher = {Springer}
}
@article{EngWie2008interactive,
title = {Interactive coordination of objective decompositions in
multiobjective programming},
author = {Engau, Alexander and Margaret M. Wiecek },
journal = {Management Science},
volume = 54,
number = 7,
pages = {1350--1363},
year = 2008,
publisher = {{INFORMS}}
}
@article{EssMatDau2008,
author = {Imen Essafi and Yazid Mati and St{\'e}phane Dauz{\`e}re-P{\`e}r{\'e}s },
title = {A Genetic Local Search Algorithm for Minimizing Total Weighted
Tardiness in the Job-shop Scheduling Problem},
journal = {Computers \& Operations Research},
year = 2008,
volume = 35,
number = 8,
pages = {2599--2616}
}
@article{FanBif2013,
author = {Fan, Wei and Bifet, Albert},
title = {Mining big data: current status, and forecast to the future},
journal = {{ACM} {SIGKDD} Explorations Newsletter},
year = 2013,
volume = 14,
number = 2,
pages = {1--5},
publisher = {ACM}
}
@article{Fanelli2012,
author = {Fanelli, Daniele},
title = {Negative results are disappearing from most disciplines and
countries},
journal = {Scientometrics},
year = 2012,
volume = 90,
number = 3,
pages = {891--904},
doi = {10.1007/s11192-011-0494-7},
abstract = {Concerns that the growing competition for funding and
citations might distort science are frequently discussed, but
have not been verified directly. Of the hypothesized
problems, perhaps the most worrying is a worsening of
positive-outcome bias. A system that disfavours negative
results not only distorts the scientific literature directly,
but might also discourage high-risk projects and pressure
scientists to fabricate and falsify their data. This study
analysed over 4,600 papers published in all disciplines
between 1990 and 2007, measuring the frequency of papers
that, having declared to have ``tested'' a hypothesis,
reported a positive support for it. The overall frequency of
positive supports has grown by over 22{\%} between 1990 and
2007, with significant differences between disciplines and
countries. The increase was stronger in the social and some
biomedical disciplines. The United States had published, over
the years, significantly fewer positive results than Asian
countries (and particularly Japan) but more than European
countries (and in particular the United
Kingdom). Methodological artefacts cannot explain away these
patterns, which support the hypotheses that research is
becoming less pioneering and/or that the objectivity with
which results are produced and published is decreasing.}
}
@article{FarBinResFal2005tps,
author = { Faria, Jr, H. and S. Binato and Mauricio G. C. Resende and D. J. Falc{\~a}o },
title = {Power transmission network design by a greedy randomized
adaptive path relinking approach},
journal = {IEEE Transactions on Power Systems},
year = 2005,
volume = 20,
number = 1,
pages = {43--49}
}
@article{FarMarYan2015pltoolbox,
author = {Farrugia, Vincent E. and Mart{\'i}nez, H{\'e}ctor P. and
Yannakakis, Georgios N.},
title = {The Preference Learning Toolbox},
journal = {Arxiv preprint arXiv:1506.01709},
year = 2015,
doi = {10.48550/arXiv.1506.01709}
}
@article{FarWalSav2006hydroinf,
author = {R. Farmani and Godfrey A. Walters and Dragan A. Savic },
title = {Evolutionary multi-objective optimization of the
design and operation of water distribution network:
total cost vs. reliability vs. water quality},
journal = { Journal of Hydroinformatics },
year = 2006,
volume = 8,
number = 3,
pages = {165--179}
}
@article{FavMorPel06tsptw,
author = { D. Favaretto and E. Moretti and Paola Pellegrini },
title = {Ant colony system approach for variants of the
traveling salesman problem with time windows},
journal = {Journal of Information and Optimization Sciences},
year = 2006,
volume = 27,
number = 1,
pages = {35--54}
}
@article{FavMorPel07,
author = { D. Favaretto and E. Moretti and Paola Pellegrini },
title = {Ant Colony System for a {VRP} with Multiple Time Windows and Multiple Visits},
journal = {Journal of Interdisciplinary Mathematics},
year = 2007,
volume = 10,
number = 2,
pages = {263--284}
}
@article{FawHoos2015ablation,
title = {Analysing Differences Between Algorithm Configurations
through Ablation},
author = { Chris Fawcett and Holger H. Hoos },
journal = {Journal of Heuristics},
pages = {431--458},
volume = 22,
number = 4,
year = 2016
}
@article{FeoRes1989,
author = { T. A. Feo and Mauricio G. C. Resende },
title = {A Probabilistic Heuristic for a Computationally Difficult Set
Covering Problem},
journal = {Operations Research Letters},
year = 1989,
volume = 8,
number = 2,
pages = {67--71},
annote = {Proposed GRASP}
}
@article{FeoRes1995,
author = { T. A. Feo and Mauricio G. C. Resende },
title = {Greedy Randomized Adaptive Search Procedures},
journal = {Journal of Global Optimization},
year = 1995,
volume = 6,
number = 2,
pages = {109--113}
}
@article{FeoResSmi1994,
author = { T. A. Feo and Mauricio G. C. Resende and S. H. Smith},
title = {A Greedy Randomized Adaptive Search Procedure for Maximum
Independent Set},
journal = {Operations Research},
year = 1994,
volume = 42,
pages = {860--878},
month = oct,
keywords = {GRASP}
}
@article{FerFra2014,
title = {On Insertion Tie-breaking Rules in Heuristics for the Permutation Flowshop Scheduling Problem},
author = { Victor Fernandez-Viagas and Jose M. Frami{\~n}{\'a}n },
journal = {Computers \& Operations Research},
year = 2014,
pages = {60--67},
volume = 45
}
@article{FerFra2017,
author = { Victor Fernandez-Viagas and Jose M. Frami{\~n}{\'a}n },
title = {A Beam-search-based Constructive Heuristic for the {PFSP} to Minimise
Total Flowtime},
journal = {Computers \& Operations Research},
year = 2017,
volume = 81,
pages = {167--177}
}
@article{FerFra2018,
author = { Victor Fernandez-Viagas and Jose M. Frami{\~n}{\'a}n },
title = {Iterated-greedy-based algorithms with beam search initialization for the permutation flowshop to minimise total tardiness},
journal = {Expert Systems with Applications},
year = 2018,
volume = 94,
pages = {58--69}
}
@article{FerGarAl2016mpe,
author = { Javier Ferrer and Jos{\'e} Garc{\'i}a-Nieto and Alba, Enrique and Chicano, Francisco },
doi = {10.1155/2016/3871046},
journal = {Mathematical Problems in Engineering},
pages = {1--19},
title = {Intelligent Testing of Traffic Light Programs: Validation in
Smart Mobility Scenarios},
volume = 2016,
year = 2016
}
@article{FerGuiRamJua2016,
author = {Alberto Ferrer and Daniel Guimarans and Helena {Ramalhinho Louren{\c c}o} and Angel A. Juan},
title = {A {BRILS} Metaheuristic for Non-smooth Flow-shop Problems with Failure-risk
Costs},
journal = {Expert Systems with Applications},
year = 2016,
volume = 44,
pages = {177--186}
}
@article{FerLopAlb2019asoc,
author = { Javier Ferrer and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Alba, Enrique },
title = {Reliable Simulation-Optimization of Traffic Lights in a
Real-World City},
journal = {Applied Soft Computing},
year = 2019,
volume = 78,
pages = {697--711},
doi = {10.1016/j.asoc.2019.03.016},
supplement = {https://github.com/MLopez-Ibanez/irace-sumo}
}
@article{FerNavBer2009:ejor,
author = { Eduardo Fernandez and Jorge Navarro and Sergio Bernal },
title = {Multicriteria Sorting Using a Valued Indifference Relation Under a Preference Disaggregation Paradigm},
journal = {European Journal of Operational Research},
volume = 198,
number = 2,
pages = {602--609},
year = 2009
}
@article{FerRuiFra2016,
author = { Victor Fernandez-Viagas and Rub{\'e}n Ruiz and Jose M. Frami{\~n}{\'a}n },
title = {A New Vision of Approximate Methods for the Permutation Flowshop to Minimise Makespan: State-of-the-art and Computational Evaluation},
journal = {European Journal of Operational Research},
volume = 257,
number = 3,
pages = {707--721},
year = 2017
}
@article{FerUrr2010do,
author = {R. {Ferreira da Silva} and S. Urrutia},
title = {A {General} {VNS} Heuristic for the Traveling Salesman
Problem with Time Windows},
journal = {Discrete Optimization},
year = 2010,
volume = 7,
number = 4,
pages = {203--211},
keywords = {TSPTW, GVNS},
doi = {10.1016/j.disopt.2010.04.002}
}
@article{FerValFra2018,
author = { Victor Fernandez-Viagas and Jorge M. S. Valente and Jose M. Frami{\~n}{\'a}n },
title = {Iterated-greedy-based algorithms with Beam Search Initialization for the Permutation Flowshop to Minimise Total Tardiness},
journal = {Expert Systems with Applications},
volume = 94,
pages = {58--69},
year = 2018
}
@article{FiaDaCSchSeb2010:amai,
author = { {\'A}lvaro Fialho and Luis {Da Costa} and Marc Schoenauer and Mich{\`e}le Sebag },
title = {Analyzing Bandit-based Adaptive Operator Selection
Mechanisms},
journal = {Annals of Mathematics and Artificial Intelligence},
year = 2010,
volume = 60,
number = {1--2},
pages = {25--64}
}
@article{Fie2000:siamo,
author = { Mark J. Fielding },
title = {Simulated Annealing with an Optimal Fixed Temperature},
journal = {SIAM Journal on Optimization},
year = 2000,
volume = 11,
number = 2,
pages = {289--307}
}
@article{FieEveSing2003tec,
title = {Using unconstrained elite archives for multiobjective
optimization},
author = { Jonathan E. Fieldsend and Everson, Richard M. and Singh, Sameer},
journal = {IEEE Transactions on Evolutionary Computation},
volume = 7,
number = 3,
pages = {305--323},
year = 2003,
doi = {10.1109/TEVC.2003.810733}
}
@article{FigFonHalKla2017easy,
title = {Easy to say they are Hard, but Hard to see they are
Easy-Towards a Categorization of Tractable Multiobjective
Combinatorial Optimization Problems},
author = { Jos{\'e} Rui Figueira and Carlos M. Fonseca and Halffmann, Pascal and Kathrin Klamroth and Lu{\'i}s Paquete and Ruzika, Stefan and Schulze,
Britta and Stiglmayr, Michael and Willems, David},
journal = {Journal of Multi-Criteria Decision Analysis},
volume = 24,
number = {1-2},
pages = {82--98},
year = 2017,
publisher = {Wiley Online Library},
doi = {10.1002/mcda.1574}
}
@article{FisBar2020improv,
author = { Andreas Fischbach and Thomas Bartz-Beielstein },
title = {Improving the reliability of test functions generators},
journal = {Applied Soft Computing},
year = 2020,
volume = 92,
pages = 106315
}
@article{FisGloLod2005,
title = {The feasibility pump},
author = { Matteo Fischetti and Fred Glover and Andrea Lodi },
journal = {Mathematical Programming},
volume = 104,
number = 1,
pages = {91--104},
year = 2005,
publisher = {Springer}
}
@article{FisLod2003:mp,
author = { Matteo Fischetti and Andrea Lodi },
title = {Local Branching},
journal = {Mathematical Programming Series B},
year = 2003,
volume = 98,
pages = {23--47}
}
@article{FisMon2014joh,
title = {Proximity search for 0-1 mixed-integer convex programming},
author = { Matteo Fischetti and Monaci, Michele },
journal = {Journal of Heuristics},
volume = 20,
number = 6,
pages = {709--731},
year = 2014,
publisher = {Springer}
}
@article{FisMon2014or,
author = { Matteo Fischetti and Monaci, Michele },
title = {Exploiting Erraticism in Search},
journal = {Operations Research},
volume = 62,
number = 1,
pages = {114--122},
year = 2014,
doi = {10.1287/opre.2013.1231},
annote = {\url{http://mat.tepper.cmu.edu/blog/?p=1695}},
abstract = { High sensitivity to initial conditions is generally viewed
as a drawback of tree search methods because it leads to
erratic behavior to be mitigated somehow. In this paper we
investigate the opposite viewpoint and consider this behavior
as an opportunity to exploit. Our working hypothesis is that
erraticism is in fact just a consequence of the exponential
nature of tree search that acts as a chaotic amplifier, so it
is largely unavoidable. We propose a bet-and-run approach to
actually turn erraticism to one's advantage. The idea is to
make a number of short sample runs with randomized initial
conditions, to bet on the "most promising" run selected
according to certain simple criteria, and to bring it to
completion. Computational results on a large testbed of mixed
integer linear programs from the literature are presented,
showing the potential of this approach even when embedded in
a proof-of-concept implementation. }
}
@article{FisMonSal2012:or,
author = { Matteo Fischetti and Monaci, Michele and Domenico Salvagnin},
title = {Three Ideas for the Quadratic Assignment Problem},
journal = {Operations Research},
year = 2012,
volume = 60,
number = 4,
pages = {954--964}
}
@article{FisSal2009,
title = {Feasibility pump 2.0},
author = { Matteo Fischetti and Salvagnin, Domenico},
journal = {Mathematical Programming Computation},
volume = 1,
number = {2--3},
pages = {201--222},
year = 2009,
publisher = {Springer}
}
@article{Fle1970bfgs,
author = {Fletcher, Roger},
title = {A new approach to variable metric algorithms},
journal = {The Computer Journal},
year = 1970,
volume = 13,
number = 3,
pages = {317--322},
month = sep,
annote = {One of the four papers that proposed BFGS.},
doi = {10.1093/comjnl/13.3.317},
eprint = {https://academic.oup.com/comjnl/article-pdf/13/3/317/988678/130317.pdf},
keywords = {BFGS}
}
@article{FleGlo99,
title = {Improved constructive multistart strategies for the
quadratic assignment problem using adaptive memory},
author = {Fleurent, Charles and Fred Glover },
journal = {INFORMS Journal on Computing},
volume = 11,
number = 2,
pages = {198--204},
year = 1999
}
@article{Fli2007effects,
title = {The effects of adding objectives to an optimisation problem
on the solution set},
author = {Fliege, J{\"o}rg},
journal = {Operations Research Letters},
volume = 35,
number = 6,
pages = {782--790},
year = 2007,
publisher = {Elsevier}
}
@article{FliVer1986,
title = {Distance Based Ranking Models},
author = {Fligner, Michael A. and Verducci, Joseph S.},
journal = {Journal of the Royal Statistical Society: Series B (Methodological)},
number = 3,
volume = 48,
pages = {359--369},
year = 1986,
keywords = {Mallows model, ranking, probabilistic models},
doi = {10.1111/j.2517-6161.1986.tb01420.x}
}
@article{Flo1956,
author = {M. M. Flood},
title = {The Travelling Salesman Problem},
journal = {Operations Research},
year = 1956,
volume = 4,
pages = {61--75}
}
@article{FloKel2010plos,
author = {Floreano, D. and Keller, L.},
journal = {PLoS Biology},
pages = {e1000292},
title = {Evolution of Adaptive Behaviour in Robots by Means
of {Darwinian} Selection},
volume = 8,
number = 1,
year = 2010,
doi = {10.1371/journal.pbio.1000292}
}
@article{FloUrz2000nn,
author = {Floreano, D. and Urzelai, J.},
title = {Evolutionary robots with on-line self-organization
and behavioral fitness},
journal = {Neural Networks},
year = 2000,
volume = 13,
number = {4-5},
pages = {431--443}
}
@article{Flores1986pragmatic,
author = {Flores, Benito E.},
title = {A pragmatic view of accuracy measurement in forecasting},
journal = {Omega},
year = 1986,
volume = 14,
number = 2,
pages = {93--98},
annote = {Proposed symmetric mean absolute percentage error (SMAPE)}
}
@article{FocLodMil02tsptw,
author = { Filippo Focacci and Andrea Lodi and Michela Milano },
title = {A Hybrid Exact Algorithm for the {TSPTW}},
journal = {INFORMS Journal on Computing},
year = 2002,
volume = 14,
pages = {403--417}
}
@article{FonFle1995ec,
title = {An overview of evolutionary algorithms in multiobjective
optimization},
author = { Carlos M. Fonseca and Peter J. Fleming },
journal = {Evolutionary Computation},
year = 1995,
number = 1,
pages = {1--16},
volume = 3,
annote = {Proposed FON benchmark problem}
}
@article{FonFle1998:tsmca,
author = { Carlos M. Fonseca and Peter J. Fleming },
title = {Multiobjective Optimization and Multiple Constraint
Handling with Evolutionary Algorithms ({II}):
{Application} Example},
journal = {IEEE Transactions on Systems, Man, and Cybernetics -- Part A},
year = 1998,
volume = 28,
number = 1,
pages = {38--44},
month = jan,
doi = {10.1109/3468.650320}
}
@article{FonFle1998:tsmca1,
author = { Carlos M. Fonseca and Peter J. Fleming },
title = {Multiobjective Optimization and Multiple Constraint
Handling with Evolutionary Algorithms ({I}): {A}
Unified Formulation},
journal = {IEEE Transactions on Systems, Man, and Cybernetics -- Part A},
year = 1998,
volume = 28,
number = 1,
pages = {26--37},
month = jan,
doi = {10.1109/3468.650319}
}
@article{ForKea2009surrogate,
author = {Forrester, Alexander I. J. and Keane, Andy J.},
title = {Recent advances in surrogate-based optimization},
journal = {Progress in Aerospace Sciences},
volume = 45,
number = {1-3},
pages = {50--79},
doi = {10.1016/j.paerosci.2008.11.001},
year = 2009,
keywords = {Kriging; Gaussian Process; EGO; Design of Experiments}
}
@article{FowGelKok2010ejor,
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.}
}
@article{Fox1993integrating,
author = { Bennett L. Fox },
title = {Integrating and accelerating tabu search, simulated
annealing, and genetic algorithms},
journal = {Annals of Operations Research},
volume = 41,
number = 2,
pages = {47--67},
year = 1993,
publisher = {Springer}
}
@article{Fra2018tutorial,
author = {Peter I. Frazier},
title = {A Tutorial on {Bayesian} Optimization},
journal = {Arxiv preprint arXiv:1807.02811},
year = 2018,
doi = {10.48550/arXiv.1807.02811}
}
@article{Fra2022:4or,
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,
doi = {10.1007/s10288-022-00511-7}
}
@article{FraBraBru2014automode,
author = {G. Francesca and M. Brambilla and
A. Brutschy and Vito Trianni and Mauro Birattari },
title = {{AutoMoDe}: A Novel Approach to the Automatic Design
of Control Software for Robot Swarms},
journal = {Swarm Intelligence},
year = 2014,
volume = 8,
number = 2,
pages = {89--112},
doi = {10.1007/s11721-014-0092-4}
}
@article{FraBraBru2015automode,
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}
}
@article{FraGupLei2004,
title = {A Review and Classification of Heuristics for Permutation Flow-shop Scheduling with Makespan Objective},
author = { Jose M. Frami{\~n}{\'a}n and Jatinder N. D. Gupta and Rainer Leisten },
journal = {Journal of the Operational Research Society},
year = 2004,
number = 12,
pages = {1243--1255},
volume = 55
}
@article{FraPerStu2018ol,
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},
journal = {Optimization Letters},
year = 2018,
volume = 12,
number = 8,
pages = {1741--1753},
doi = {10.1007/s11590-018-1240-3}
}
@article{FraSamDiC2016,
author = { Alberto Franzin and Sambo, Francesco and Di Camillo, Barbara},
title = {\rpackage{bnstruct}: an {R} package for {Bayesian} Network structure learning
in the presence of missing data},
journal = {Bioinformatics},
year = 2016,
volume = 33,
number = 8,
pages = {1250--1252}
}
@article{FraStu2019:cor,
author = { Alberto Franzin and Thomas St{\"u}tzle },
title = {Revisiting Simulated Annealing: A Component-Based Analysis},
journal = {Computers \& Operations Research},
volume = 104,
pages = {191--206},
year = 2019,
doi = {10.1016/j.cor.2018.12.015}
}
@article{FraStu2022:ejor,
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,
pages = {395--410},
doi = {10.1016/j.ejor.2022.04.014}
}
@article{FreDue2007clust,
author = {Brendan J. Frey and Delbert Dueck},
title = {Clustering by Passing Messages Between Data Points},
year = 2007,
month = feb,
publisher = {American Association for the Advancement of Science ({AAAS})},
volume = 315,
number = 5814,
pages = {972--976},
doi = {10.1126/science.1136800},
journal = {Science},
keywords = {clustering; affinity propagation}
}
@article{FreFleGui2015aggregation,
title = {Aggregation trees for visualization and dimension reduction
in many-objective optimization},
author = {de Freitas, Alan R. R. and Peter J. Fleming and Guimar{\~a}es, Frederico G.},
journal = {Information Sciences},
volume = 298,
pages = {288--314},
year = 2015,
publisher = {Elsevier}
}
@article{FriChaMar2010:ijor,
author = { Hela Frikha and Habib Chabchoub and Jean-Marc Martel },
title = {Inferring criteria's relative importance coefficients
in {PROMETHEE II}},
journal = {International Journal of Operational Research},
year = 2010,
volume = 7,
number = 2,
pages = {257--275}
}
@article{FriJoh05FFTW,
author = {Matteo Frigo and Steven G. Johnson},
title = {The Design and Implementation of {FFTW3}},
journal = {Proceedings of the IEEE},
year = 2005,
volume = 93,
number = 2,
pages = {216--231},
note = {Special issue on ``Program Generation, Optimization, and Platform Adaptation''}
}
@article{Friedman1937use,
title = {The use of ranks to avoid the assumption of normality
implicit in the analysis of variance},
author = {Friedman, Milton},
journal = {Journal of the American Statistical Association},
volume = 32,
number = 200,
pages = {675--701},
year = 1937
}
@article{FuEglLi2008,
author = {Z Fu and R Eglese and L Y O Li},
title = {A unified tabu search algorithm for vehicle routing problems with soft time windows},
journal = {Journal of the Operational Research Society},
volume = 59,
number = 5,
pages = {663--673},
year = 2008
}
@article{FueDoeHar09ejor,
author = { Guenther Fuellerer and Karl F. Doerner and Richard F. Hartl and Manuel Iori },
title = {Metaheuristics for vehicle routing problems with
three-dimensional loading constraints},
journal = {European Journal of Operational Research},
year = 2009,
volume = 201,
number = 3,
pages = {751--759},
doi = {10.1016/j.ejor.2009.03.046}
}
@article{FueDoeHarIor09,
author = { Guenther Fuellerer and Karl F. Doerner and Richard F. Hartl and Manuel Iori },
title = {Ant colony optimization for the two-dimensional
loading vehicle routing problem},
journal = {Computers \& Operations Research},
year = 2009,
volume = 36,
number = 3,
pages = {655--673}
}
@article{Fuk2008ec,
title = {Automated Discovery of Local Search Heuristics for
Satisfiability Testing},
author = { Fukunaga, Alex S. },
number = 1,
journal = {Evolutionary Computation},
month = mar,
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.}
}
@article{Fursin2011milepost,
author = {Grigori Fursin and Yuriy Kashnikov and Abdul Wahid Memon and Zbigniew Chamski and Olivier Temam and Mircea Namolaru and Elad Yom-Tov and Bilha Mendelson and Ayal Zaks and Eric Courtois and Francois Bodin and Phil Barnard and Elton Ashton and Edwin Bonilla and John Thomson and Christopher K. I. Williams and Michael O'Boyle},
title = {Milepost {GCC}: Machine Learning Enabled Self-tuning Compiler},
journal = {International Journal of Parallel Programming},
year = 2011,
volume = 39,
number = 3,
pages = {296--327},
publisher = {Springer, US},
doi = {10.1007/s10766-010-0161-2}
}
@article{GagPriGra02:jors,
author = { Caroline Gagn{\'e} and W. L. Price and M. Gravel},
title = {Comparing an {ACO} algorithm with other heuristics
for the single machine scheduling problem with
sequence-dependent setup times},
journal = {Journal of the Operational Research Society},
year = 2002,
volume = 53,
pages = {895--906}
}
@article{GagSch2007,
author = { Matteo Gagliolo and J. Schmidhuber},
title = {Learning dynamic algorithm portfolios},
journal = {Annals of Mathematics and Artificial Intelligence},
year = 2007,
volume = 47,
number = {3-4},
pages = {295--328},
doi = {10.1007/s10472-006-9036-z},
annote = {fully dynamic and online algorithm selection technique, with
no separate training phase: all candidate algorithms are run
in parallel, while a model incrementally learns their runtime
distributions.}
}
@article{GalHao1999heacol,
title = {Hybrid evolutionary algorithms for graph coloring},
author = {Galinier, Philippe and Jin-Kao Hao },
journal = {Journal of Combinatorial Optimization},
volume = 3,
number = 4,
pages = {379--397},
year = 1999,
publisher = {Springer},
doi = {10.1023/A:1009823419804}
}
@article{GalLeb1977ejor,
title = {Redundant objective functions in linear vector maximum
problems and their determination},
journal = {European Journal of Operational Research},
volume = 1,
number = 3,
pages = {176--184},
year = 1977,
doi = {10.1016/0377-2217(77)90025-X},
author = {Tomas Gal and Heiner Leberling},
abstract = {Suppose that in a multicriteria linear programming problem
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
theory which describes properties of absolute and relative
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.}
}
@article{GamDor00:informs,
author = { L. M. Gambardella and Marco Dorigo },
title = {Ant {Colony} {System} Hybridized with a New Local
Search for the Sequential Ordering Problem},
volume = 12,
number = 3,
pages = {237--255},
journal = {INFORMS Journal on Computing},
year = 2000,
anote = {IJ.26}
}
@article{GamMonWey12:ejor,
author = { L. M. Gambardella and Roberto Montemanni and Dennis Weyland },
title = {Coupling Ant Colony Systems with Strong Local Searches},
journal = {European Journal of Operational Research},
volume = 220,
number = 3,
year = 2012,
pages = {831--843},
doi = {10.1016/j.ejor.2012.02.038}
}
@article{GanJasFre2000joh,
author = { Xavier Gandibleux and Andrzej Jaszkiewicz and A. Fr{\'e}ville and Roman S{\l}owi{\'n}ski },
title = {Special Issue on {Multiple} {Objective} {Metaheuristics}},
journal = {Journal of Heuristics},
year = 2000,
volume = 6,
number = 3
}
@article{Gao2016,
author = {Gao, Kaizhou and Zhang, Yicheng and Sadollah, Ali and Su,
Rong},
doi = {10.1016/j.asoc.2016.07.029},
journal = {Applied Soft Computing},
keywords = {harmony search algorithm,traffic light scheduling},
month = nov,
pages = {359--372},
title = {Optimizing urban traffic light scheduling problem using
harmony search with ensemble of local search},
volume = 48,
year = 2016
}
@article{GaoNieLi2019visarxiv,
title = {Visualisation of {Pareto} Front Approximation: A Short Survey
and Empirical Comparisons},
author = {Gao, Huiru and Nie, Haifeng and Li, Ke},
journal = {Arxiv preprint arXiv:1903.01768},
year = 2019,
url = {http://arxiv.org/abs/1903.01768}
}
@article{GarAlbOli2012,
author = { Jos{\'e} Garc{\'i}a-Nieto and Alba, Enrique and Olivera, Ana Carolina },
journal = {Engineering Applications of Artificial Intelligence},
keywords = {Cycle program optimization,Particle swarm
optimization,Realistic traffic instances,SUMO microscopic
simulator of urban mobility,Traffic light scheduling},
month = mar,
number = 2,
pages = {274--283},
title = {Swarm intelligence for traffic light scheduling: Application
to real urban areas},
volume = 25,
year = 2012
}
@article{GarCorHer07,
author = { Carlos Garc{\'i}a-Mart{\'i}nez and Oscar Cord{\'o}n and Francisco Herrera },
title = {A taxonomy and an empirical analysis of multiple
objective ant colony optimization algorithms for the
bi-criteria {TSP}},
journal = {European Journal of Operational Research},
volume = 180,
number = 1,
year = 2007,
pages = {116--148}
}
@article{GarFer2015saferl,
title = {A comprehensive survey on safe reinforcement learning},
author = {Garc{\'i}a, Javier and Fern{\'a}ndez, Fernando},
journal = {Journal of Machine Learning Research},
volume = 16,
number = 1,
pages = {1437--1480},
year = 2015,
epub = {http://jmlr.org/papers/volume16/garcia15a/garcia15a.pdf}
}
@article{GarFerLueHer2010tests,
title = {Advanced nonparametric tests for multiple comparisons in the
design of experiments in computational intelligence and data
mining: Experimental analysis of power},
author = {Garc{\'i}a, Salvador and Fern{\'a}ndez, Alberto and Luengo,
Juli{\'a}n and Francisco Herrera },
journal = {Information Sciences},
volume = 180,
number = 10,
pages = {2044--2064},
year = 2010
}
@article{GarGloRodLozMar2014,
author = { Carlos Garc{\'i}a-Mart{\'i}nez and Fred Glover and Francisco J. Rodr{\'i}guez and Manuel Lozano and Rafael Mart{\'i} },
title = {Strategic Oscillation for the Quadratic Multiple Knapsack Problem},
journal = {Computational Optimization and Applications},
year = 2014,
volume = 58,
number = 1,
pages = {161--185}
}
@article{GarJohSet76,
author = {M. R. Garey and David S. Johnson and R. Sethi},
title = {The Complexity of Flowshop and Jobshop Scheduling},
journal = {Mathematics of Operations Research},
year = 1976,
volume = 1,
pages = {117--129}
}
@article{GarKal2001eff,
author = {Garnier, Josselin and Kallel, Leila},
title = {Efficiency of Local Search with Multiple Local Optima},
journal = {SIAM Journal Discrete Mathematics},
volume = 15,
number = 1,
pages = {122--141},
year = 2001,
doi = {10.1137/S0895480199355225}
}
@article{GarMolLoz2009joh,
author = {Garc{\'i}a, Salvador and Daniel Molina and Manuel Lozano and Francisco Herrera },
journal = {Journal of Heuristics},
number = 617,
title = {A study on the use of non-parametric tests for analyzing the
evolutionary algorithms' behaviour: a case study on the
{CEC'2005} {Special} {Session} on {Real} {Parameter}
{Optimization}},
volume = 15,
year = 2009,
pages = {617--644},
doi = {10.1007/s10732-008-9080-4}
}
@article{GarOliAlb2013tec,
author = { Jos{\'e} Garc{\'i}a-Nieto and Olivera, Ana Carolina and Alba, Enrique },
doi = {10.1109/TEVC.2013.2260755},
journal = {IEEE Transactions on Evolutionary Computation},
month = dec,
number = 6,
pages = {823--839},
title = {Optimal Cycle Program of Traffic Lights With Particle Swarm
Optimization},
volume = 17,
year = 2013
}
@article{GarRodLoz2012soco,
author = { Carlos Garc{\'i}a-Mart{\'i}nez and Francisco J. Rodr{\'i}guez and Manuel Lozano },
title = {Arbitrary function optimisation with metaheuristics: No free
lunch and real-world problems},
journal = {Soft Computing},
year = 2012,
volume = 16,
number = 12,
pages = {2115--2133},
doi = {10.1007/s00500-012-0881-x}
}
@article{GarRodLoz2014,
author = { Carlos Garc{\'i}a-Mart{\'i}nez and Francisco J. Rodr{\'i}guez and Manuel Lozano },
title = {Tabu-enhanced Iterated Greedy Algorithm: A Case Study in the Quadratic Multiple Knapsack Problem},
journal = {European Journal of Operational Research},
year = 2014,
volume = 232,
number = 3,
pages = {454--463}
}
@article{GauDodGro2012gsa,
author = {Melvin, Gauci and Tony J. Dodd and Roderich Gro{\ss} },
title = {Why `{GSA}: a gravitational search algorithm' is not
genuinely based on the law of gravity},
journal = {Natural Computing},
year = 2012,
volume = 11,
number = 4,
pages = {719--720}
}
@article{Gei2011,
author = {Martin Josef Geiger},
title = {Decision Support for Multi-objective Flow Shop Scheduling
by the {Pareto} Iterated Local Search Methodology},
journal = {Computers and Industrial Engineering},
volume = 61,
number = 3,
pages = {805--812},
year = 2011
}
@article{Geiger2017:ejor,
author = {Martin Josef Geiger},
title = {A Multi-threaded Local Search Algorithm and Computer Implementation for the Multi-mode, Resource-constrained Multi-project Scheduling Problem},
journal = {European Journal of Operational Research},
year = 2017,
volume = 256,
pages = {729--741}
}
@article{GemGem1984,
author = { Stuart Geman and Donald Geman },
title = {Stochastic Relaxation, {Gibbs} Distributions, and the {Bayesian} Restoration of Images},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = 6,
number = 6,
pages = {721--741},
year = 1984,
publisher = {IEEE Press}
}
@article{GenGuePot1999ts,
title = {Parallel tabu search for real-time vehicle routing and dispatching},
author = { Michel Gendreau and Francois Guertin and Jean-Yves Potvin and {\'E}ric D. Taillard },
journal = {Transportation Science},
volume = 33,
number = 4,
pages = {381--390},
year = 1999
}
@article{GenGuePot2006trc,
title = {Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick-ups and deliveries},
author = { Michel Gendreau and Francois Guertin and Jean-Yves Potvin and S{\'e}guin, Ren{\'e}},
journal = {Transportation Research Part C: Emerging Technologies},
volume = 14,
number = 3,
pages = {157--174},
year = 2006,
publisher = {Elsevier}
}
@article{GenMit2014,
title = {Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey},
author = {Gen, Mitsuo and Lin, Lin},
journal = {Journal of Intelligent Manufacturing},
volume = 25,
number = 5,
pages = {849--866},
year = 2014,
publisher = {Springer}
}
@article{GenPogTul2010,
title = {Variable selection using random forests},
author = {Genuer, Robin and Poggi, Jean-Michel and Tuleau-Malot, Christine},
journal = {Pattern Recognition Letters},
volume = 31,
number = 14,
pages = {2225--2236},
year = 2010,
publisher = {Elsevier}
}
@article{Gendreau98tsptw,
author = { Michel Gendreau and A. Hertz and Gilbert Laporte and M. Stan },
title = {A Generalized Insertion Heuristic for the Traveling
Salesman Problem with Time Windows},
journal = {Operations Research},
year = 1998,
volume = 46,
pages = {330--335}
}
@article{GenGhiGue2015tdtsp,
author = { Michel Gendreau and Ghiani, Gianpaolo and Guerriero,
Emanuela},
title = {Time-dependent routing problems: A review},
journal = {Computers \& Operations Research},
year = 2015,
volume = 64,
pages = {189--197},
month = dec,
doi = {10.1016/j.cor.2015.06.001}
}
@article{GerHorTen2015comp,
author = {Gershman, Samuel J. and Horvitz, Eric J. and Tenenbaum,
Joshua B.},
title = {Computational rationality: A converging paradigm for
intelligence in brains, minds, and machines},
volume = 349,
number = 6245,
pages = {273--278},
year = 2015,
doi = {10.1126/science.aac6076},
publisher = {American Association for the Advancement of Science},
abstract = {After growing up together, and mostly growing apart in the
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.},
epub = {https://science.sciencemag.org/content/349/6245/273.full.pdf},
journal = {Science}
}
@article{GeuDamWeh2006extratrees,
author = {Pierre Geurts and Damien Ernst and Louis Wehenkel},
title = {Extremely randomized trees},
doi = {10.1007/s10994-006-6226-1},
year = 2006,
month = mar,
publisher = {Springer Science and Business Media {LLC}},
volume = 63,
number = 1,
pages = {3--42},
journal = {Machine Learning},
annote = {Proposed ExtraTrees}
}
@article{GheGobLeu2003gfs,
author = {Ghemawat, Sanjay and Gobioff, Howard and Leung, Shun-Tak},
title = {The {Google} {File} {System}},
journal = {SIGOPS Oper. Syst. Rev.},
volume = 37,
number = 5,
year = 2003,
month = dec,
pages = {29--43},
publisher = {ACM Press},
address = { New York, NY}
}
@article{GhoNad09:asc,
author = { K. Ghoseiri and B. Nadjari },
title = {An ant colony optimization algorithm for the bi-objective
shortest path problem},
journal = {Applied Soft Computing},
volume = 10,
number = 4,
year = 2010,
pages = {1237--1246}
}
@article{GirRabPib2016,
title = {Quantification of Treatment Effect Modification on Both an Additive and Multiplicative Scale},
author = {Girerd, Nicolas and Rabilloud, Muriel and Pibarot, Philippe
and Mathieu, Patrick and Roy, Pascal},
journal = {PLoS One},
year = 2016,
month = apr,
volume = 11,
pages = {1--14},
number = 4,
doi = {10.1371/journal.pone.0153010}
}
@article{Glo1977,
author = { Fred Glover },
title = {Heuristics for Integer Programming Using Surrogate Constraints},
journal = {Decision Sciences},
year = 1977,
volume = 8,
pages = {156--166}
}
@article{Glo1986,
author = { Fred Glover },
title = {Future Paths for Integer Programming and Links to
Artificial Intelligence},
journal = {Computers \& Operations Research},
year = 1986,
volume = 13,
number = 5,
pages = {533--549}
}
@article{Glo1989,
title = {Tabu Search -- {Part} {I}},
author = { Fred Glover },
journal = {INFORMS Journal on Computing},
volume = 1,
number = 3,
pages = {190--206},
year = 1989,
doi = {10.1287/ijoc.1.3.190}
}
@article{Glo1990,
author = { Fred Glover },
title = {Tabu Search -- {Part} {II}},
journal = {INFORMS Journal on Computing},
year = 1990,
number = 1,
volume = 2,
pages = {4--32}
}
@article{GloHao2011so,
author = { Fred Glover and Jin-Kao Hao },
title = {The case for Strategic Oscillation},
journal = {Annals of Operations Research},
year = 2011,
volume = 183,
number = 1,
pages = {163--173}
}
@article{GloKocAli98,
author = { Fred Glover and Gary A. Kochenberger and Bahram Alidaee},
title = {Adaptive Memory Tabu Search for Binary Quadratic Programs},
journal = {Management Science},
year = 1998,
volume = 44,
number = 3,
pages = {336--345}
}
@article{GloLuHao2010diversif,
title = {Diversification-driven tabu search for unconstrained binary
quadratic problems},
author = { Fred Glover and L{\"u}, Zhipeng and Jin-Kao Hao },
journal = {{4OR}: A Quarterly Journal of Operations Research},
volume = 8,
number = 3,
pages = {239--253},
year = 2010,
doi = {10.1007/s10288-009-0115-y}
}
@article{GoeScho2014recovery,
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 },
journal = {Computers \& Operations Research},
volume = 52,
pages = {1--15},
year = 2014,
publisher = {Elsevier}
}
@article{Gol1970bfgs,
author = {Donald Goldfarb},
title = {A Family of Variable-Metric Methods Derived by Variational
Means},
journal = {Mathematics of Computation},
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}
}
@article{Gold1990pm,
title = {Probability matching, the magnitude of reinforcement, and
classifier system bidding},
author = { David E. Goldberg },
journal = {Machine Learning},
volume = 5,
number = 4,
pages = {407--425},
year = 1990
}
@article{GonZhaChi2018kbs,
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.}
}
@article{GorKlaRuz2011connectedness,
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}
}
@article{Gos2009rl,
author = {Gosavi, Abhijit},
title = {Reinforcement Learning: A Tutorial Survey and Recent
Advances},
journal = {INFORMS Journal on Computing},
volume = 21,
number = 2,
pages = {178--192},
year = 2009,
doi = {10.1287/ijoc.1080.0305}
}
@article{GouOrbToi03,
author = {N. I. M. Gould and D. Orban and P. L. Toint},
title = {{CUTEr} and {SifDec}: A constrained and unconstrained testing
environment, revisited},
journal = {ACM Transactions on Mathematical Software},
year = 2003,
volume = 29,
pages = {373--394}
}
@article{GraChi1996jair,
author = {Jonathan Gratch and Steve A. Chien},
title = {Adaptive Problem-solving for Large-scale Scheduling Problems:
A Case Study},
journal = {Journal of Artificial Intelligence Research},
year = 1996,
volume = 4,
pages = {365--396},
annote = {Earliest hyper-heuristic?}
}
@article{GraHer2009treedgp,
author = {Robert B. Gramacy and Lee, Herbert K. H.},
title = {Bayesian Treed {Gaussian} Process Models With an Application
to Computer Modeling},
journal = {Journal of the American Statistical Association},
volume = 103,
pages = {1119--1130},
year = 2008,
doi = {10.1198/016214508000000689},
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},
journal = {Journal of Simulation},
year = 2016,
volume = 10,
number = 1,
pages = {69--77}
}
@article{GraPriGag02,
author = {M. Gravel and W. L. Price and Caroline Gagn{\'e} },
title = {Scheduling continuous casting of aluminum using a
multiple objective ant colony optimization
metaheuristic},
journal = {European Journal of Operational Research},
year = 2002,
volume = 143,
number = 1,
pages = {218--229},
doi = {10.1016/S0377-2217(01)00329-0}
}
@article{Gre86,
author = {John J. Grefenstette},
title = {Optimization of Control Parameters for Genetic Algorithms},
journal = {IEEE Transactions on Systems, Man, and Cybernetics},
year = 1986,
volume = 16,
number = 1,
pages = {122--128},
keywords = {parameter tuning},
doi = {10.1109/TSMC.1986.289288}
}
@article{GreKadMouSlo2011:ejor,
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},
journal = {European Journal of Operational Research},
volume = 214,
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},
journal = {European Journal of Operational Research},
volume = 239,
number = 3,
pages = {711--730},
year = 2014,
doi = {10.1016/j.ejor.2014.05.022}
}
@article{GriBauIoa2018modeling,
title = {Modelling science trustworthiness under publish or perish pressure},
author = {David R. Grimes and Chris T. Bauch and John P. A. Ioannidis },
journal = {Royal Society Open Science},
volume = 5,
pages = {171511},
year = 2018
}
@article{GroDelTad2004,
author = { Andrea Grosso and Federico {Della Croce} and R. Tadei},
title = {An Enhanced Dynasearch Neighborhood for the
Single-Machine Total Weighted Tardiness Scheduling
Problem},
journal = {Operations Research Letters},
year = 2004,
volume = 32,
number = 1,
pages = {68--72}
}
@article{GroJamLoc2009,
author = { Andrea Grosso and A. R. M. J. U. Jamali and Marco Locatelli},
title = {Finding Maximin Latin Hypercube Designs by Iterated Local Search Heuristics},
journal = {European Journal of Operational Research},
year = 2009,
volume = 197,
number = 2,
pages = {541--547}
}
@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},
author = {Groz, Beno{\^i}t and Maniu, Silviu},
journal = {Evolutionary Computation},
year = 2019,
number = 4,
pages = {611--637},
volume = 27
}
@article{GruFon2002spl,
author = { Viviane {Grunert da Fonseca} and Carlos M. Fonseca },
title = {A link between the multivariate cumulative distribution
function and the hitting function for random closed sets},
journal = {Statistics \& Probability Letters},
year = 2002,
volume = 57,
number = 2,
pages = {179--182},
doi = {10.1016/S0167-7152(02)00046-9}
}
@article{GueFonPaq2021hv,
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}
}
@article{Gui2011objred,
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}}
}
@article{GunNgPoh2012,
title = {A Hybridized {Lagrangian} Relaxation and Simulated Annealing
Method for the Course Timetabling Problem},
author = { Aldy Gunawan and Ng, Kien Ming and Poh, Kim Leng },
journal = {Computers \& Operations Research},
volume = 39,
number = 12,
pages = {3074--3088},
year = 2012,
publisher = {Elsevier}
}
@article{Gup1986,
title = {Flowshop schedules with sequence dependent setup times},
author = {J. N. D. Gupta},
journal = {Journal of Operations Research Society of Japan},
volume = 29,
year = 1986,
pages = {206--219}
}
@article{Gut2000:fgcs,
author = { Gutjahr, Walter J. },
title = {A {Graph}-based {Ant} {System} and its Convergence},
journal = {Future Generation Computer Systems},
year = 2000,
volume = 16,
number = 8,
pages = {873--888}
}
@article{Gut2002:ipl,
author = { Gutjahr, Walter J. },
title = {{ACO} Algorithms with Guaranteed Convergence to the
Optimal Solution},
journal = {Information Processing Letters},
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},
journal = {Computers \& Operations Research},
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},
journal = {Methodology and Computing in Applied Probability},
year = 2008,
volume = 10,
number = 3,
pages = {409--433}
}
@article{GutYeoZve2002,
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}},
journal = {Discrete Applied Mathematics},
volume = 117,
number = {1--3},
year = 2002
}
@article{GuyWesBar2002rfe,
title = {Gene selection for cancer classification using support vector
machines},
author = {Guyon, Isabelle and Weston, Jason and Barnhill, Stephen and
Vapnik, Vladimir},
journal = {Machine Learning},
volume = 46,
number = 1,
pages = {389--422},
year = 2002,
publisher = {Springer},
keywords = {recursive feature elimination}
}
@article{HaaSakTam2001,
title = {An adaptive {Metropolis} algorithm},
author = {Haario, Heikki and Saksman, Eero and Tamminen, Johanna},
journal = {Bernoulli},
volume = 7,
number = 2,
pages = {223--242},
year = 2001
}
@article{HadRee2013borg,
author = { David Hadka and Patrick M. Reed },
title = {Borg: An Auto-Adaptive Many-Objective Evolutionary Computing
Framework},
journal = {Evolutionary Computation},
number = 2,
pages = {231--259},
volume = 21,
year = 2013
}
@article{HadReed2012ec,
author = { David Hadka and Patrick M. Reed },
title = {Diagnostic Assessment of Search Controls and Failure Modes in
Many-Objective Evolutionary Optimization},
journal = {Evolutionary Computation},
volume = 20,
number = 3,
year = 2012,
pages = {423--452}
}
@article{HadRus1969rules,
title = {Rules for ordering uncertain prospects},
author = {Hadar, Josef and Russell, William R.},
journal = {The American Economic Review},
volume = 59,
number = 1,
pages = {25--34},
year = 1969,
epub = {https://www.jstor.org/stable/1811090},
keywords = {stochastic dominance}
}
@article{HaiLasWis1971bicriterion,
title = {On a bicriterion formation of the problems of integrated
system identification and system optimization},
author = {Haimes, Y. and Lasdon, L. and Da Wismer, D.},
journal = {IEEE Transactions on Systems, Man, and Cybernetics},
volume = 1,
number = 3,
pages = {296--297},
year = 1971,
doi = {10.1109/TSMC.1971.4308298},
keywords = {epsilon-constraint method}
}
@article{HajLin1992decomposition,
title = {Genetic search strategies in multicriterion optimal design},
author = {Hajela, Prabhat and Lin, C-Y},
journal = {Structural Optimization},
volume = 4,
number = 2,
pages = {99--107},
year = 1992,
publisher = {Springer}
}
@article{HajSas1989,
title = {Simulated annealing--to cool or not},
author = { Bruce Hajek and Galen Sasaki},
journal = {System \& Control Letters},
volume = 12,
number = 5,
pages = {443--447},
year = 1989,
publisher = {Elsevier}
}
@article{Hajek1988,
title = {Cooling Schedules for Optimal Annealing},
author = { Bruce Hajek },
journal = {Mathematics of Operations Research},
volume = 13,
number = 2,
pages = {311--329},
year = 1988,
publisher = {{INFORMS}}
}
@article{HalOliSud2022ai,
author = { George T. Hall and Oliveto, Pietro S. and Dirk Sudholt },
title = {On the impact of the performance metric on efficient
algorithm configuration},
doi = {10.1016/j.artint.2021.103629},
year = 2022,
month = feb,
publisher = {Elsevier {BV}},
volume = 303,
pages = 103629,
journal = {Artificial Intelligence},
keywords = {irace}
}
@article{HamLah2016path,
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}
}
@article{HamLuoSaa2013bor,
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}
}
@article{HamRuh1994,
author = {Hamacher, Horst W. and Ruhe, G\"{u}nter},
title = {On spanning tree problems with multiple objectives},
journal = {Annals of Operations Research},
year = 1994,
volume = 52,
number = 4,
pages = {209--230}
}
@article{HanAugBroTus2022anytime,
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}
}
@article{HanAugMer2016coco,
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}
}
@article{HanJau90,
author = { Pierre Hansen and B. Jaumard},
title = {Algorithms for the Maximum Satisfiability Problem},
journal = {Computing},
year = 1990,
volume = 44,
pages = {279--303}
}
@article{HanMla01:ejor,
title = {Variable neighborhood search: Principles and applications},
author = { Pierre Hansen and Nenad Mladenovi{\'c} },
journal = {European Journal of Operational Research},
volume = 130,
number = 3,
pages = {449--467},
year = 2001
}
@article{HanOst2001ec,
author = { Nikolaus Hansen and Ostermeier, A.},
title = {Completely derandomized self-adaptation in evolution
strategies},
journal = {Evolutionary Computation},
year = 2001,
volume = 9,
pages = {159--195},
number = 2,
doi = {10.1162/106365601750190398},
keywords = {CMA-ES}
}
@article{HanRosMauSchAug2011,
author = { Nikolaus Hansen and Raymond Ros and Nikolaus Mauny and Marc Schoenauer and Anne Auger },
title = {Impacts of invariance in search: When {CMA-ES} and {PSO} face ill-conditioned and non-separable problems},
journal = {Applied Soft Computing},
year = 2011,
volume = 11,
number = 8,
pages = {5755--5769}
}
@article{Hanne1999ejor,
author = {Thomas Hanne},
journal = {European Journal of Operational Research},
title = {On the convergence of multiobjective evolutionary algorithms},
volume = 117,
number = 3,
pages = {553--564},
year = 1999,
doi = {10.1016/S0377-2217(98)00262-8},
keywords = {archiving, efficiency presserving}
}
@article{Hanne2007ejor,
title = {A multiobjective evolutionary algorithm for approximating the
efficient set},
author = {Hanne, Thomas},
journal = {European Journal of Operational Research},
year = 2007,
number = 3,
pages = {1723--1734},
volume = 176
}
@article{HarSaf2004,
author = {Hardin, Douglas P. and Saff, Edward B.},
title = {Discretizing Manifolds via Minimum Energy Points},
journal = {Notices of the American Mathematical Society},
year = 2004,
number = 10,
pages = {1186--1194},
volume = 51
}
@article{HarSho87a,
author = {J. P. Hart and A. W. Shogan},
title = {Semi-greedy heuristics: An empirical study},
journal = {Operations Research Letters},
volume = 6,
number = 3,
pages = {107--114},
year = 1987
}
@article{HarSim2016ec,
author = { Emma Hart and Kevin Sim},
title = {A Hyper-Heuristic Ensemble Method for Static Job-Shop Scheduling},
journal = {Evolutionary Computation},
volume = 24,
number = 4,
pages = {609--635},
year = 2016,
doi = {10.1162/EVCO_a_00183 }
}
@article{Haraguchi2016:joh,
author = {Kazuya Haraguchi},
title = {Iterated Local Search with {Trellis}-Neighborhood for the Partial {Latin} Square Extension Problem},
journal = {Journal of Heuristics},
year = 2016,
volume = 22,
number = 5,
pages = {727--757}
}
@article{HasRaj2004,
author = { Sameer Hasija and Chandrasekharan Rajendran },
title = {Scheduling in flowshops to minimize total tardiness of jobs},
journal = {International Journal of Production Research},
volume = 42,
number = 11,
pages = {2289--2301},
year = 2004,
doi = {10.1080/00207540310001657595}
}
@article{HasYagIba2008:do,
author = {Hideki Hashimoto and Mutsunori Yagiura and Toshihide Ibaraki },
title = {An Iterated Local Search Algorithm for the Time-dependent Vehicle
Routing Problem with Time Windows},
journal = {Discrete Optimization},
year = 2008,
volume = 5,
number = 2,
pages = {434--456}
}
@article{Haykin2004nn,
title = {A comprehensive foundation},
author = {Haykin, Simon},
journal = {Neural Networks},
volume = 2,
pages = 41,
year = 2004
}
@article{HazGunEre08customer,
title = {Customer order scheduling problem: a comparative
metaheuristics study},
number = 5,
journal = {International Journal of Advanced Manufacturing Technology},
author = {{\"O}nc{\"u} Hazir and Yavuz G{\"u}nalay and Erdal Erel},
month = may,
year = 2008,
keywords = {{ACO,Customer} order {scheduling,Genetic}
{algorithms,Meta-heuristics,Simulated} {annealing,Tabu}
search},
pages = {589--598},
volume = 37,
doi = {10.1007/s00170-007-0998-8},
abstract = {The customer order scheduling problem {(COSP)} is defined as
to determine the sequence of tasks to satisfy the demand of
customers who order several types of products produced on a
single machine. A setup is required whenever a product type
is launched. The objective of the scheduling problem is to
minimize the average customer order flow time. Since the
customer order scheduling problem is known to be strongly
{NP-hard,} we solve it using four major metaheuristics and
compare the performance of these heuristics, namely,
simulated annealing, genetic algorithms, tabu search, and ant
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
colony perform better for large problems whereas simulated
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.}
}
@article{HeYen2016many,
title = {Many-Objective Evolutionary Algorithm: Objective Space
Reduction and Diversity Improvement},
author = {He, Zhenan and Yen, Gary G.},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2016,
number = 1,
pages = {145--160},
volume = 20
}
@article{HeZhaChu2021automl,
author = {Xin He and Kaiyong Zhao and Xiaowen Chu},
title = {{AutoML}: A survey of the state-of-the-art},
journal = {Knowledge-Based Systems},
volume = 212,
pages = 106622,
year = 2021,
issn = {0950-7051},
doi = {10.1016/j.knosys.2020.106622}
}
@article{HelBraMos2013bound,
author = {Helwig, Sabine and J{\"u}rgen Branke and Mostaghim, Sanaz },
title = {Experimental Analysis of Bound Handling Techniques in
Particle Swarm Optimization},
doi = {10.1109/tevc.2012.2189404},
year = 2013,
month = apr,
volume = 17,
number = 2,
pages = {259--271},
journal = {IEEE Transactions on Evolutionary Computation},
keywors = {PSO; box-constraints; constraint handling; bounds}
}
@article{HelKar1970,
author = {Held, Michael and Karp, Richard M.},
title = {The Traveling-Salesman Problem and Minimum Spanning Trees},
journal = {Operations Research},
volume = 18,
number = 6,
pages = {1138--1162},
year = 1970
}
@article{HelRen1998mp,
author = {Helmberg, Christoph and Rendl, Franz},
title = {Solving quadratic (0,1)-problems by semidefinite programs and
cutting planes},
journal = {Mathematical Programming},
year = 1998,
volume = 82,
number = 3,
pages = {291--315}
}
@article{Helsgaun00,
author = { Keld Helsgaun },
title = {An Effective Implementation of the {Lin}-{Kernighan}
Traveling Salesman Heuristic},
journal = {European Journal of Operational Research},
year = 2000,
volume = 126,
pages = {106--130}
}
@article{Helsgaun09,
author = { Keld Helsgaun },
title = {General {\it k}-opt Submoves for the {Lin}-{Kernighan} {TSP}
Heuristic},
journal = {Mathematical Programming Computation},
year = 2009,
volume = 1,
number = {2--3},
pages = {119--163}
}
@article{Her2015toms,
author = {Michael A. Heroux},
title = {Editorial: {ACM} {TOMS} Replicated Computational Results
Initiative},
doi = {10.1145/2743015},
year = 2015,
month = jun,
publisher = {Association for Computing Machinery ({ACM})},
volume = 41,
number = 3,
pages = {1--5},
journal = {ACM Transactions on Mathematical Software}
}
@article{HerBlu2009:si,
author = {H. Hern{\'a}ndez and Christian Blum },
title = {Ant colony optimization for multicasting in static
wireless ad-hoc networks},
journal = {Swarm Intelligence},
volume = 3,
number = 2,
pages = {125--148},
year = 2009
}
@article{HerColDua2021efficient,
title = {An efficient Variable Neighborhood Search for the Space-Free
Multi-Row Facility Layout problem},
author = {Herr{\'a}n, Alberto and Colmenar, J. Manuel and Duarte, Abraham },
journal = {European Journal of Operational Research},
year = 2021,
publisher = {Elsevier},
doi = {10.1016/j.ejor.2021.03.027}
}
@article{HerLozSan03:real-coded-crossover,
author = { Francisco Herrera and Manuel Lozano and A. M. S{\'a}nchez },
title = {A taxonomy for the crossover operator for real-coded genetic
algorithms: An experimental study},
journal = {International Journal of Intelligent Systems},
year = 2003,
volume = 18,
number = 3,
pages = {309--338},
doi = {10.1002/int.10091}
}
@article{HerLozVer1998air,
author = { Francisco Herrera and Manuel Lozano and Verdegay, J. L.},
title = {Tackling Real-Coded Genetic Algorithms: Operators
and Tools for Behavioural Analysis},
journal = {Artificial Intelligence Review},
year = 1998,
volume = 12,
pages = {265--319},
keywords = {genetic algorithms, real coding, continuous search
spaces, mutation, recombination}
}
@article{HerSch2022hausdorff,
title = {A Bounded Archiver for {Hausdorff} Approximations of the
{Pareto} Front for Multi-Objective Evolutionary Algorithms},
author = { Hern{\'a}ndez Castellanos, Carlos Ignacio and Oliver Sch{\"u}tze },
journal = {Mathematical and Computational Applications},
volume = 27,
number = 3,
pages = 48,
year = 2022,
doi = {10.3390/mca27030048},
publisher = {Multidisciplinary Digital Publishing Institute}
}
@article{HerSchSun2020noneps,
author = { Hern{\'a}ndez Castellanos, Carlos Ignacio and Oliver Sch{\"u}tze and Sun, J. Q. and Ober-Bl\"obaum, S.},
title = {Non-epsilon dominated evolutionary algorithm for the set of
approximate solutions},
journal = {Mathematical and Computational Applications},
year = 2020,
volume = 25,
number = 1,
pages = 3,
keywords = {archiving, multimodal}
}
@article{HerWer1987tabucol,
author = {A. Hertz and de Werra, D.},
title = {Using Tabu Search Techniques for Graph Coloring},
journal = {Computing},
year = 1987,
volume = 39,
number = 4,
pages = {345--351}
}
@article{Hermet2006ecj,
author = { van Hemert, Jano I. },
title = {Evolving Combinatorial Problem Instances That Are Difficult
to Solve},
journal = {Evolutionary Computation},
volume = 14,
number = 4,
pages = {433--462},
year = 2006,
doi = {10.1162/evco.2006.14.4.433},
abstract = {This paper demonstrates how evolutionary computation can be
used to acquire difficult to solve combinatorial problem
instances. As a result of this technique, the corresponding
algorithms used to solve these instances are
stress-tested. The technique is applied in three important
domains of combinatorial optimisation, binary constraint
satisfaction, Boolean satisfiability, and the travelling
salesman problem. The problem instances acquired through this
technique are more difficult than the ones found in popular
benchmarks. In this paper, these evolved instances are
analysed with the aim to explain their difficulty in terms of
structural properties, thereby exposing the weaknesses of
corresponding algorithms. }
}
@article{HeuNieKru2020publish,
author = {Robert Heum{\"u}ller and Sebastian Nielebock and Jacob
Kr{\"u}ger and Frank Ortmeier},
title = {Publish or perish, but do not forget your software artifacts},
doi = {10.1007/s10664-020-09851-6},
year = 2020,
volume = 25,
number = 6,
pages = {4585--4616},
journal = {Empirical Software Engineering}
}
@article{Hic2006,
author = {Christian Hicks},
title = {A Genetic Algorithm tool for optimising cellular or
functional layouts in the capital goods industry},
journal = {International Journal of Production Economics},
volume = 104,
number = 2,
pages = {598--614},
year = 2006,
doi = {10.1016/j.ijpe.2005.03.010}
}
@article{HieLiLiuPar2020,
title = {Many-objective test suite generation for software product
lines},
author = {Hierons, Robert M. and Li, Miqing and Liu, Xiaohui and
Parejo, Jose Antonio and Segura, Sergio and Xin Yao },
journal = {{ACM} Transactions on Software Engineering and Methodology},
year = 2020,
number = 1,
pages = {1--46},
volume = 29
}
@article{HinSal2006autoencoder,
title = {Reducing the dimensionality of data with neural networks},
author = {Hinton, Geoffrey E. and Salakhutdinov, Ruslan R.},
journal = {Science},
volume = 313,
number = 5786,
pages = {504--507},
year = 2006
}
@article{Hoeffding1963,
title = {Probability inequalities for sums of bounded random
variables},
author = {Hoeffding, Wassily},
journal = {Journal of the American Statistical Association},
volume = 58,
number = 301,
pages = {13--30},
year = 1963
}
@article{HonKahMoo1997,
author = {I. Hong and A. B. Kahng and B. R. Moon},
title = {Improved large-step {Markov} chain variants for the symmetric {TSP}},
journal = {Journal of Heuristics},
year = 1997,
volume = 3,
number = 1,
pages = {63--81}
}
@article{Hoo1994or,
author = { John N. Hooker },
title = {Needed: An Empirical Science of Algorithms},
journal = {Operations Research},
year = 1994,
volume = 42,
number = 2,
pages = {201--212}
}
@article{Hoo1996joh,
author = { John N. Hooker },
title = {Testing Heuristics: We Have It All Wrong},
journal = {Journal of Heuristics},
year = 1996,
volume = 1,
number = 1,
pages = {33--42},
doi = {10.1007/BF02430364}
}
@article{Hoo2012,
title = {Generalized functional {ANOVA} diagnostics for
high-dimensional functions of dependent variables},
author = {Hooker, Giles},
journal = {Journal of Computational and Graphical Statistics},
year = 2012,
volume = 16,
number = 3,
pages = {709--732},
doi = {10.1198/106186007X237892}
}
@article{HooLinSch2014claspfolio2,
title = {Claspfolio 2: Advances in Algorithm Selection for Answer Set
Programming},
author = { Holger H. Hoos and Marius Thomas Lindauer and Schaub, Torsten},
journal = {Theory and Practice of Logic Programming},
volume = 14,
number = {4-5},
pages = {560--585},
year = 2014,
publisher = {Cambridge University Press}
}
@article{HooStu2014,
author = { Holger H. Hoos and Thomas St{\"u}tzle },
title = {On the Empirical Scaling of Run-time for Finding Optimal
Solutions to the Traveling Salesman Problem},
journal = {European Journal of Operational Research},
year = 2014,
volume = 238,
number = 1,
pages = {87--94}
}
@article{HooStu2015:ol,
author = { Holger H. Hoos and Thomas St{\"u}tzle },
title = {On the Empirical Time Complexity of Finding Optimal Solutions
vs.\ Proving Optimality for {Euclidean} {TSP} Instances},
journal = {Optimization Letters},
year = 2015,
volume = 9,
number = 6,
pages = {1247--1254}
}
@article{Hoos:PbO,
author = { Holger H. Hoos },
title = {Programming by optimization},
journal = {Communications of the ACM},
volume = 55,
number = 2,
month = feb,
year = 2012,
pages = {70--80},
numpages = 11,
doi = {10.1145/2076450.2076469}
}
@article{HotTanTie2020deep,
author = {Hottung, Andr{\'e} and Tanaka, Shunji and Kevin Tierney },
title = {Deep learning assisted heuristic tree search for the
container pre-marshalling problem},
journal = {Computers \& Operations Research},
year = 2020,
volume = 113,
pages = 104781,
doi = {10.1016/j.cor.2019.104781}
}
@article{HotTie2022nlns,
author = {Hottung, Andr{\'e} and Kevin Tierney },
title = {Neural large neighborhood search for routing problems},
journal = {Artificial Intelligence},
year = 2022,
volume = 313,
pages = 103786,
month = dec,
doi = {10.1016/j.artint.2022.103786}
}
@article{HozDjuHoz2005,
title = {Estimating the mean and variance from the median, range, and
the size of a sample},
author = {Hozo, Stela Pudar and Djulbegovic, Benjamin and Hozo, Iztok},
journal = {BMC Medical Research Methodology},
volume = 5,
number = 1,
pages = 13,
year = 2005
}
@article{HuKahTsa95,
author = {T. C. Hu and A. B. Kahng and C.-W. A. Tsao},
title = {Old Bachelor Acceptance: A New Class of Non-Monotone
Threshold Accepting Methods},
journal = {ORSA Journal on Computing},
year = 1995,
volume = 7,
number = 4,
pages = {417--425}
}
@article{HuWanQiu2018nca,
author = {Hu, Wenbin and Wang, Huan and Qiu, Zhenyu and Nie, Cong and
Yan, Liping},
doi = {10.1007/s00521-016-2508-0},
journal = {Neural Computing \& Applications},
keywords = {BML,Optimization,Simulation,Traffic congestion,Updating
rules},
title = {A quantum particle swarm optimization driven urban traffic
light scheduling model},
year = 2018
}
@article{HuYanWang2017,
author = {Hu, Wenbin and Yan, Liping and Wang, Huan and Du, Bo and Tao,
Dacheng},
journal = {Information Sciences},
keywords = {BML model,Prediction,Real-time,Traffic jam,Urban traffic
network},
title = {Real-time traffic jams prediction inspired by {Biham},
{Middleton} and {Levine} (BML) model},
year = 2017
}
@article{HuaAllNot2006global,
title = {Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models},
author = {Huang, Deng and Allen, Theodore T. and Notz, William I. and
Zeng, Ning},
journal = {Journal of Global Optimization},
volume = 34,
number = 3,
pages = {441--466},
year = 2006,
publisher = {Springer},
doi = {10.1007/s10898-005-2454-3}
}
@article{HuaLiYao2020algconf,
author = {Changwu Huang and Yuanxiang Li and Xin Yao },
title = {A Survey of Automatic Parameter Tuning Methods for
Metaheuristics},
journal = {IEEE Transactions on Evolutionary Computation},
volume = 24,
number = 2,
pages = {201--216},
year = 2020,
doi = {10.1109/TEVC.2019.2921598}
}
@article{HubHinBar2006tec,
title = {A Review of Multiobjective Test Problems and a Scalable Test
Problem Toolkit},
volume = 10,
doi = {10.1109/TEVC.2005.861417},
number = 5,
journal = {IEEE Transactions on Evolutionary Computation},
author = {Huband, S. and Hingston, P. and Barone, L. and While, L.},
year = 2006,
pages = {477--506},
annote = {Proposed the WFG benchmark suite}
}
@article{HubLukHog1997,
author = {B. Huberman and R. Lukose and T. Hogg},
title = {An Economic Approach to Hard Computational Problems},
journal = {Science},
year = 1997,
volume = 275,
pages = {51--54}
}
@article{HueRioRui2017,
author = {D. L. Huerta-Mu{\~n}oz and R. Z. R{\'i}os-Mercado and Rub{\'e}n Ruiz },
title = {An Iterated Greedy Heuristic for a Market Segmentation Problem with Multiple Attributes},
journal = {European Journal of Operational Research},
year = 2017,
volume = 261,
number = 1,
pages = {75--87}
}
@article{HumLieTal2013joh,
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},
volume = 19,
doi = {10.1007/s10732-013-9228-8},
number = 6,
journal = {Journal of Heuristics},
month = jun,
year = 2013,
pages = {881--915}
}
@article{HunJosMel2009dace,
title = {Design and Analysis of Computer Experiments With Branching
and Nested Factors},
author = {Hung, Ying and Joseph, V. Roshan and Melkote, Shreyes N.},
journal = {Technometrics},
year = 2009,
number = 4,
pages = {354--365},
volume = 51,
doi = {10.1198/TECH.2009.07097}
}
@article{HurMau2010,
author = {M. Hurtgen and J.-C. Maun},
title = {Optimal {PMU} placement using Iterated Local Search},
journal = {International Journal of Electrical Power \& Energy Systems},
year = 2010,
volume = 32,
number = 8,
pages = {857--860}
}
@article{Hurlbert1984,
author = {S. H. Hurlbert},
title = {Pseudoreplication and the Design of Ecological Field
Experiments},
journal = {Ecological Monographs},
volume = 54,
number = 2,
pages = {187--211},
year = 1984
}
@article{HusStu2014cor,
author = {Mohamed Saifullah Hussin and Thomas St{\"u}tzle },
title = {Tabu Search vs. Simulated Annealing for Solving
Large Quadratic Assignment Instances},
journal = {Computers \& Operations Research},
volume = 43,
pages = {286--291},
year = 2014
}
@article{HutHooLey2010amai,
author = { Frank Hutter and Holger H. Hoos and Kevin Leyton-Brown },
title = {Tradeoffs in the Empirical Evaluation of Competing Algorithm Designs},
journal = {Annals of Mathematics and Artificial Intelligence},
year = 2010,
volume = 60,
number = {1--2},
pages = {65--89}
}
@article{HutHooLey2013arxiv,
author = { Frank Hutter and Holger H. Hoos and Kevin Leyton-Brown },
title = {Bayesian Optimization With Censored Response Data},
journal = {Arxiv preprint arXiv:1310.1947},
year = 2013,
url = {http://arxiv.org/abs/1310.1947}
}
@article{HutHooLeyStu2009jair,
author = { Frank Hutter and Holger H. Hoos and Kevin Leyton-Brown and Thomas St{\"u}tzle },
title = {{\softwarepackage{ParamILS}:} An Automatic Algorithm Configuration Framework},
journal = {Journal of Artificial Intelligence Research},
year = 2009,
volume = 36,
pages = {267--306},
month = oct,
doi = {10.1613/jair.2861}
}
@article{HutLinBal2017aij,
author = { Frank Hutter and Marius Thomas Lindauer and Adrian Balint and Sam Bayless and Holger H. Hoos and Kevin Leyton-Brown },
title = {The Configurable {SAT} Solver Challenge {(CSSC)}},
journal = {Artificial Intelligence},
year = 2017,
volume = 243,
pages = {1--25},
doi = {10.1016/j.artint.2016.09.006}
}
@article{HutXuHooLey2014,
author = { Frank Hutter and Lin Xu and Holger H. Hoos and Kevin Leyton-Brown },
title = {Algorithm Runtime Prediction: Methods \& evaluation},
journal = {Artificial Intelligence},
year = 2014,
volume = 206,
pages = {79--111},
doi = {10.1016/j.artint.2013.10.003},
abstract = {Perhaps surprisingly, it is possible to predict how long an
algorithm will take to run on a previously unseen input,
using machine learning techniques to build a model of the
algorithm's runtime as a function of problem-specific
instance features. Such models have important applications to
algorithm analysis, portfolio-based algorithm selection, and
the automatic configuration of parameterized algorithms. Over
the past decade, a wide variety of techniques have been
studied for building such models. Here, we describe
extensions and improvements of existing models, new families
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
(SAT), travelling salesperson (TSP) and mixed integer
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
instance distributions; they also span a very wide range of
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
demonstrate that our new models yield substantially better
runtime predictions than previous approaches in terms of
their generalization to new problem instances, to new
algorithms from a parameterized space, and to both
simultaneously.},
keywords = {Empirical performance models; Mixed integer programming; SAT}
}
@article{IOHanalyzer,
author = { Wang, Hao and Diederick Vermetten and Furong Ye and Carola Doerr and Thomas B{\"a}ck },
title = {{IOHanalyzer}: Detailed Performance Analyses for Iterative
Optimization Heuristics},
journal = {ACM Transactions on Evolutionary Learning and Optimization},
year = 2022,
volume = 2,
number = 1,
pages = {3:1--3:29},
doi = {10.1145/3510426}
}
@article{IOHexperimenter2021,
author = {Jacob de Nobel and Furong Ye and Diederick Vermetten and Wang, Hao and Carola Doerr and Thomas B{\"a}ck },
title = {{IOHexperimenter}: Benchmarking Platform for Iterative
Optimization Heuristics},
journal = {Arxiv preprint arXiv:2111.04077},
year = 2021,
annote = {Published in ECJ~\cite{IOHexperimenter2024}},
doi = {10.48550/arXiv.2111.04077}
}
@article{IOHexperimenter2024,
author = {Jacob de Nobel and Furong Ye and Diederick Vermetten and Wang, Hao and Carola Doerr and Thomas B{\"a}ck },
title = {{IOHexperimenter}: Benchmarking Platform for Iterative
Optimization Heuristics},
journal = {Evolutionary Computation},
year = 2024,
pages = {1--6},
doi = {10.1162/evco_a_00342}
}
@article{IOHprofiler,
author = { Carola Doerr and Wang, Hao and Furong Ye and van Rijn,
Sander and Thomas B{\"a}ck },
title = {{IOHprofiler}: A Benchmarking and Profiling Tool for
Iterative Optimization Heuristics},
journal = {Arxiv preprint arXiv:1806.07555},
year = 2018,
month = oct,
keywords = {Benchmarking; Heuristics},
doi = {10.48550/arXiv.1810.05281}
}
@article{IacPal2012:si,
author = {Claudio Iacopino and Phil Palmer},
title = {The Dynamics of Ant Colony Optimization Algorithms Applied to Binary Chains},
journal = {Swarm Intelligence},
year = 2012,
volume = 6,
number = 4,
pages = {343--377}
}
@article{IacPalPol2014ants,
journal = {Acta Futura},
title = {How Ants Can Manage Your Satellites},
doi = {10.2420/AF09.2014.59},
volume = 9,
author = {Iacopino, Claudio and Palmer, Phil and Policella, N. and
Donati, A. and Brewer, A.},
pages = {59--72},
year = 2014,
keywords = {ACO, Space}
}
@article{IbaImaNon++2008:dam,
author = {Toshihide Ibaraki and Shinji Imahori and Koji Nonobe and Kensuke Sobue and Takeaki Uno and Mutsunori Yagiura },
title = {An Iterated Local Search Algorithm for the Vehicle Routing Problem
with Convex Time Penalty Functions},
journal = {Discrete Applied Mathematics},
year = 2008,
volume = 156,
number = 11,
pages = {2050--2069}
}
@article{Ibaraki2010:itor,
author = {Toshihide Ibaraki},
title = {A Personal Perspective on Problem Solving by General Purpose Solvers},
journal = {International Transactions in Operational Research},
year = 2010,
volume = 17,
number = 3,
pages = {303--315}
}
@article{IdeSch2016,
author = {Ide, Jonas and Sch{\"o}bel, Anita },
title = {Robustness for uncertain multi-objective optimization: a
survey and analysis of different concepts},
journal = {OR Spectrum},
year = 2016,
volume = 38,
number = 1,
pages = {235--271},
abstract = {In this paper, we discuss various concepts of robustness for
uncertain multi-objective optimization problems. We extend
the concepts of flimsily, highly, and lightly robust
efficiency and we collect different versions of minmax robust
efficiency and concepts based on set order relations from the
literature. Altogether, we compare and analyze ten different
concepts and point out their relations to each
other. Furthermore, we present reduction results for the
class of objective-wise uncertain multi-objective
optimization problems.},
doi = {10.1007/s00291-015-0418-7}
}
@article{IgeHanRot2007ec,
author = { Christian Igel and Nikolaus Hansen and S. Roth},
title = {Covariance Matrix Adaptation for Multi-objective
Optimization},
journal = {Evolutionary Computation},
year = 2007,
volume = 15,
pages = {1--28},
number = 1
}
@article{IgeHeiGla2008jmlr,
author = { Christian Igel and V. Heidrich-Meisner and T. Glasmachers },
title = {Shark},
journal = {Journal of Machine Learning Research},
year = 2008,
volume = 9,
month = jun,
pages = {993--996},
url = {http://www.jmlr.org/papers/volume9/igel08a/igel08a.pdf}
}
@article{Ilich98,
author = { Nesa Ilich and Slobodan P. Simonovic },
title = {Evolutionary Algorithm for minimization of pumping
cost},
journal = {Journal of Computing in Civil Engineering, {ASCE}},
year = 1998,
volume = 12,
number = 4,
pages = {232--240},
month = oct,
abstract = {This paper deals with minimizing the total cost of
pumping in a liquid pipeline. Previous experience
with the most common solution procedures in pipeline
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.}
}
@article{ImaYagNag2009:ejor,
author = {Takashi Imamichi and Mutsunori Yagiura and Hiroshi Nagamochi},
title = {An Iterated Local Search Algorithm Based on Nonlinear
Programming for the Irregular Strip Packing Problem},
journal = {Discrete Optimization},
year = 2009,
volume = 6,
number = 4,
pages = {345--361}
}
@article{Inselberg1985pc,
title = {The Plane with Parallel Coordinates},
author = {Inselberg, Alfred},
journal = {The {Visual} {Computer}},
volume = 1,
number = 2,
pages = {69--91},
year = 1985
}
@article{Ioa2005why,
title = {Why Most Published Research Findings Are False},
author = { John P. A. Ioannidis },
journal = {PLoS Medicine},
volume = 2,
number = 8,
doi = {10.1371/journal.pmed.0020124},
pages = {e124},
year = 2005
}
@article{Irnich2008,
author = {Stefan Irnich},
title = {A Unified Modeling and Solution Framework for Vehicle Routing and
Local Search-Based Metaheuristics},
journal = {INFORMS Journal on Computing},
year = 2008,
volume = 20,
number = 2,
pages = {270--287}
}
@article{IruCalLoz2016mallows,
author = { Irurozki, Ekhine and Borja Calvo and Jos{\'e} A. Lozano },
title = {Sampling and Learning {Mallows} and Generalized {Mallows}
Models Under the {Cayley} Distance},
doi = {10.1007/s11009-016-9506-7},
year = 2016,
month = jun,
volume = 20,
number = 1,
pages = {1--35},
journal = {Methodology and Computing in Applied Probability}
}
@article{IruCalLoz2016permallows,
title = {{\rpackage{PerMallows}}: An {\proglang{R}} Package for Mallows
and Generalized Mallows Models},
author = { Irurozki, Ekhine and Calvo, Borja and Jos{\'e} A. Lozano },
abstract = {In this paper we present the R package PerMallows, which is a
complete toolbox to work with permutations, distances and
some of the most popular probability models for permutations:
Mallows and the Generalized Mallows models. The Mallows model
is an exponential location model, considered as analogous to
the Gaussian distribution. It is based on the definition of a
distance between permutations. The Generalized Mallows model
is its best-known extension. The package includes functions
for making inference, sampling and learning such
distributions. The distances considered in PerMallows are
Kendall's $\tau$, Cayley, Hamming and Ulam.},
doi = {10.18637/jss.v071.i12},
issn = 15487660,
journal = {Journal of Statistical Software},
keywords = {Cayley,Generalized Mallows,Hamming,Kendall's
$\tau$,Learning,Mallows,Permutation,R,Ranking,Sampling,Ulam},
volume = 71,
year = 2019
}
@article{IruLobPer2020arxiv,
title = {Rank aggregation for non-stationary data streams},
author = { Irurozki, Ekhine and Lobo, Jesus and Perez, Aritz and Del Ser,
Javier},
keywords = {uborda},
journal = {Arxiv preprint arXiv:1910.08795 [stat.ML]},
year = 2020,
url = {https://arxiv.org/abs/1910.08795}
}
@article{Ish1998,
author = { Ishibuchi, Hisao and Murata, T. },
title = {A multi-objective genetic local search algorithm and its
application to flowshop scheduling},
journal = {IEEE Transactions on Systems, Man, and Cybernetics -- Part C},
number = 3,
pages = {392--403},
volume = 28,
year = 1998
}
@article{IshAkeNoj2015tec,
author = { Ishibuchi, Hisao and N. Akedo and Y. Nojima},
journal = {IEEE Transactions on Evolutionary Computation},
title = {Behavior of Multiobjective Evolutionary Algorithms on
Many-Objective Knapsack Problems},
year = 2015,
volume = 19,
number = 2,
pages = {264--283},
doi = {10.1109/TEVC.2014.2315442}
}
@article{IshImaSet2018refpoint,
title = {How to specify a reference point in hypervolume calculation
for fair performance comparison},
author = { Ishibuchi, Hisao and Imada, Ryo and Setoguchi, Yu and
Nojima, Yusuke},
journal = {Evolutionary Computation},
year = 2018,
number = 3,
pages = {411--440},
volume = 26
}
@article{IshMisTan1995,
title = {Modified simulated annealing algorithms for the flow shop
sequencing problem},
author = { Ishibuchi, Hisao and Misaki, Shinta and Tanaka, Hideo},
journal = {European Journal of Operational Research},
volume = 81,
number = 2,
pages = {388--398},
year = 1995
}
@article{IshSetMas2017shape,
title = {Performance of decomposition-based many-objective algorithms
strongly depends on {Pareto} front shapes},
author = { Ishibuchi, Hisao and Setoguchi, Yu and Masuda, Hiroyuki and
Nojima, Yusuke},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2017,
number = 2,
pages = {169--190},
volume = 21,
publisher = {IEEE}
}
@article{IviTha2019repro,
author = {Peter Ivie and Douglas Thain},
title = {Reproducibility in Scientific Computing},
doi = {10.1145/3186266},
year = 2019,
publisher = {Association for Computing Machinery ({ACM})},
volume = 51,
number = 3,
pages = {1--36},
journal = {{ACM} Computing Surveys}
}
@article{IyeSax2004,
author = {Srikanth K. Iyer and Barkha Saxena},
title = {Improved genetic algorithm for the permutation flowshop
scheduling problem},
journal = {Computers \& Operations Research},
volume = 31,
number = 4,
pages = {593--606},
year = 2004,
doi = {10.1016/S0305-0548(03)00016-9}
}
@article{IzzBecMyaNas2007search,
author = { Dario Izzo and Becerra, V. M. and Myatt, D. R. and Nasuto,
S. J. and Bishop, J. M.},
title = {Search space pruning and global optimisation of multiple
gravity assist spacecraft trajectories},
journal = {Journal of Global Optimization},
year = 2007,
volume = 38,
pages = {283--296},
publisher = {Springer},
doi = {10.1007/s10898-006-9106-0}
}
@article{Izzo2015lambert,
author = { Dario Izzo },
title = {Revisiting {Lambert}'s Problem},
journal = {Celestial Mechanics and Dynamical Astronomy},
year = 2015,
volume = 121,
pages = {1--15},
publisher = {Springer}
}
@article{Jac2011jss,
title = {Multi-State Models for Panel Data: The {\rpackage{msm}} Package for \proglang{R}},
author = {Christopher H. Jackson},
journal = {Journal of Statistical Software},
year = 2011,
volume = 38,
number = 8,
pages = {1--29},
url = {http://www.jstatsoft.org/v38/i08/}
}
@article{JacBogNas1991,
author = {Richard H. F. Jackson and Paul T. Boggs and Stephen G. Nash and Susan Powell},
title = {Guidelines for Reporting Results of Computational Experiments. Report of the Ad
Hoc Committee},
journal = {Mathematical Programming},
year = 1991,
volume = 49,
number = 3,
pages = {413--425}
}
@article{JacBru1995:ig,
author = {Jacobs, Larry W. and Brusco, Michael J.},
title = {A Local Search Heuristic for Large Set-Covering
Problems},
journal = {Naval Research Logistics},
year = 1995,
volume = 42,
number = 7,
pages = {1129--1140}
}
@article{JacKah1995anchoring,
author = {Jacowitz, Karen E. and Kahneman, Daniel },
title = {Measures of {Anchoring} in {Estimation} {Tasks}},
journal = {Personality and Social Psychology Bulletin},
year = 1995,
volume = 21,
number = 11,
pages = {1161--1166},
month = nov,
issn = {0146-1672},
doi = {10.1177/01461672952111004},
abstract = {The authors describe a method for the quantitative study of
anchoring effects in estimation tasks. A calibration group
provides estimates of a set of uncertain quantities. Subjects
in the anchored condition first judge whether a specified
number (the anchor) is higher or lower than the true value
before estimating each quantity. The anchors are set at
predetermined percentiles of the distribution of estimates in
the calibration group (15th and 85th percentiles in this
study). This procedure permits the transformation of anchored
estimates into percentiles in the calibration group, allows
pooling of results across problems, and provides a natural
measure of the size of the effect. The authors illustrate the
method by a demonstration that the initial judgment of the
anchor is susceptible to an anchoring-like bias and by an
analysis of the relation between anchoring and subjective
confidence.},
language = {en}
}
@article{JacOzcJoh2018,
author = {Warren G. Jackson and Ender {\"O}zcan and Robert I. John},
title = {Move acceptance in local search metaheuristics for cross-domain search},
journal = {Expert Systems with Applications},
volume = 109,
pages = {131--151},
year = 2018
}
@article{JaeParKip2008ejor,
title = {The development of a multi-objective Tabu Search algorithm for continuous optimisation problems},
author = {Daniel M Jaeggi and Geoffrey T Parks and Timoleon Kipouros and P John Clarkson},
journal = {European Journal of Operational Research},
volume = 185,
number = 3,
pages = {1192--1212},
year = 2008
}
@article{JajMinHarPro1992,
title = {{CLASS}: computerized layout solutions using simulated annealing},
author = {Jajodia, Satish and Minis, Ioannis and Harhalakis, George and Proth, Jean-Marie},
journal = {International Journal of Production Research},
volume = 30,
number = 1,
pages = {95--108},
year = 1992,
publisher = {Taylor \& Francis}
}
@article{Jas2002,
title = {Genetic local search for multi-objective combinatorial optimization},
author = { Andrzej Jaszkiewicz },
journal = {European Journal of Operational Research},
volume = 137,
number = 1,
pages = {50--71},
year = 2002
}
@article{Jas2018ejor,
title = {Many-Objective {Pareto} Local Search},
author = { Andrzej Jaszkiewicz },
journal = {European Journal of Operational Research},
year = 2018,
volume = 271,
number = 3,
pages = {1001--1013},
doi = {10.1016/j.ejor.2018.06.009}
}
@article{JasLus2018ndtree,
title = {{ND}-tree-based update: a fast algorithm for the dynamic
nondominance problem},
author = { Andrzej Jaszkiewicz and Thibaut Lust },
journal = {IEEE Transactions on Evolutionary Computation},
year = 2018,
number = 5,
pages = {778--791},
volume = 22,
publisher = {IEEE}
}
@article{Jasz02mogls,
author = { Andrzej Jaszkiewicz },
title = {On the performance of multiple-objective genetic
local search on the 0/1 knapsack problem -- A
comparative experiment},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2002,
volume = 6,
number = 4,
pages = {402--412}
}
@article{Jen03,
title = {Reducing the run-time complexity of multiobjective
{EA}s: The {NSGA-II} and other algorithms},
author = {M. T. Jensen},
journal = {IEEE Transactions on Evolutionary Computation},
volume = 7,
number = 5,
pages = {503--515},
year = 2003
}
@article{Jensen2004,
author = {M. T. Jensen},
title = {Helper-Objectives: {Using} Multi-Objective Evolutionary
Algorithms for Single-Objective Optimisation},
journal = {Journal of Mathematical Modelling and Algorithms},
year = 2004,
volume = 3,
number = 4,
pages = {323--347},
keywords = {multi-objectivization}
}
@article{JerSor1998dam,
title = {The {Metropolis} algorithm for graph bisection},
author = { Mark Jerrum and Sorkin, Gregory},
journal = {Discrete Applied Mathematics},
volume = 82,
number = 1,
pages = {155--175},
year = 1998,
publisher = {Elsevier}
}
@article{Jerrum1992,
title = {Large cliques elude the {Metropolis} process},
author = { Mark Jerrum },
journal = {Random Structures \& Algorithms},
volume = 3,
number = 4,
pages = {347--359},
year = 1992,
publisher = {John Wiley \& Sons}
}
@article{JiaOngZhaFen2014metrics,
author = {S. Jiang and Y. S. Ong and J. Zhang and L. Feng},
journal = {IEEE Transactions on Cybernetics},
title = {Consistencies and Contradictions of Performance Metrics in
Multiobjective Optimization},
year = 2014,
volume = 44,
number = 12,
pages = {2391--2404}
}
@article{JiaZouYanYao2022dynamic,
author = {Jiang, Shouyong and Zou, Juan and Yang, Shengxiang and Xin Yao },
title = {Evolutionary Dynamic Multi-Objective Optimisation: A Survey},
year = 2022,
volume = 55,
number = 4,
doi = {10.1145/3524495},
journal = {{ACM} Computing Surveys},
month = nov,
articleno = 76,
numpages = 47,
keywords = {evolutionary algorithm, evolutionary dynamic multi-objective
optimisation, dynamic environment, Multi-objective
optimisation}
}
@article{Jin2005fitness,
author = { Yaochu Jin },
title = {A Comprehensive Survey of Fitness Approximation in
Evolutionary Computation},
journal = {Soft Computing},
year = 2005,
volume = 9,
number = 1,
pages = {3--12}
}
@article{Jin2011surrogate,
author = { Yaochu Jin },
title = {Surrogate-Assisted Evolutionary Computation: Recent
Advances and Future Challenges},
shorttitle = {Surrogate-Assisted Evolutionary Computation},
year = 2011,
month = jun,
journal = {Swarm and Evolutionary Computation},
volume = 1,
number = 2,
pages = {61--70},
issn = {2210-6502},
doi = {10.1016/j.swevo.2011.05.001},
abstract = {Surrogate-assisted, or meta-model based evolutionary
computation uses efficient computational models, often known
as surrogates or meta-models, for approximating the fitness
function in evolutionary algorithms. Research on
surrogate-assisted evolutionary computation began over a
decade ago and has received considerably increasing interest
in recent years. Very interestingly, surrogate-assisted
evolutionary computation has found successful applications
not only in solving computationally expensive single- or
multi-objective optimization problems, but also in addressing
dynamic optimization problems, constrained optimization
problems and multi-modal optimization problems. This paper
provides a concise overview of the history and recent
developments in surrogate-assisted evolutionary computation
and suggests a few future trends in this research area.},
langid = {english},
keywords = {Evolutionary computation,Expensive optimization
problems,Machine learning,Meta-models,Model
management,Surrogates}
}
@article{JinWanChu2019data,
author = { Yaochu Jin and Handing Wang and Tinkle Chugh and Dan
Guo and Kaisa Miettinen },
journal = {IEEE Transactions on Evolutionary Computation},
title = {Data-Driven Evolutionary Optimization: An Overview and Case
Studies},
year = 2019,
month = jun,
number = 3,
pages = {442--458},
volume = 23,
doi = {10.1109/tevc.2018.2869001}
}
@article{JinWon2010adapt,
title = {Adaptive, convergent, and diversified archiving strategy for
multiobjective evolutionary algorithms},
author = {Jin, Huidong and Wong, Man-Leung},
journal = {Expert Systems with Applications},
year = 2010,
number = 12,
pages = {8462--8470},
volume = 37,
publisher = {Elsevier}
}
@article{Joh54,
author = {David S. Johnson},
title = {Optimal Two- and Three-stage Production Scheduling
with Setup Times Included},
journal = {Naval Research Logistics Quarterly},
year = 1954,
volume = 1,
pages = {61--68}
}
@article{JohAraMcGSch1989,
author = {David S. Johnson and Cecilia R. Aragon and Lyle A. McGeoch and Catherine Schevon},
title = {Optimization by Simulated Annealing: An Experimental
Evaluation: Part {I}, Graph Partitioning},
journal = {Operations Research},
year = 1989,
volume = 37,
number = 6,
pages = {865--892},
doi = {10.1287/opre.39.3.378}
}
@article{JohAraMcGSch1991,
author = {David S. Johnson and Cecilia R. Aragon and Lyle A. McGeoch and Catherine Schevon},
title = {Optimization by Simulated Annealing: An Experimental
Evaluation: Part {II}, Graph Coloring and Number Partitioning},
journal = {Operations Research},
year = 1991,
volume = 39,
number = 3,
pages = {378--406}
}
@article{JohJac2002:dam,
title = {On the Convergence of Generalized Hill Climbing Algorithms},
author = { Alan W. Johnson and Sheldon H. Jacobson },
journal = {Discrete Applied Mathematics},
volume = 119,
number = 1,
pages = {37--57},
year = 2002,
publisher = {Elsevier}
}
@article{JohMooYlv1990,
title = {Minimax and maximin distance designs},
author = {Johnson, Mark E. and Moore, Leslie M. and Ylvisaker, Donald},
journal = {Journal of Statistical Planning and Inference},
year = 1990,
number = 2,
pages = {131--148},
volume = 26,
keywords = {Bayesian design}
}
@article{JohPapYan1988,
author = {David S. Johnson and Christos H. Papadimitriou and Mihalis Yannakakis },
title = {How Easy is Local Search?},
journal = {Journal of Computer System Science},
year = 1988,
volume = 37,
number = 1,
pages = {79--100}
}
@article{JonKriMicSch2023generalized,
title = {Generalized Relax-and-Fix Heuristic},
author = {Joncour, C. and Kritter, J. and Michel, S. and Schepler, X.},
journal = {Computers \& Operations Research},
volume = 149,
pages = 106038,
year = 2023,
publisher = {Elsevier}
}
@article{JonSchWel98ego,
author = {Donald R. Jones and Matthias Schonlau and William J. Welch},
title = {Efficient Global Optimization of Expensive Black-Box
Functions},
journal = {Journal of Global Optimization},
year = 1998,
volume = 13,
number = 4,
pages = {455--492},
keywords = {EGO},
annote = {Proposed EGO algorithm}
}
@article{JonSpe1992formal,
title = {A formal analysis of the role of multi-point crossover in
genetic algorithms},
author = { De Jong, Kenneth A. and Spears, William M.},
journal = {Annals of Mathematics and Artificial Intelligence},
volume = 5,
number = 1,
pages = {1--26},
year = 1992,
publisher = {Springer}
}
@article{JooLeyDec2022knapsack,
author = {Jorik Jooken and Pieter Leyman and Patrick {De Causmaecker}},
title = {A new class of hard problem instances for the 0--1 knapsack
problem},
journal = {European Journal of Operational Research},
year = 2022,
volume = 301,
number = 3,
pages = {841--854}
}
@article{JooLeyWau2023exploring,
author = {Jorik Jooken and Pieter Leyman and Tony Wauters and Patrick {De Causmaecker}},
title = {Exploring search space trees using an adapted version of
{Monte} {Carlo} tree search for combinatorial optimization
problems},
journal = {Computers \& Operations Research},
year = 2023,
volume = 150,
pages = 106070,
doi = {10.1016/j.cor.2022.106070}
}
@article{JosCle1999:jair,
author = {D. E. Joslin and D. P. Clements},
title = {Squeaky Wheel Optimization},
journal = {Journal of Artificial Intelligence Research},
year = 1999,
volume = 10,
pages = {353--373}
}
@article{Jowitt92,
author = { P. W. Jowitt and G. Germanopoulos },
title = {Optimal pump scheduling in water supply networks},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
year = 1992,
volume = 118,
number = 4,
pages = {406--422},
note = {},
abstract = {The electricity cost of pumping accounts for a large
part of the total operating cost for water-supply
networks. This study presents a method based on
linear programming for determining an optimal
(minimum cost) schedule of pumping on a 24-hr
basis. Both unit and maximum demand electricity
charges are considered. Account is taken of the
relative efficiencies of the available pumps, the
structure of the electricity tariff, the
consumer-demand profile, and the hydraulic
characteristics and operational constraints of the
network. The use of extended-period simulation of
the network operation in determining the parameters
of the linearized network equations and constraints
and in studying the optimized network operation is
described. An application of the method to an
existing network in the United Kingdom is presented,
showing that considerable savings are possible. The
method was found to be robust and with low
computation-time requirements, and is therefore
suitable for real-time implementation.}
}
@article{JuaFauGras2015orp,
author = {Angel A. Juan and Javier Faulin and Scott E. Grasman and
Markus Rabe and Gon{\c c}alo Figueira},
title = {A review of simheuristics: Extending metaheuristics to deal
with stochastic combinatorial optimization problems},
journal = {Operations Research Perspectives},
volume = 2,
pages = {62--72},
year = 2015,
doi = {10.1016/j.orp.2015.03.001},
keywords = {Metaheuristics; Simulation; Combinatorial optimization;
Stochastic problems}
}
@article{JuaLouMatLuoCas2014,
author = {Angel A. Juan and Helena R. {Louren{\c c}o} and Manuel Mateo and Rachel Luo and Quim Castell{\`{a}}},
title = {Using Iterated Local Search for Solving the Flow-shop Problem: Parallelization,
Parametrization, and Randomization Issues},
journal = {International Transactions in Operational Research},
year = 2014,
volume = 21,
number = 1,
pages = {103--126}
}
@article{JunReiThi1994,
author = {M. J{\"u}nger and Gerhard Reinelt and S. Thienel},
title = {Provably Good Solutions for the Traveling Salesman Problem},
journal = {Zeitschrift f{\"u}r Operations Research},
year = 1994,
volume = 40,
number = 2,
pages = {183--217}
}
@article{KabColKorLop2017jacryst,
author = { Kabova, Elena A. and Cole, Jason C. and Oliver Korb and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Williams, Adrian C. and Shankland, Kenneth },
title = {Improved performance of crystal structure solution from
powder diffraction data through parameter tuning of a
simulated annealing algorithm},
journal = {Journal of Applied Crystallography},
year = 2017,
volume = 50,
number = 5,
pages = {1411--1420},
month = oct,
doi = {10.1107/S1600576717012602},
abstract = {Significant gains in the performance of the simulated
annealing algorithm in the {\it DASH} software package have
been realized by using the {\it irace} automatic
configuration tool to optimize the values of three key
simulated annealing parameters. Specifically, the success
rate in finding the global minimum in intensity $\chi^2$
space is improved by up to an order of magnitude. The general
applicability of these revised simulated annealing parameters
is demonstrated using the crystal structure determinations of
over 100 powder diffraction datasets.},
keywords = {crystal structure determination, powder diffraction,
simulated annealing, parameter tuning, irace}
}
@article{KahTve1979prospect,
title = {Prospect theory: {An} analysis of decision under risk},
author = { Kahneman, Daniel and Tversky, Amos },
journal = {Econometrica},
pages = {263--291},
volume = 47,
number = 2,
year = 1979,
doi = {10.2307/1914185}
}
@article{Kahneman2003maps,
title = {Maps of bounded rationality: Psychology for behavioral
economics},
author = { Kahneman, Daniel },
journal = {The American Economic Review},
volume = 93,
number = 5,
pages = {1449--1475},
year = 2003
}
@article{KalHasHemSor2023deeprl,
author = {Kallestad, Jakob and Hasibi, Ramin and Hemmati, Ahmad and Kenneth S{\"o}rensen },
title = {A general deep reinforcement learning hyperheuristic
framework for solving combinatorial optimization problems},
journal = {European Journal of Operational Research},
year = 2023,
volume = 309,
number = 1,
pages = {446--468},
month = aug,
doi = {10.1016/j.ejor.2023.01.017},
keywords = {Deep RL, hyper-heuristic, ALNS}
}
@article{KanHeWei2013,
author = {Qinma Kang and Hong He and Jun Wei},
title = {An Effective Iterated Greedy Algorithm for
Reliability-oriented Task Allocation in Distributed Computing
Systems},
journal = {Journal of Parallel and Distributed Computing},
year = 2013,
volume = 73,
number = 8,
pages = {1106--1115}
}
@article{Kar2016,
author = {Korhan Karabulut},
title = {A hybrid iterated greedy algorithm for total tardiness
minimization in permutation flowshops},
journal = {Computers and Industrial Engineering},
year = 2016,
volume = 98,
number = {Supplement C},
pages = {300 -- 307}
}
@article{KarAka2009:airev,
author = { Dervis Karaboga and Bahriye Akay },
title = {A Survey: Algorithms Simulating Bee Swarm Intelligence},
journal = {Artificial Intelligence Review},
year = 2009,
volume = 31,
number = {1--4},
pages = {61--85}
}
@article{KarHooEib2015:tec,
author = {Giorgos Karafotias and Mark Hoogendoorn and Agoston E. Eiben },
title = {Parameter Control in Evolutionary Algorithms: Trends and Challenges},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2015,
volume = 19,
number = 2,
pages = {167--187}
}
@article{KarKok2010tdea,
title = {A territory defining multiobjective evolutionary algorithms
and preference incorporation},
author = { Karahan, {\.I}brahim and Murat K{\"o}ksalan },
journal = {IEEE Transactions on Evolutionary Computation},
volume = 14,
number = 4,
pages = {636--664},
year = 2010,
keywords = {TDEA},
doi = {10.1109/TEVC.2009.2033586}
}
@article{KarMohMey2022ml,
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}
}
@article{Kee1981or,
author = {R. L. Keeney},
title = {Analysis of preference dependencies among objectives},
journal = {Operations Research},
year = 1981,
volume = 29,
pages = {1105--1120}
}
@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},
title = {Good Laboratory Practice for Optimization Research},
year = 2016,
volume = 67,
number = 4,
pages = {676--689},
journal = {Journal of the Operational Research Society},
doi = {10.1057/jors.2015.77}
}
@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
}
@article{KerLin70,
author = {B. W. Kernighan and S. Lin},
title = {An Efficient Heuristic Procedure for Partitioning
Graphs},
journal = {Bell Systems Technology Journal},
year = 1970,
volume = 49,
number = 2,
pages = {213--219}
}
@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}
}
@article{Kerr1998harking,
doi = {10.1207/s15327957pspr0203_4},
year = 1998,
month = aug,
publisher = {{SAGE} Publications},
volume = 2,
number = 3,
pages = {196--217},
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}},
journal = {Artificial Intelligence},
year = 2016,
volume = 232,
pages = {20--42},
doi = {10.1016/j.artint.2015.11.002}
}
@article{KilUrl2015constr,
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},
journal = {Journal of the Operational Research Society},
year = 1993,
volume = 44,
number = 1,
pages = {19--28},
doi = {10.1057/jors.1993.3}
}
@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},
journal = {SIAM Journal on Optimization},
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}
}
@article{KocGloAli2004ors,
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}
}
@article{KorWal1998paretorace,
author = { Pekka Korhonen and Wallenius, Jyrki },
title = {A pareto race},
journal = {Naval Research Logistics},
year = 1988,
volume = 35,
number = 6,
pages = {615--623},
doi = {10.1002/1520-6750(198812)35:6<615::AID-NAV3220350608>3.0.CO;2-K},
abstract = {A dynamic and visual ``free-search'' type of interactive
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
display. The method is implemented on an IBM PC/1
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.}
}
@article{Kot2014:aim,
author = {Kotthoff, Lars},
title = {Algorithm Selection for Combinatorial Search Problems: {A} Survey},
journal = {{AI} Magazine},
year = 2014,
volume = 35,
number = 3,
pages = {48--60}
}
@article{KotNeuRogWit2012swarm,
author = {K{\"o}tzing, Timo and Frank Neumann and R{\"o}glin, Heiko and Carsten Witt },
title = {Theoretical Analysis of Two {ACO} Approaches for the
Traveling Salesman Problem},
journal = {Swarm Intelligence},
year = 2012,
volume = 6,
number = 1,
pages = {1--21},
abstract = {Bioinspired algorithms, such as evolutionary algorithms and
ant colony optimization, are widely used for different
combinatorial optimization problems. These algorithms rely
heavily on the use of randomness and are hard to understand
from a theoretical point of view. This paper contributes to
the theoretical analysis of ant colony optimization and
studies this type of algorithm on one of the most prominent
combinatorial optimization problems, namely the traveling
salesperson problem (TSP). We present a new construction
graph and show that it has a stronger local property than one
commonly used for constructing solutions of the TSP. The
rigorous runtime analysis for two ant colony optimization
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.},
doi = {10.1007/s11721-011-0059-7}
}
@article{KotThoHooHutLey2016autoweka,
title = {{Auto-WEKA} 2.0: Automatic model selection and hyperparameter
optimization in {WEKA}},
author = {Kotthoff, Lars and Thornton, Chris and Holger H. Hoos and Frank Hutter and Kevin Leyton-Brown },
journal = {Journal of Machine Learning Research},
volume = 17,
pages = {1--5},
year = 2016
}
@article{KowStaMad2009sustainable,
title = {Sustainable energy futures: Methodological challenges in
combining scenarios and participatory multi-criteria
analysis},
author = {Kowalski, Katharina and Stagl, Sigrid and Madlener, Reinhard
and Omann, Ines},
journal = {European Journal of Operational Research},
volume = 197,
number = 3,
pages = {1063--1074},
year = 2009,
publisher = {Elsevier}
}
@article{Kra2010,
author = {Oliver Kramer},
title = {Iterated Local Search with {Powell}'s Method: A Memetic
Algorithm for Continuous Global Optimization},
journal = {Memetic Computing},
year = 2010,
volume = 2,
number = 1,
pages = {69--83},
doi = {10.1007/s12293-010-0032-9}
}
@article{KraErdBeh2012sumo,
title = {Recent development and applications of {SUMO} - {Simulation}
of {Urban} {MO}bility},
author = { Krajzewicz, Daniel and Erdmann, Jakob and Behrisch, Michael
and Bieker, Laura},
journal = {International Journal On Advances in Systems and
Measurements},
volume = 5,
number = {3-4},
year = 2012,
pages = {128--138}
}
@article{Kreipl00:js,
author = {S. Kreipl},
title = {A Large Step Random Walk for Minimizing Total Weighted Tardiness in a Job Shop},
journal = {Journal of Scheduling},
year = 2000,
volume = 3,
number = 3,
pages = {125--138}
}
@article{KriTriDoe2017,
author = {Stefanie Kritzinger and Fabien Tricoire and Karl F. Doerner and Richard F. Hartl and Thomas St{\"u}tzle },
title = {A Unified Framework for Routing Problems with a Fixed Fleet Size},
journal = {International Journal of Metaheuristics},
year = 2017,
volume = 6,
number = 3,
pages = {160--209}
}
@article{Kruskal1956,
title = {On the shortest spanning subtree of a graph and the traveling salesman problem},
author = {Kruskal, Joseph B},
journal = {Proceedings of the American Mathematical Society},
volume = 7,
number = 1,
pages = {48--50},
year = 1956
}
@article{KuhBie2016:cor,
author = {Kuhpfahl, J and Christian Bierwirth},
title = {A Study on Local Search Neighborhoods for the Job Shop Scheduling Problem with Total Weighted Tardiness Objective},
journal = {Computers \& Operations Research},
year = 2016,
volume = 66,
pages = {44--57}
}
@article{KuhFonPaqRuz2016hvsubset,
title = {Hypervolume subset selection in two dimensions: Formulations
and algorithms},
author = {Kuhn, Tobias and Carlos M. Fonseca and Lu{\'i}s Paquete and Ruzika,
Stefan and Duarte, Miguel M. and Jos{\'e} Rui Figueira },
journal = {Evolutionary Computation},
year = 2016,
number = 3,
pages = {411--425},
volume = 24
}
@article{Kuhn1955,
title = {The hungarian method for the assignment problem},
author = {Kuhn, Harold W.},
journal = {Naval Research Logistics Quarterly},
volume = 2,
number = {1--2},
pages = {83--97},
year = 1955
}
@article{Kuhn2008:jss,
author = {Kuhn, Max},
title = {Building Predictive Models in \proglang{R} Using the {\rpackage{caret}} Package},
journal = {Journal of Statistical Software},
volume = 28,
number = 5,
year = 2008,
pages = {1--26}
}
@article{KumSin2007sci,
author = {Kumar, R. and Singh, P. K.},
title = {{Pareto} Evolutionary Algorithm Hybridized with
Local Search for Biobjective {TSP}},
journal = {Studies in Computational Intelligence},
year = 2007,
volume = 75,
pages = {361--398}
}
@article{KunLucPre1975jacm,
author = {H. T. Kung and F. Luccio and F. P. Preparata},
title = {On Finding the Maxima of a Set of Vectors},
pages = {469--476},
journal = {Journal of the ACM},
year = 1975,
volume = 22,
number = 4
}
@article{KurDav1982ms,
author = {Kurtulus, I. and Davis, E. W.},
title = {Multi-Project Scheduling: Categorization of
Heuristic Rules Performance},
volume = 28,
doi = {10.1287/mnsc.28.2.161},
abstract = {Application of heuristic solution procedures to the
practical problem of project scheduling has
previously been studied by numerous
researchers. However, there is little consensus
about their findings, and the practicing manager is
currently at a loss as to which scheduling rule to
use. Furthermore, since no categorization process
was developed, it is assumed that once a rule is
selected it must be used throughout the whole
project. This research breaks away from this
tradition by providing a categorization process
based on two powerful project summary measures. The
first measure identifies the location of the peak of
total resource requirements and the second measure
identifies the rate of utilization of each resource
type. The performance of the rules are classified
according to values of these two measures, and it is
shown that a rule introduced by this research
performs significantly better on most categories of
projects.},
number = 2,
journal = {Management Science},
year = 1982,
keywords = {project management, research and development},
pages = {161--172}
}
@article{Kushner1964,
author = {Kushner, H. J.},
journal = {Journal of Basic Engineering},
title = {A New Method of Locating the Maximum Point of an Arbitrary
Multipeak Curve in the Presence of Noise},
year = 1964,
issn = {0021-9223},
month = mar,
number = 1,
pages = {97--106},
volume = 86,
abstract = {A versatile and practical method of searching a parameter
space is presented. Theoretical and experimental results
illustrate the usefulness of the method for such problems as
the experimental optimization of the performance of a system
with a very general multipeak performance function when the
only available information is noise-distributed samples of
the function. At present, its usefulness is restricted to
optimization with respect to one system parameter. The
observations are taken sequentially; but, as opposed to the
gradient method, the observation may be located anywhere on
the parameter interval. A sequence of estimates of the
location of the curve maximum is generated. The location of
the next observation may be interpreted as the location of
the most likely competitor (with the current best estimate)
for the location of the curve maximum. A Brownian motion
stochastic process is selected as a model for the unknown
function, and the observations are interpreted with respect
to the model. The model gives the results a simple intuitive
interpretation and allows the use of simple but efficient
sampling procedures. The resulting process possesses some
powerful convergence properties in the presence of noise; it
is nonparametric and, despite its generality, is efficient in
the use of observations. The approach seems quite promising
as a solution to many of the problems of experimental system
optimization.},
doi = {10.1115/1.3653121},
epub = {https://asmedigitalcollection.asme.org/fluidsengineering/article-pdf/86/1/97/5763745/97_1.pdf}
}
@article{Kwa2017ems,
title = {The Exploratory Modeling Workbench: An open source toolkit
for exploratory modeling, scenario discovery, and
(multi-objective) robust decision making},
author = { Kwakkel, Jan H. },
journal = {Environmental Modelling \& Software},
volume = 96,
pages = {239--250},
year = 2017
}
@article{LTDZ2002b,
author = { Marco Laumanns and Lothar Thiele and Kalyanmoy Deb and Eckart Zitzler },
title = {Combining Convergence and Diversity in Evolutionary
Multiobjective Optimization},
journal = {Evolutionary Computation},
year = 2002,
volume = 10,
number = 3,
pages = {263--282},
doi = {10.1162/106365602760234108},
keywords = {archiving, $\epsilon$-dominance, $\epsilon$-approximation,
$\epsilon$-Pareto},
annote = {Proposed $\epsilon$-approx and $\epsilon$-Pareto archivers}
}
@article{LaTMuePen11:soco,
author = {LaTorre, Antonio and Muelas, Santiago and Pe{\~n}a,
Jos{\'e}-Mar{\'i}a},
title = {A {MOS}-based dynamic memetic differential evolution
algorithm for continuous optimization: a scalability test},
journal = {Soft Computing},
year = 2011,
volume = 15,
number = 11,
pages = {2187--2199}
}
@article{LaaAarLen1992:or,
author = { Peter J. M. van Laarhoven and Emile H. L. Aarts and Jan Karel Lenstra },
title = {Job Shop Scheduling by Simulated Annealing},
journal = {Operations Research},
year = 1992,
volume = 40,
number = 1,
pages = {113--125}
}
@article{LabMarSav1998:npp,
author = {Martine Labb{\'e} and Patrice Marcotte and Gilles Savard},
title = {A Bilevel Model of Taxation and Its Application to Optimal Highway Pricing},
journal = {Management Science},
volume = 44,
number = 12,
year = 1998,
pages = {1608--1622},
doi = {10.1287/mnsc.44.12.1608},
publisher = {{INFORMS}}
}
@article{LabVio2013:npp,
author = {Martine Labb{\'e} and Alessia Violin},
title = {Bilevel programming and price setting problems},
journal = {{4OR}: A Quarterly Journal of Operations Research},
year = 2013,
volume = 11,
number = 1,
pages = {1--30},
doi = {10.1007/s10288-012-0213-0}
}
@article{LacMolHer2014is,
author = {Benjamin Lacroix and Daniel Molina and Francisco Herrera },
title = {Region based memetic algorithm for real-parameter
optimisation},
journal = {Information Sciences},
year = 2014,
volume = 262,
pages = {15--31},
doi = {10.1016/j.ins.2013.11.032},
keywords = {irace}
}
@article{Laguna2016:editornote,
author = { Manuel Laguna },
title = {Editor's Note on the {MIC} 2013 Special Issue of the
Journal of Heuristics (Volume 22, Issue 4, August 2016)},
journal = {Journal of Heuristics},
year = 2016,
volume = 22,
number = 5,
pages = {665--666}
}
@article{LaiHao2016,
author = {Xiangjing Lai and Jin-Kao Hao },
title = {Iterated Maxima Search for the Maximally Diverse Grouping Problem},
journal = {European Journal of Operational Research},
year = 2016,
volume = 254,
number = 3,
pages = {780--800}
}
@article{LanDoi1960,
author = {A. H. Land and A. G. Doig},
journal = {Econometrica},
number = 3,
pages = {497--520},
publisher = {Wiley, Econometric Society},
title = {An Automatic Method of Solving Discrete Programming Problems},
volume = 28,
year = 1960
}
@article{LanHar2015,
author = { Langdon, William B. and Mark Harman},
title = {Optimising Software with Genetic Programming},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2015,
volume = 19,
number = 1,
pages = {118--135}
}
@article{LanKotMar2014jscs,
author = {M. Lang and H. Kotthaus and P. Marwedel and C. Weihs and
J. Rahnenf{\"u}hrer and Bernd Bischl },
title = {Automatic Model Selection for High-Dimensional Survival
Analysis},
journal = {Journal of Statistical Computation and Simulation},
year = 2014,
volume = 85,
number = 1,
pages = {62--76},
doi = {10.1080/00949655.2014.929131}
}
@article{LanSouDes1990,
title = {Classification of travelling salesman problem formulations},
author = { A. Langevin and F. Soumis and J. Desrosiers },
journal = {Operations Research Letters},
volume = 9,
number = 2,
pages = {127--132},
year = 1990,
publisher = {Elsevier}
}
@article{Langevin93tsptw,
author = { A. Langevin and M. Desrochers and J. Desrosiers and Sylvie G{\'e}linas and F. Soumis },
title = {A Two-Commodity Flow Formulation for the Traveling Salesman
and Makespan Problems with Time Windows},
journal = {Networks},
year = 1993,
volume = 23,
number = 7,
pages = {631--640}
}
@article{Lansey94,
author = { Kevin E. Lansey and K. Awumah },
title = {Optimal Pump Operations Considering Pump Switches},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
volume = 120,
number = 1,
pages = {17--35},
date = {1994-01/1994-02},
year = 1994,
month = jan # { / } # feb
}
@article{Laporte2009,
author = { Gilbert Laporte },
title = {Fifty Years of Vehicle Routing},
journal = {Transportation Science},
year = 2009,
volume = 43,
number = 4,
pages = {408--416}
}
@article{Lau2007arxiv,
title = {Stochastic convergence of random search to fixed size
{Pareto} set approximations},
author = { Marco Laumanns },
journal = {Arxiv preprint arXiv:0711.2949},
year = 2007,
url = {https://arxiv.org/abs/0711.2949}
}
@article{LauHao2009:cie,
author = {Beno{\^i}t Laurent and Jin-Kao Hao },
title = {Iterated Local Search for the Multiple Depot Vehicle Scheduling Problem},
journal = {Computers and Industrial Engineering},
year = 2009,
volume = 57,
number = 1,
pages = {277--286}
}
@article{LauThiZit2004,
title = {Running time analysis of multiobjective evolutionary
algorithms on pseudo-boolean functions},
author = { Marco Laumanns and Lothar Thiele and Eckart Zitzler },
journal = {IEEE Transactions on Evolutionary Computation},
volume = 8,
number = 2,
pages = {170--182},
year = 2004
}
@article{LauThiZit2004nc,
author = { Marco Laumanns and Lothar Thiele and Eckart Zitzler },
title = {Running time analysis of evolutionary algorithms on a
simplified multiobjective knapsack problem},
volume = 3,
journal = {Natural Computing},
number = 1,
pages = {37--51},
year = 2004
}
@article{LauZen2011ejor,
author = { Marco Laumanns and Zenklusen, Rico},
title = {Stochastic convergence of random search methods to
fixed size {Pareto} front approximations},
journal = {European Journal of Operational Research},
volume = 213,
number = 2,
pages = {414--421},
year = 2011,
doi = {10.1016/j.ejor.2011.03.039}
}
@article{LawWoo1966,
author = {Lawler, E. L. and Wood, D. E.},
title = {Branch-and-Bound Methods: A Survey},
journal = {Operations Research},
volume = 14,
number = 4,
year = 1966,
pages = {699--719},
numpages = 21,
doi = {10.1287/opre.14.4.699},
publisher = {{INFORMS}}
}
@article{Lazic2004,
author = {S. E. Lazic},
title = {The problem of pseudoreplication in neuroscientific studies:
is it affecting your analysis?},
journal = {{BMC} Neuroscience},
volume = 11,
number = 5,
pages = {397--407},
year = 2004,
doi = {10.1186/1471-2202-11-5}
}
@article{LeCBen1995convnet,
title = {Convolutional networks for images, speech, and time series},
author = {LeCun, Yann and Bengio, Yoshua and others},
journal = {The handbook of brain theory and neural networks},
volume = 3361,
number = 10,
pages = {255--258},
year = 1995
}
@article{LeCBenHin2015nature,
title = {Deep learning},
author = {LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey},
journal = {Nature},
volume = 521,
number = 7553,
pages = {436--444},
year = 2015,
publisher = {Nature Research}
}
@article{LeaTavMouMac2016:itor,
author = {Vin{\'i}cius {Leal do Forte} and Fl{\'a}vio Marcelo {Tavares
Montenegro} and Jos{\'e} Andr{\'e} {de Moura Brito} and
Nelson Maculan},
title = {Iterated Local Search Algorithms for the {Euclidean}
{Steiner} Tree Problem in \emph{n} Dimensions},
journal = {International Transactions in Operational Research},
year = 2016,
volume = 23,
number = 6,
pages = {1185--1199}
}
@article{LehWit2012,
title = {Black-box search by unbiased variation},
author = {Lehre, Per Kristian and Carsten Witt },
journal = {Algorithmica},
volume = 64,
number = 4,
pages = {623--642},
year = 2012,
publisher = {Springer}
}
@article{Leighton1979,
author = {Frank Thomson Leighton},
title = {A Graph Coloring Algorithm for Large Scheduling Problems},
journal = {Journal of Research of the National Bureau of Standards},
year = 1979,
volume = 84,
number = 6,
pages = {489--506}
}
@article{LemGroPop2006general,
title = {A general analytic method for generating robust strategies
and narrative scenarios},
author = {Lempert, Robert J. and Groves, David G. and Popper, Steven
W. and Bankes, Steven C. },
journal = {Management Science},
volume = 52,
number = 4,
pages = {514--528},
year = 2006,
publisher = {{INFORMS}}
}
@article{Leon00,
author = {C. Leon and S. Martin and J. M. Elena and J. Luque},
title = {{EXPLORE}: Hybrid expert system for water networks
management},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
year = 2000,
volume = 126,
number = 2,
pages = {65--74}
}
@article{Levin1973,
title = {Universal'nyie perebornyie zadachi},
author = {Levin, Leonid},
journal = {Problemy Peredachi Informatsii},
volume = 9,
pages = {265--266},
year = 1973
}
@article{LewKurJoe2009gen,
author = {Lewandowski, Daniel and Kurowicka, Dorota and Joe, Harry},
title = {Generating Random Correlation Matrices Based on Vines and
Extended Onion Method},
journal = {Journal of Multivariate Analysis},
year = 2009,
volume = 100,
number = 9,
pages = {1989--2001},
doi = {10.1016/j.jmva.2009.04.008},
abstract = {We extend and improve two existing methods of generating
random correlation matrices, the onion method of Ghosh and
Henderson [S. Ghosh, S.G. Henderson, Behavior of the norta
method for correlated random vector generation as the
dimension increases, ACM Transactions on Modeling and
Computer Simulation (TOMACS) 13 (3) (2003) 276-294] and the
recently proposed method of Joe [H. Joe, Generating random
correlation matrices based on partial correlations, Journal
of Multivariate Analysis 97 (2006) 2177-2189] based on
partial correlations. The latter is based on the so-called
D-vine. We extend the methodology to any regular vine and
study the relationship between the multiple correlation and
partial correlations on a regular vine. We explain the onion
method in terms of elliptical distributions and extend it to
allow generating random correlation matrices from the same
joint distribution as the vine method. The methods are
compared in terms of time necessary to generate 5000 random
correlation matrices of given dimensions.},
keywords = {Correlation matrix; Dependence vines; Onion method; Partial
correlation; LKJ}
}
@article{Li2008two,
title = {A two-step rejection procedure for testing multiple
hypotheses},
author = {Li, Jianjun David},
journal = {Journal of Statistical Planning and Inference},
volume = 138,
number = 6,
pages = {1521--1527},
year = 2008
}
@article{Li2021telo,
title = {Is Our Archiving Reliable? Multiobjective Archiving Methods
on ``Simple'' Artificial Input Sequences},
author = { Li, Miqing },
journal = {ACM Transactions on Evolutionary Learning and Optimization},
year = 2021,
number = 3,
pages = {1--19},
volume = 1,
doi = {10.1145/3465335}
}
@article{LiCheFuYao2018twoarch,
title = {Two-archive evolutionary algorithm for constrained
multiobjective optimization},
author = {Li, Ke and Chen, Renzhi and Fu, Guangtao and Xin Yao },
journal = {IEEE Transactions on Evolutionary Computation},
year = 2018,
number = 2,
pages = {303--315},
volume = 23,
publisher = {IEEE}
}
@article{LiCheYao2020ieeese,
author = { Li, Miqing and Chen, Tao and Xin Yao },
title = {How to evaluate solutions in {Pareto}-based search-based
software engineering? {A} critical review and methodological
guidance},
journal = {IEEE Transactions on Software Engineering},
year = 2020,
volume = 48,
number = 5,
pages = {1771--1799},
doi = {10.1109/TSE.2020.3036108}
}
@article{LiGroYanLiu2018multi,
title = {Multi-line distance minimization: A visualized many-objective
test problem suite},
author = { Li, Miqing and Grosan, Crina and Yang, Shengxiang and
Liu, Xiaohui and Xin Yao },
journal = {IEEE Transactions on Evolutionary Computation},
year = 2018,
number = 1,
pages = {61--78},
volume = 22,
annote = {highly degenerate Pareto fronts}
}
@article{LiLopYao2023archiving,
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,
volume = 28,
number = 3,
pages = {696--717},
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.}
}
@article{LiShaBah2016traffic,
author = {Li, Zhiyi and Shahidehpour, Mohammad and Bahramirad, Shay and
Khodaei, Amin},
doi = {10.1109/TSG.2016.2526032},
journal = {IEEE Transactions on Smart Grid},
number = 4,
pages = {1--1},
title = {Optimizing Traffic Signal Settings in Smart Cities},
volume = 3053,
year = 2016,
abstract = {Traffic signals play a critical role in smart cities for
mitigating traffic congestions and reducing the emission in
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.}
}
@article{LiChenXuGupta2015,
author = {Xiaoping Li and Long Chen and Haiyan Xu and Jatinder N. D. Gupta},
title = {Trajectory Scheduling Methods for Minimizing Total Tardiness in a Flowshop},
journal = {Operations Research Perspectives},
volume = 2,
pages = {13--23},
year = 2015,
issn = {2214--7160},
doi = {10.1016/j.orp.2014.12.001}
}
@article{LiJamSal2018hyperband,
author = {Lisha Li and Kevin Jamieson and Giulia DeSalvo and Afshin
Rostamizadeh and Ameet Talwalkar},
title = {Hyperband: A Novel Bandit-Based Approach to Hyperparameter
Optimization},
journal = {Journal of Machine Learning Research},
year = 2018,
volume = 18,
number = 185,
pages = {1--52},
epub = {http://jmlr.org/papers/v18/16-558.html},
abstract = {Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian optimization to adaptively select configurations, we focus on speeding up random search through adaptive resource allocation and early-stopping. We formulate hyperparameter optimization as a pure-exploration non-stochastic infinite-armed bandit problem where a predefined resource like iterations, data samples, or features is allocated to randomly sampled configurations. We introduce a novel algorithm, our algorithm, for this framework and analyze its theoretical properties, providing several desirable guarantees. Furthermore, we compare our algorithm with popular Bayesian optimization methods on a suite of hyperparameter optimization problems. We observe that our algorithm can provide over an order-of-magnitude speedup over our competitor set on a variety of deep-learning and kernel-based learning problems.},
keywords = {racing}
}
@article{LiLi07,
author = {Y. Li and W. Li},
title = {Adaptive Ant Colony Optimization Algorithm Based on
Information Entropy: Foundation and Application},
journal = {Fundamenta Informaticae},
volume = 77,
number = 3,
year = 2007,
pages = {229--242},
publisher = {IOS Press},
address = {Amsterdam, The Netherlands}
}
@article{LiLiTanYao2015many,
author = {Li, Bingdong and Li, Jinlong and Tang, Ke and Xin Yao },
title = {Many-Objective Evolutionary Algorithms: A Survey},
journal = {{ACM} Computing Surveys},
volume = 48,
number = 1,
year = 2015,
pages = {1--35},
doi = {10.1145/2792984},
numpages = 35
}
@article{LiTanLiYao2016stochastic,
title = {Stochastic ranking algorithm for many-objective optimization
based on multiple indicators},
author = {Li, Bingdong and Tang, Ke and Li, Jinlong and Xin Yao },
journal = {IEEE Transactions on Evolutionary Computation},
year = 2016,
number = 6,
pages = {924--938},
volume = 20,
publisher = {IEEE}
}
@article{LiYanLiu2014shift,
title = {Shift-based density estimation for {Pareto}-based algorithms
in many-objective optimization},
author = { Li, Miqing and Yang, Shengxiang and Liu, Xiaohui},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2014,
number = 3,
pages = {348--365},
volume = 18,
publisher = {IEEE},
annote = {Proposed SDE indicator algorithm}
}
@article{LiYanLiu2016tec,
title = {{Pareto} or non-{Pareto}: {Bi}-criterion evolution in
multiobjective optimization},
author = { Li, Miqing and Yang, Shengxiang and Liu, Xiaohui},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2016,
number = 5,
pages = {645--665},
volume = 20
}
@article{LiYao2019qual,
title = {Quality Evaluation of Solution Sets in Multiobjective
Optimisation: A Survey},
author = { Li, Miqing and Xin Yao },
journal = {{ACM} Computing Surveys},
year = 2019,
number = 2,
volume = 52,
pages = {1--38},
doi = {10.1145/3300148},
publisher = {ACM}
}
@article{LiYao2017arxiv,
title = {Dominance Move: A Measure of Comparing Solution Sets in
Multiobjective Optimization},
author = { Li, Miqing and Xin Yao },
journal = {arXiv preprint arXiv:1702.00477},
year = 2017
}
@article{LiYao2020ec,
title = {What weights work for you? Adapting weights for any {Pareto}
front shape in decomposition-based evolutionary
multiobjective optimisation},
author = { Li, Miqing and Xin Yao },
journal = {Evolutionary Computation},
year = 2020,
number = 2,
pages = {227--253},
volume = 28
}
@article{LiZha2009:moead-de,
title = {Multiobjective Optimization Problems with Complicated
{Pareto} sets, {MOEA/D} and {NSGA-II}},
author = {Li, Hui and Zhang, Qingfu },
journal = {IEEE Transactions on Evolutionary Computation},
volume = 13,
number = 2,
pages = {284--302},
year = 2009
}
@article{LiZouYan2021twoarch,
title = {A two-archive algorithm with decomposition and fitness
allocation for multi-modal multi-objective optimization},
author = {Li, Zhipan and Zou, Juan and Yang, Shengxiang and Zheng,
Jinhua},
journal = {Information Sciences},
year = 2021,
pages = {413--430},
volume = 574,
publisher = {Elsevier}
}
@article{LiaAydStu13,
author = {Liao, Tianjun and Do\v{g}an Ayd{\i}n and Thomas St{\"u}tzle },
title = {Artificial Bee Colonies for Continuous Optimization: Experimental Analysis and Improvements},
journal = {Swarm Intelligence},
year = 2013,
volume = 7,
number = 4,
pages = {327--356}
}
@article{LiaMolMonStu2014,
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},
journal = {Evolutionary Computation},
year = 2014,
volume = 22,
number = 2,
pages = {351--359}
}
@article{LiaMolStu2015,
author = {Liao, Tianjun and Daniel Molina and Thomas St{\"u}tzle },
title = {Performance Evaluation of Automatically Tuned Continuous
Optimizers on Different Benchmark Sets},
journal = {Applied Soft Computing},
year = 2015,
volume = 27,
pages = {490--503}
}
@article{LiaMonStu13:soco,
author = {Liao, Tianjun and Marco A. {Montes de Oca} and Thomas St{\"u}tzle },
title = {Computational results for an automatically tuned {CMA-ES}
with increasing population size on the {CEC'05} benchmark
set},
journal = {Soft Computing},
pages = {1031--1046},
volume = 17,
number = 6,
year = 2013,
doi = {0.1007/s00500-012-0946-x}
}
@article{LiaSocMonStuDor2014,
author = {Liao, Tianjun and Krzysztof Socha and Marco A. {Montes de Oca} and Thomas St{\"u}tzle and Marco Dorigo },
title = {Ant Colony Optimization for Mixed-Variable Optimization
Problems},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2014,
volume = 18,
number = 4,
pages = {503--518},
keywords = {ACOR}
}
@article{LiaStuMonDor2014,
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},
journal = {European Journal of Operational Research},
year = 2014,
volume = 234,
number = 3,
pages = {597--609}
}
@article{LiaTseLua07,
author = { C.-J. Liao and C.-T. Tseng and P. Luarn },
title = {A Discrete Version of Particle Swarm Optimization
for Flowshop Scheduling Problems},
journal = {Computers \& Operations Research},
volume = 34,
number = 10,
pages = {3099--3111},
year = 2007
}
@article{LieDaoVerDer2019tec,
author = { Arnaud Liefooghe and Fabio Daolio and Bilel Derbel and Verel, S{\'e}bastien and Aguirre, Hern\'{a}n E. and Tanaka, Kiyoshi },
journal = {IEEE Transactions on Evolutionary Computation},
number = 6,
pages = {1063--1077},
title = {Landscape-Aware Performance Prediction for Evolutionary
Multi-objective Optimization},
volume = 24,
year = 2020
}
@article{LieHumMes2011,
author = { Arnaud Liefooghe and J{\'e}r{\'e}mie Humeau and Salma Mesmoudi and Laetitia Jourdan and Talbi, El-Ghazali },
title = {On dominance-based multiobjective local search: design,
implementation and experimental analysis on scheduling and
traveling salesman problems},
journal = {Journal of Heuristics},
volume = 18,
number = 2,
pages = {317--352},
year = 2012,
abstract = {This paper discusses simple local search approaches for
approximating the efficient set of multiobjective
combinatorial optimization problems. We focus on algorithms
defined by a neighborhood structure and a dominance relation
that iteratively improve an archive of nondominated
solutions. Such methods are referred to as dominance-based
multiobjective local search. We first provide a concise
overview of existing algorithms, and we propose a model
trying to unify them through a fine-grained
decomposition. The main problem-independent search components
of dominance relation, solution selection, neighborhood
exploration and archiving are largely discussed. Then, a
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
directions for future research are highlighted.},
doi = {10.1007/s10732-011-9181-3}
}
@article{LieJouTal2011paradiseo,
title = {A Software Framework Based on a Conceptual Unified
Model for Evolutionary Multiobjective Optimization:
{ParadisEO}-{MOEO}},
author = { Arnaud Liefooghe and Laetitia Jourdan and Talbi, El-Ghazali },
journal = {European Journal of Operational Research},
volume = 209,
number = 2,
pages = {104--112},
year = 2011
}
@article{LieVerHao2014hybrid,
author = { Arnaud Liefooghe and Verel, S{\'e}bastien and Jin-Kao Hao },
title = {A hybrid metaheuristic for multiobjective unconstrained
binary quadratic programming},
journal = {Applied Soft Computing},
year = 2014,
volume = 16,
pages = {10--19},
publisher = {Elsevier}
}
@article{LikKoc2007predictive,
title = {Predictive control of a gas--liquid separation plant based on
a {Gaussian} process model},
author = {Likar, Bojan and Kocijan, Ju{\v{s}}},
journal = {Computers \& Chemical Engineering},
volume = 31,
number = 3,
pages = {142--152},
year = 2007,
publisher = {Elsevier},
doi = {10.1016/j.compchemeng.2006.05.011}
}
@article{LinEggFeu2022smac3,
author = { Marius Thomas Lindauer and Katharina Eggensperger and Matthias Feurer and Biedenkapp, Andr{\'e} and Difan Deng and Carolin Benjamins and Tim
Ruhkopf and René Sass and Frank Hutter },
title = {{SMAC3}: A Versatile Bayesian Optimization Package for
Hyperparameter Optimization},
journal = {Journal of Machine Learning Research},
year = 2022,
volume = 23,
pages = {1--9},
epub = {http://jmlr.org/papers/v23/21-0888.html}
}
@article{LinHooHutSch2015autofolio,
title = {{AutoFolio}: An Automatically Configured Algorithm Selector},
author = { Marius Thomas Lindauer and Holger H. Hoos and Frank Hutter and Schaub, Torsten},
journal = {Journal of Artificial Intelligence Research},
volume = 53,
pages = {745--778},
year = 2015
}
@article{LinKer73,
author = {S. Lin and B. W. Kernighan},
title = {An Effective Heuristic Algorithm for the Traveling
Salesman Problem},
journal = {Operations Research},
year = 1973,
volume = 21,
number = 2,
pages = {498--516}
}
@article{LinVanKot2019,
title = {The algorithm selection competitions 2015 and 2017},
author = { Marius Thomas Lindauer and van Rijn, Jan N. and Kotthoff, Lars},
journal = {Artificial Intelligence},
volume = 272,
pages = {86--100},
year = 2019
}
@article{LisWit2015tcs,
title = {Runtime Analysis of Ant Colony Optimization on Dynamic
Shortest Path Problems},
journal = {Theoretical Computer Science},
volume = 561,
number = {Part A},
pages = {73--85},
year = 2015,
doi = {10.1016/j.tcs.2014.06.035},
author = { Andrei Lissovoi and Carsten Witt },
abstract = {A simple ACO algorithm called $\lambda$-MMAS for dynamic
variants of the single-destination shortest paths problem is
studied by rigorous runtime analyses. Building upon previous
results for the special case of 1-MMAS, it is studied to what
extent an enlarged colony using $\lambda$ ants per vertex
helps in tracking an oscillating optimum. It is shown that
easy cases of oscillations can be tracked by a constant
number of ants. However, the paper also identifies more
involved oscillations that with overwhelming probability
cannot be tracked with any polynomial-size colony. Finally,
parameters of dynamic shortest-path problems which make the
optimum difficult to track are discussed. Experiments
illustrate theoretical findings and conjectures. }
}
@article{LitMurSweKar63,
author = {J. D. C. Little and K. G. Murty and D. W. Sweeney and C. Karel},
title = {An Algorithm for the Traveling Salesman Problem},
journal = {Operations Research},
year = 1963,
volume = 11,
pages = {972--989}
}
@article{LiuMalWanBre2017vis,
author = { Shusen Liu and Dan Maljovec and Bei Wang and
Peer-Timo Bremer and Valerio Pascucci},
title = {Visualizing High-Dimensional Data: Advances in the Past
Decade},
doi = {10.1109/TVCG.2016.2640960},
year = 2017,
journal = { IEEE Transactions on Visualization and Computer Graphics },
volume = 23,
number = 3
}
@article{LiuRee2001,
author = {Jiyin Liu and Colin R. Reeves },
title = {Constructive and Composite Heuristic Solutions to the
{P//$\Sigma$Ci} Scheduling Problem},
journal = {European Journal of Operational Research},
volume = 132,
number = 2,
pages = {439--452},
year = 2001,
doi = {10.1016/S0377-2217(00)00137-5}
}
@article{LiuYenGon2018twoarch,
title = {A multimodal multiobjective evolutionary algorithm using
two-archive and recombination strategies},
author = {Liu, Yiping and Yen, Gary G. and Gong, Dunwei},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2018,
number = 4,
pages = {660--674},
volume = 23
}
@article{LocSch1999mlsl,
author = {Locatelli, Marco and Schoen, Fabio},
title = {Random Linkage: a family of acceptance/rejection algorithms
for global optimisation.},
journal = {Mathematical Programming},
year = 1999,
volume = 85,
number = 2,
keywords = {Multi-Level Single-Linkage (MLSL)}
}
@article{LodMarMon2002,
title = {Two-dimensional packing problems: A survey},
author = { Andrea Lodi and Silvano Martello and Monaci, Michele },
journal = {European Journal of Operational Research},
volume = 141,
number = 2,
pages = {241--252},
year = 2002,
doi = {10.1016/S0377-2217(02)00123-6}
}
@article{LodMarVig1999binpack,
title = {Heuristic and metaheuristic approaches for a class of
two-dimensional bin packing problems},
author = { Andrea Lodi and Silvano Martello and Vigo, Daniele },
journal = {INFORMS Journal on Computing},
volume = 11,
number = 4,
pages = {345--357},
year = 1999,
publisher = {{INFORMS}},
doi = {10.1287/ijoc.11.4.345}
}
@article{LodMarVig2004tspack,
title = {{TSpack}: a unified tabu search code for multi-dimensional bin
packing problems},
author = { Andrea Lodi and Silvano Martello and Vigo, Daniele },
journal = {Annals of Operations Research},
volume = 131,
number = {1-4},
pages = {203--213},
year = 2004,
publisher = {Springer},
doi = {10.1023/B:ANOR.0000039519.03572.08}
}
@article{LodZar2017learning,
title = {On Learning and Branching: A Survey},
author = { Andrea Lodi and Zarpellon, Giulia},
journal = {TOP},
volume = 25,
pages = {207--236},
year = 2017,
publisher = {Springer}
}
@article{LohHorLin2008antennas,
author = {Lohn, Jason D. and Hornby, Gregory S. and Linden, Derek S.},
title = {Human-competitive Evolved Antennas},
journal = {Artificial Intelligence for Engineering Design, Analysis and Manufacturing},
volume = 22,
number = 3,
year = 2008,
pages = {235--247},
doi = {10.1017/s0890060408000164},
publisher = {Cambridge University Press},
annote = {Evolutionary optimization of antennas for NASA}
}
@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{LouBoi2008vns_anytime,
author = { Samir Loudni and Patrice Boizumault },
title = {Combining {VNS} with constraint programming for
solving anytime optimization problems},
journal = {European Journal of Operational Research},
year = 2008,
volume = 191,
pages = {705--735},
doi = {10.1016/j.ejor.2006.12.062}
}
@article{Lourenco1995,
author = { Helena R. {Louren{\c c}o} },
title = {Job-Shop Scheduling: Computational Study of Local
Search and Large-Step Optimization Methods},
journal = {European Journal of Operational Research},
year = 1995,
volume = 83,
number = 2,
pages = {347--364}
}
@article{LovTor2001aor,
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.},
number = 2,
journal = {International Journal of Production Economics},
year = 2009,
keywords = {genetic algorithm, multi-mode resource-constrained
project scheduling},
pages = {302--316}
}
@article{LozGloGarRodMar2014,
author = { Manuel Lozano and Fred Glover and Carlos Garc{\'i}a-Mart{\'i}nez and Francisco J. Rodr{\'i}guez and Rafael Mart{\'i} },
title = {Tabu Search with Strategic Oscillation for the Quadratic Minimum Spanning Tree},
journal = {IIE Transactions},
year = 2014,
volume = 46,
number = 4,
pages = {414--428}
}
@article{LozMolGar2011,
author = { Manuel Lozano and Daniel Molina and Carlos Garc{\'i}a-Mart{\'i}nez },
title = {Iterated Greedy for the Maximum Diversity Problem},
journal = {European Journal of Operational Research},
year = 2011,
volume = 214,
number = 1,
pages = {31--38}
}
@article{LuGloHao2010ejor,
author = { L{\"u}, Zhipeng and Fred Glover and Jin-Kao Hao },
title = {A hybrid metaheuristic approach to solving the
{UBQP} problem},
journal = {European Journal of Operational Research},
volume = 207,
number = 3,
pages = {1254--1262},
year = 2010,
doi = {10.1016/j.ejor.2010.06.039}
}
@article{Lucas2014ising,
title = {Ising formulations of many {NP} problems},
author = {Lucas, Andrew},
journal = { Frontiers in Physics },
volume = 2,
pages = 5,
year = 2014,
publisher = {Frontiers},
doi = {10.3389/fphy.2014.00005}
}
@article{LundyMees1986,
title = {Convergence of an Annealing Algorithm},
author = { M. Lundy and A. Mees },
journal = {Mathematical Programming},
volume = 34,
number = 1,
pages = {111--124},
year = 1986
}
@article{LusTeg2009tpls,
author = { Thibaut Lust and Jacques Teghem },
title = {Two-phase {Pareto} local search for the biobjective traveling
salesman problem},
doi = {10.1007/s10732-009-9103-9},
abstract = {In this work, we present a method, called {Two-Phase}
{Pareto} Local Search, to find a good approximation of the
efficient set of the biobjective traveling salesman
problem. In the first phase of the method, an initial
population composed of a good approximation of the extreme
supported efficient solutions is generated. We use as second
phase a {Pareto} Local Search method applied to each solution
of the initial population. We show that using the combination
of these two techniques: good initial population generation
plus {Pareto} Local Search gives better results than
state-of-the-art algorithms. Two other points are introduced:
the notion of ideal set and a simple way to produce
near-efficient solutions of multiobjective problems, by using
an efficient single-objective solver with a data perturbation
technique. },
journal = {Journal of Heuristics},
volume = 16,
number = 3,
pages = {475--510},
year = 2010
}
@article{LusTeg2010arxiv,
author = { Thibaut Lust and Jacques Teghem },
title = {The multiobjective multidimensional knapsack
problem: a survey and a new approach},
journal = {Arxiv preprint arXiv:1007.4063},
year = 2010,
note = {Published as~\cite{LusTeg2012itor}}
}
@article{LusTeg2012itor,
title = {The multiobjective multidimensional knapsack
problem: a survey and a new approach},
author = { Thibaut Lust and Jacques Teghem },
journal = {International Transactions in Operational Research},
volume = 19,
number = 4,
pages = {495--520},
year = 2012,
doi = {10.1111/j.1475-3995.2011.00840.x}
}
@article{LustJasz09btsp,
author = { Thibaut Lust and Andrzej Jaszkiewicz },
title = {Speed-up techniques for solving large-scale biobjective
{TSP}},
journal = {Computers \& Operations Research},
year = 2010,
doi = {10.1016/j.cor.2009.01.005},
pages = {521--533},
volume = 37,
number = 3,
keywords = {Multiobjective combinatorial optimization, Hybrid
metaheuristics, TSP, Local search, Speed-up techniques}
}
@article{LuvBarBri2014,
title = {A survey on multi-objective evolutionary algorithms
for many-objective problems},
author = { C. von L{\"u}cken and Benjam{\'i}n Bar{\'a}n and Brizuela, Carlos},
pages = {707--756},
year = 2014,
journal = {Computational Optimization and Applications},
volume = 58,
number = 3
}
@article{MaaHin2008tsne,
author = {Laurens van der Maaten and Geoffrey Hinton},
title = {Visualizing Data using t-{SNE}},
journal = {Journal of Machine Learning Research},
year = 2008,
volume = 9,
number = 86,
pages = {2579--2605},
epub = {http://jmlr.org/papers/v9/vandermaaten08a.html}
}
@article{MachBelTal2018ale,
author = {Machado, Marlos C. and Bellemare, Marc G. and Talvitie, Erik
and Veness, Joel and Hausknecht, Matthew and Bowling,
Michael},
title = {Revisiting the {Arcade} {Learning} {Environment}: Evaluation
Protocols and Open Problems for General Agents},
year = 2018,
publisher = {AI Access Foundation},
address = {El Segundo, CA, USA},
volume = 61,
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,
pages = {523--562},
numpages = 40
}
@article{Madden2012,
title = {From Databases to Big Data},
author = {Madden, Sam},
journal = {IEEE Internet Computing},
volume = 16,
number = 3,
year = 2012
}
@article{MahFesDam2007harmony,
author = {M. Mahdavi and M. Fesanghary and E. Damangir},
title = {An improved harmony search algorithm for solving optimization
problems},
journal = {Applied Mathematics and Computation},
volume = 188,
number = 2,
pages = {1567--1579},
year = 2007,
doi = {10.1016/j.amc.2006.11.033},
keywords = {Global optimization, Heuristics, Harmony search algorithm,
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)
algorithm. In this paper the impacts of constant parameters
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
reveal that the proposed algorithm can find better solutions
when compared to HS and other heuristic or deterministic
methods and is a powerful search algorithm for various
engineering optimization problems.}
}
@article{MaiRon2012,
title = {New heuristics for total tardiness minimization in
a flexible flowshop},
author = {Mainieri, Guilherme B. and Ronconi, D{\'e}bora P.},
journal = {Optimization Letters},
pages = {1--20},
year = 2012
}
@article{MaieSimp03:ACODesignWDN,
author = { Holger R. Maier and Angus R. Simpson and Aaron C. Zecchin and Wai Kuan Foong and Kuang Yeow Phang and Hsin Yeow Seah and Tan, Chan Lim },
title = {Ant Colony Optimization for Design of Water Distribution
Systems},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
volume = 129,
number = 3,
pages = {200--209},
date = {2003-05/2003-06},
year = 2003,
month = may # { / } # jun
}
@article{MMalDas2022twiarch,
title = {A twin-archive guided decomposition based
multi/many-objective evolutionary algorithm},
author = {M, Sri Srinivasa Raju and Mallipeddi, Rammohan and Das, Kedar Nath},
journal = {Swarm and Evolutionary Computation},
volume = 71,
pages = 101082,
year = 2022,
doi = {10.1016/j.swevo.2022.101082},
publisher = {Elsevier}
}
@article{MalEng2013survey,
author = {Katherine M. Malan and Andries Engelbrecht },
title = {A survey of techniques for characterising fitness landscapes
and some possible ways forward},
journal = {Information Sciences},
volume = 241,
pages = {148--163},
year = 2013,
issn = {0020-0255},
doi = {10.1016/j.ins.2013.04.015}
}
@article{Males85,
author = { R. M. Males and R. M. Clark and P. J. Wehrman and W. E. Gateset },
title = {Algorithm for mixing problems in water systems},
journal = { Journal of Hydraulic Engineering, {ASCE}},
year = 1985,
volume = 111,
number = 2,
pages = {206--219}
}
@article{Man1999:joc,
author = { Vittorio Maniezzo },
title = {Exact and Approximate Nondeterministic Tree-Search Procedures
for the Quadratic Assignment Problem},
journal = {INFORMS Journal on Computing},
year = 1999,
volume = 11,
number = 4,
pages = {358--369}
}
@article{ManCar2000:fgcs,
author = { Vittorio Maniezzo and A. Carbonaro },
title = {An {ANTS} Heuristic for the Frequency Assignment Problem},
journal = {Future Generation Computer Systems},
year = 2000,
volume = 16,
number = 8,
pages = {927--935}
}
@article{ManCol99,
author = { Vittorio Maniezzo and Alberto Colorni },
title = {The {Ant} {System} Applied to the Quadratic
Assignment Problem},
journal = {IEEE Transactions on Knowledge and Data Engineering},
year = 1999,
volume = 11,
number = 5,
pages = {769--778}
}
@article{Mar84,
title = {On a multicritera shortest path problem},
journal = {European Journal of Operational Research},
volume = 16,
pages = {236--245},
year = 1984,
author = {E. Q. V. Martins}
}
@article{MarAro2004smo,
author = {Marler, R. T. and Arora, J. S.},
title = {Survey of multi-objective optimization methods for
engineering},
journal = {Structural and Multidisciplinary Optimization},
year = 2004,
volume = 26,
number = 6,
pages = {369--395},
month = apr,
doi = {10.1007/s00158-003-0368-6},
annote = {Discusses a priori (scalarized) methods.}
}
@article{MarCavHer2023repr,
author = { Raul Mart{\'i}n-Santamar{\'i}a and Cavero, Sergio and Herrán, Alberto and Duarte, Abraham and Colmenar, J. Manuel },
title = {A Practical Methodology for Reproducible Experimentation: An
Application to the Double-Row Facility Layout Problem},
journal = {Evolutionary Computation},
year = 2023,
pages = {1--35},
month = nov,
issn = {1063-6560},
doi = {10.1162/evco_a_00317},
publisher = {MIT Press},
keywords = {irace}
}
@article{MarDeBHaeVanSnoBae07,
author = {D. Martens and M. De Backer and R. Haesen and
J. Vanthienen and M. Snoeck and B. Baesens},
title = {Classification With Ant Colony Optimization},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2007,
volume = 11,
number = 5,
pages = {651--665}
}
@article{MarLopStuCol2024auto,
author = { Raul Mart{\'i}n-Santamar{\'i}a and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle and Colmenar, J. Manuel },
title = {On the automatic generation of metaheuristic algorithms for
combinatorial optimization problems},
journal = {European Journal of Operational Research},
year = 2024,
doi = {10.1016/j.ejor.2024.06.001},
abstract = {Metaheuristic algorithms have become one of the preferred
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}
}
@article{MarMarWeiWol2002,
title = {Cutting planes in integer and mixed integer programming},
author = {Marchand, Hugues and Martin, Alexander and Weismantel, Robert and Wolsey, Laurence},
journal = {Discrete Applied Mathematics},
volume = 123,
number = {1--3},
pages = {397--446},
year = 2002,
publisher = {Elsevier}
}
@article{MarMoo1997air,
author = {O. Maron and A. W. Moore},
title = {The Racing Algorithm: {Model} Selection for Lazy Learners},
journal = {Artificial Intelligence Research},
year = 1997,
volume = 11,
number = {1--5},
pages = {193--225},
doi = {10.1023/A:1006556606079}
}
@article{MarOtt1995,
author = { Olivier Martin and S. W. Otto},
title = {Partitioning of Unstructured Meshes for Load
Balancing},
journal = {Concurrency: Practice and Experience},
year = 1995,
volume = 7,
number = 4,
pages = {303--314}
}
@article{MarOtt1996:aor,
author = { Olivier Martin and S. W. Otto},
title = {Combining Simulated Annealing with Local Search
Heuristics},
journal = {Annals of Operations Research},
year = 1996,
volume = 63,
pages = {57--75}
}
@article{MarOttFel91:cs,
author = { Olivier Martin and S. W. Otto and E. W. Felten},
title = {Large-Step {Markov} Chains for the Traveling
Salesman Problem},
journal = {Complex Systems},
year = 1991,
volume = 5,
number = 3,
pages = {299--326}
}
@article{MarOttFel92:orl,
author = { Olivier Martin and S. W. Otto and E. W. Felten},
title = {Large-step {Markov} Chains for the {TSP}
Incorporating Local Search Heuristics},
journal = {Operations Research Letters},
year = 1992,
volume = 11,
number = 4,
pages = {219--224}
}
@article{MarReiDua2012,
author = { Rafael Mart{\'i} and Gerhard Reinelt and Duarte, Abraham },
title = {A Benchmark Library and a Comparison of Heuristic Methods for the Linear Ordering Problem},
journal = {Computational Optimization and Applications},
year = 2012,
volume = 51,
number = 3,
pages = {1297--1317}
}
@article{MarSanPer2022strategic,
author = { Raul Mart{\'i}n-Santamar{\'i}a and Jes{\'u}s S{\'a}nchez-Oro and S. P\'{e}rez-Pel\'{o} and Duarte, Abraham },
title = {Strategic oscillation for the balanced minimum sum-of-squares
clustering problem},
journal = {Information Sciences},
year = 2022,
volume = 585,
pages = {529--542},
doi = {10.1016/j.ins.2021.11.048}
}
@article{MarTot1990dam,
author = { Silvano Martello and Paolo Toth },
title = {Lower bounds and reduction procedures for the bin
packing problem},
journal = {Discrete Applied Mathematics},
volume = 28,
number = 1,
year = 1990,
pages = {59--70},
doi = {10.1016/0166-218X(90)90094-S}
}
@article{MarVig1998exact,
title = {Exact solution of the two-dimensional finite bin packing
problem},
author = { Silvano Martello and Vigo, Daniele },
journal = {Management Science},
volume = 44,
number = 3,
pages = {388--399},
year = 1998,
publisher = {{INFORMS}},
doi = {10.1287/mnsc.44.3.388}
}
@article{MasHwa1981isgp,
author = {Masud, Abu S. and Hwang, C. L.},
title = {Interactive Sequential Goal Programming},
journal = {Journal of the Operational Research Society},
year = 1981,
volume = 32,
number = 5,
pages = {391--400},
month = may,
issn = {1476-9360},
doi = {10.1057/jors.1981.76},
abstract = {This paper introduces a new solution method based on Goal
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},
journal = {Expert Systems with Applications},
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,
pages = {33--42}
}
@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)}
}
@article{McCormick04,
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.}
}
@article{McDermott2020nfl,
author = {James McDermott},
title = {When and Why Metaheuristics Researchers can Ignore "No Free
Lunch" Theorems},
journal = {{SN} Computer Science},
volume = 1,
number = 60,
pages = {1--18},
year = 2020,
doi = {10.1007/s42979-020-0063-3}
}
@article{McG1992vrt,
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.},
volume = 24,
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}
}
@article{McG1998joc,
author = { Catherine C. McGeoch },
title = {Toward an Experimental Method for Algorithm Simulation},
journal = {INFORMS Journal on Computing},
year = 1996,
volume = 8,
number = 1,
pages = {1--15},
doi = {10.1287/ijoc.8.1.1}
}
@article{MckBecCon1979lhs,
title = {A Comparison of Three Methods for Selecting Values of Input
Variables in the Analysis of Output from a Computer Code},
author = {Michael D. McKay and Richard J. Beckman and W. J. Conover },
journal = {Technometrics},
year = 1979,
number = 2,
pages = {239--245},
volume = 21,
abstract = {Two types of sampling plans are examined as alternatives to
simple random sampling in Monte Carlo studies. These plans
are shown to be improvements over simple random sampling with
respect to variance for a class of estimators which includes
the sample mean and the empirical distribution function.},
publisher = {American Statistical Association and American Society for
Quality},
doi = {10.2307/1268522}
}
@article{MckBerMaiFic2018combining,
title = {Combining local preferences with multi-criteria decision
analysis and linear optimization to develop feasible energy
concepts in small communities},
author = {McKenna, Russell and Bertsch, Valentin and Mainzer, Kai and
Fichtner, Wolf},
journal = {European Journal of Operational Research},
volume = 268,
number = 3,
pages = {1092--1110},
year = 2018
}
@article{Mckay2010,
author = {Mckay, Robert I. and Hoai, Nguyen Xuan and Whigham,
Peter Alexander and Shan, Yin and O'Neill, Michael },
title = {Grammar-based Genetic Programming: A Survey},
journal = {Genetic Programming and Evolvable Machines},
volume = 11,
number = {3-4},
month = sep,
year = 2010,
pages = {365--396},
doi = {10.1007/s10710-010-9109-y}
}
@article{Meer2007,
author = {Klaus Meer},
title = {Simulated annealing versus {Metropolis} for a {TSP} instance},
journal = {Information Processing Letters},
volume = 104,
number = 6,
year = 2007,
pages = {216--219}
}
@article{MelDyeBlu2017neural,
author = {G{\'{a}}bor Melis and Chris Dyer and Phil Blunsom},
title = {On the State of the Art of Evaluation in Neural Language
Models},
journal = {Arxiv preprint arXiv:1807.02811},
year = 2017,
url = {http://arxiv.org/abs/1707.05589}
}
@article{MelNicSal2009facility,
title = {Facility location and supply chain management: {A} review},
author = {Melo, M. T. and Nickel, S. and Saldanha-da-Gama, F. },
year = 2009,
journal = {European Journal of Operational Research},
volume = 196,
number = 2,
pages = {401--412},
doi = {10.1016/j.ejor.2008.05.007}
}
@article{Men2008,
author = {Ole J. Mengshoel},
title = {Understanding the role of noise in stochastic local search:
Analysis and experiments},
journal = {Artificial Intelligence},
volume = 172,
number = 8,
pages = {955--990},
year = 2008
}
@article{MerCot2006sigevo,
author = { Juan-Juli{\'a}n Merelo and Carlos Cotta },
title = {Building bridges: the role of subfields in metaheuristics},
journal = { {SIGEVO}lution },
year = 2006,
volume = 1,
number = 4,
pages = {9--15},
doi = {10.1145/1229735.1229737}
}
@article{MerFre02:cs,
author = { Peter Merz and Bernd Freisleben },
title = {Memetic Algorithms for the Traveling Salesman
Problem},
journal = {Complex Systems},
year = 2001,
volume = 13,
number = 4,
pages = {297--345}
}
@article{MerFre2000:tec,
author = { Peter Merz and Bernd Freisleben },
title = {Fitness Landscape Analysis and Memetic Algorithms for the Quadratic Assignment Problem},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2000,
volume = 4,
number = 4,
pages = {337--352}
}
@article{MerKat2004ubqp,
author = { Peter Merz and Kengo Katayama},
title = {Memetic algorithms for the unconstrained binary quadratic
programming problem},
journal = {BioSystems},
volume = 78,
number = 1,
pages = {99--118},
year = 2004,
doi = {10.1016/j.biosystems.2004.08.002}
}
@article{MerMid2002:appi,
author = { D. Merkle and Martin Middendorf },
title = {Ant Colony Optimization with Global Pheromone
Evaluation for Scheduling a Single Machine},
journal = {Applied Intelligence},
year = 2003,
volume = 18,
number = 1,
pages = {105--111}
}
@article{MerMid2002:ec,
author = { D. Merkle and Martin Middendorf },
title = {Modeling the Dynamics of Ant Colony Optimization},
journal = {Evolutionary Computation},
volume = 10,
number = 3,
pages = {235--262},
year = 2002
}
@article{MerMidSch02:tec,
author = { D. Merkle and Martin Middendorf and Hartmut Schmeck },
title = {Ant Colony Optimization for Resource-Constrained
Project Scheduling},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2002,
volume = 6,
number = 4,
pages = {333--346}
}
@article{Merz2002joh,
author = { Peter Merz and Bernd Freisleben },
title = {Greedy and Local Search Heuristics for Unconstrained
Binary Quadratic Programming},
year = 2002,
journal = {Journal of Heuristics},
volume = 8,
number = 2,
doi = {10.1023/A:1017912624016},
pages = {197--213}
}
@article{MesSilMelMir20152esa,
author = {Rafael G. Mesquita and Ricardo M. A. Silva and Carlos A. B. Mello and P\'{e}ricles B. C. Miranda},
title = {Parameter tuning for document image binarization using a racing algorithm},
journal = {Expert Systems with Applications},
volume = 42,
number = 5,
pages = {2593--2603},
year = 2015,
doi = {10.1016/j.eswa.2014.10.039},
keywords = {irace}
}
@article{MetRosRosTel53,
author = {N. Metropolis and A. W. Rosenbluth and M. N. Rosenbluth and A. Teller and E. Teller},
title = {Equation of State Calculations by Fast Computing Machines},
journal = {Journal of Chemical Physics},
year = 1953,
volume = 21,
pages = {1087--1092}
}
@article{MeuDor2002:al,
author = {Nicolas Meuleau and Marco Dorigo },
title = {Ant Colony Optimization and Stochastic Gradient Descent},
volume = 8,
number = 2,
pages = {103--121},
journal = {Artificial Life},
year = 2002
}
@article{MeuRakWon2020iclrbb,
author = {Laurent Meunier and Herilalaina Rakotoarison and Pak{-}Kan
Wong and Baptiste Rozi{\`{e}}re and J{\'{e}}r{\'{e}}my Rapin and Olivier Teytaud and Antoine Moreau and Carola Doerr },
title = {Black-Box Optimization Revisited: Improving Algorithm
Selection Wizards through Massive Benchmarking},
journal = {Arxiv preprint arXiv:2010.04542},
year = 2020,
doi = {10.48550/arXiv.2010.04542},
keywords = {Nevergrad, NGOpt}
}
@article{MeuRakWon2022ngopt,
author = {Laurent Meunier and Herilalaina Rakotoarison and Pak{-}Kan
Wong and Baptiste Rozi{\`{e}}re and J{\'{e}}r{\'{e}}my Rapin and Olivier Teytaud and Antoine Moreau and Carola Doerr },
title = {Black-Box Optimization Revisited: Improving Algorithm
Selection Wizards Through Massive Benchmarking},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2022,
volume = 26,
number = 3,
pages = {490--500},
doi = {10.1109/TEVC.2021.3108185},
keywords = {nevergrad, NGOpt}
}
@article{Mhallah2014,
author = {R. {M'Hallah}},
title = {An iterated local search variable neighborhood descent hybrid heuristic for the total earliness tardiness permutation flow shop},
journal = {International Journal of Production Research},
year = 2014,
volume = 52,
number = 13,
pages = {3802--3819}
}
@article{MicDasRic1996cie,
author = { Zbigniew Michalewicz and Dipankar Dasgupta and Rodolphe
G. Le Riche and Marc Schoenauer },
title = {Evolutionary algorithms for constrained engineering
problems},
journal = {Computers and Industrial Engineering},
volume = 30,
number = 4,
pages = {851--870},
year = 1996,
doi = {10.1016/0360-8352(96)00037-X}
}
@article{MicPriAmoYal2014:cor,
author = {Julien Michallet and Christian Prins and Farouk Yalaoui and Gr{\'{e}}goire Vitry},
title = {Multi-start Iterated Local Search for the Periodic Vehicle Routing
Problem with Time Windows and Time Spread Constraints on Services},
journal = {Computers \& Operations Research},
year = 2014,
volume = 41,
pages = {196--207}
}
@article{Mie2014or,
title = {Survey of methods to visualize alternatives in multiple
criteria decision making problems},
author = { Kaisa Miettinen },
journal = {OR Spectrum},
volume = 36,
number = 1,
pages = {3--37},
year = 2014,
doi = {10.1007/s00291-012-0297-0}
}
@article{MieEskRui2010nautilus,
author = { Kaisa Miettinen and Eskelinen, Petri and Francisco Ruiz and Mariano Luque },
title = {{NAUTILUS} method: {An} interactive technique in
multiobjective optimization based on the nadir point},
journal = {European Journal of Operational Research},
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}
}
@article{MieMusSte2014nimbus,
title = {Interactive multiobjective optimization with {NIMBUS} for
decision making under uncertainty},
author = { Kaisa Miettinen and Mustajoki, Jyri and T. J. Stewart },
journal = {OR Spectrum},
volume = 36,
number = 1,
pages = {39--56},
year = 2014
}
@article{MilAnd2005repl,
author = {R. B. {Millar} and M. J. {Anderson}},
title = {Remedies for pseudoreplication},
journal = { Fisheries Research },
volume = 70,
number = {2--3},
pages = {397--407},
year = 2004,
doi = {10.1016/j.fishres.2004.08.016}
}
@article{Miller1956,
title = {The magical number seven, plus or minus two: Some limits on
our capacity for processing information},
author = {Miller, George A.},
journal = {Psychological Review},
volume = 63,
number = 2,
pages = {81--97},
year = 1956,
publisher = {American Psychological Association},
doi = {10.1037/h0043158}
}
@article{Min96auto,
author = {Steven Minton},
title = {Automatically configuring constraint satisfaction programs: A
case study},
journal = {Constraints},
year = 1996,
volume = 1,
number = 1,
pages = {7--43},
doi = {10.1007/BF00143877}
}
@article{MinRuiCia08,
author = { Gerardo Minella and Rub{\'e}n Ruiz and M. Ciavotta },
title = {A Review and Evaluation of Multiobjective Algorithms
for the Flowshop Scheduling Problem},
journal = {INFORMS Journal on Computing},
volume = 20,
number = 3,
pages = {451--471},
year = 2008
}
@article{MisAfsLar2022rximo,
author = {Misitano, Giovanni and Afsar, Bekir and Larraga, Giomara and Kaisa Miettinen },
title = {Towards explainable interactive multiobjective optimization:
{R-XIMO}},
journal = {Autonomous Agents and Multi-Agent Systems},
year = 2022,
volume = 36,
number = 42,
doi = {10.1007/s10458-022-09577-3}
}
@article{MisKuz2018,
title = {Investigating some strategies for construction of
initial populations in genetic algorithms},
author = { Misevi{\v{c}}ius, Alfonsas and Kuznecovait{\.e}, Dovil{\.e}},
journal = {Computational Science and Techniques},
volume = 5,
number = 1,
pages = {560--573},
year = 2018
}
@article{Misevicius2003,
author = { Misevi{\v{c}}ius, Alfonsas },
title = {Genetic Algorithm Hybridized with Ruin and Recreate Procedure: Application to the Quadratic Assignment Problem},
journal = {Knowledge-Based Systems},
year = 2003,
volume = 16,
number = {5--6},
pages = {261--268}
}
@article{Misevicius2003:inf,
title = {A modified simulated annealing algorithm for the quadratic assignment problem},
author = { Misevi{\v{c}}ius, Alfonsas },
journal = {Informatica},
volume = 14,
number = 4,
pages = {497--514},
year = 2003,
publisher = {IOS Press, Amsterdam, The Netherlands}
}
@article{MitMurPal2002unsupervised,
author = {P. Mitra and C. A. Murthy and S. K. Pal},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
title = {Unsupervised feature selection using feature similarity},
year = 2002,
volume = 24,
number = 3,
pages = {301--312},
doi = {10.1109/34.990133}
}
@article{MizKuzPla2018,
author = { Misevi{\v{c}}ius, Alfonsas and Kuznecovait{\.e}, Dovil{\.e} and
J{\={u}}rat{\.e} Platu{\v{z}}ien{\.e}},
title = {Some Further Experiments with Crossover Operators for Genetic Algorithms},
journal = {Informatica},
volume = 29,
number = 3,
year = 2018,
pages = {499--516}
}
@article{MlaHan1997:cor,
author = { Nenad Mladenovi{\'c} and Pierre Hansen },
title = {Variable Neighborhood Search},
journal = {Computers \& Operations Research},
year = 1997,
volume = 24,
number = 11,
pages = {1097--1100}
}
@article{MniKavSil2015human,
title = {Human-level control through deep reinforcement learning},
author = {Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and
Rusu, Andrei A. and Veness, Joel and Bellemare, Marc G. and
Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K.
and Ostrovski, Georg and others},
journal = {Nature},
volume = 518,
number = 7540,
pages = 529,
year = 2015,
publisher = {Nature Publishing Group}
}
@article{MocTieZil1978,
author = { Jonas Mo{\v{c}}kus and Tiesis, Vytautas and Zilinskas, Antanas},
title = {The application of bayesian methods for seeking the extremum},
journal = {Towards global optimization},
year = 1978,
pages = {117--129},
annote = {Proposed Bayesian optimization (but later than
\cite{Mockus1975})}
}
@article{MolSanHer2009gdominance,
author = { Molina, Juli{\'a}n and Luis V. Santana and Alfredo
G. Hern\'{a}ndez-D{\'i}az and Carlos A. {Coello Coello} and Rafael Caballero},
title = {{g}-Dominance: Reference point based dominance for
Multiobjective Metaheuristics},
journal = {European Journal of Operational Research},
volume = 197,
number = 2,
pages = {685--692},
month = sep,
year = 2009,
doi = {10.1016/j.ejor.2008.07.015},
annote = {Proposed $g$-NSGA-II}
}
@article{MonAydStu2011soco,
author = { Marco A. {Montes de Oca} and Do\v{g}an Ayd{\i}n and Thomas St{\"u}tzle },
title = {An Incremental Particle Swarm for Large-Scale
Continuous Optimization Problems: An Example of
Tuning-in-the-loop (Re)Design of Optimization
Algorithms},
journal = {Soft Computing},
year = 2011,
volume = 15,
number = 11,
pages = {2233--2255},
doi = {10.1007/s00500-010-0649-0}
}
@article{MonFraLiu2018bridge,
title = {Multi-criteria robust optimization framework for bridge
adaptation under climate change},
author = {Mondoro, Alysson and Frangopol, Dan M. and Liu, Liang},
journal = {Structural Safety},
volume = 74,
pages = {14--23},
year = 2018
}
@article{MonGamRiz2005,
author = { Roberto Montemanni and L. M. Gambardella and A. E. Rizzoli and
A. V. Donati},
title = {Ant colony system for a dynamic vehicle routing problem},
journal = {Journal of Combinatorial Optimization},
year = 2005,
volume = 10,
pages = {327--343}
}
@article{MonRandHen08:bias,
author = { James Montgomery and Marcus Randall and Tim Hendtlass },
title = {Solution bias in ant colony optimisation: {Lessons}
for selecting pheromone models},
journal = {Computers \& Operations Research},
volume = 35,
number = 9,
year = 2008,
pages = {2728--2749},
doi = {10.1016/j.cor.2006.12.014}
}
@article{MonRifNev2014asoc,
author = { Elizabeth Montero and Mar{\'i}a-Cristina Riff and Neveu, Bertrand},
title = {A Beginner's Buide to Tuning Methods},
journal = {Applied Soft Computing},
volume = 17,
pages = {39--51},
year = 2014,
doi = {10.1016/j.asoc.2013.12.017}
}
@article{MonStuBirDor2009tec,
author = { Marco A. {Montes de Oca} and Thomas St{\"u}tzle and Mauro Birattari and Marco Dorigo },
title = {Frankenstein's {PSO}: A Composite Particle Swarm
Optimization Algorithm},
journal = {IEEE Transactions on Evolutionary Computation},
volume = 13,
number = 5,
year = 2009,
pages = {1120--1132},
doi = {10.1109/TEVC.2009.2021465}
}
@article{MonVenSli2000:fgcs,
author = { Nicolas Monmarch{\'e} and G. Venturini and M. Slimane},
title = {On how {\em pachycondyla apicalis} ants suggest a
new search algorithm},
journal = {Future Generation Computer Systems},
year = 2000,
volume = 16,
number = 8,
pages = {937--946}
}
@article{Mor1993sim,
title = {Simulation of an adaptive behavior mechanism in an
expert decision-maker},
volume = 23,
number = 1,
journal = {IEEE Transactions on Systems, Man, and Cybernetics},
author = {Morgan, Peter D.},
year = 1993,
pages = {65--76}
}
@article{Mor80,
title = {Reducing the size of the nondominated set: Pruning
by clustering},
author = {J. N. Morse},
journal = {Computers \& Operations Research},
volume = 7,
number = {1-2},
pages = {55--66},
year = 1980
}
@article{MorDesLod2021machine,
title = {Machine-learning--based column selection for column
generation},
author = {Morabit, Mouad and Desaulniers, Guy and Andrea Lodi },
journal = {Transportation Science},
volume = 55,
number = 4,
pages = {815--831},
year = 2021,
publisher = {INFORMS},
keywords = {graph neural networks}
}
@article{MorGagGra09:ejor,
author = {Sara Morin and Caroline Gagn{\'e} and Marc Gravel},
title = {Ant colony optimization with a specialized pheromone
trail for the car-sequencing problem},
volume = 197,
doi = {10.1016/j.ejor.2008.03.033},
abstract = { This paper studies the learning process in an ant
colony optimization algorithm designed to solve the
problem of ordering cars on an assembly line
(car-sequencing problem). This problem has been
shown to be {NP-hard} and evokes a great deal of
interest among practitioners. Learning in an ant
algorithm is achieved by using an artificial
pheromone trail, which is a central element of this
metaheuristic. Many versions of the algorithm are
found in literature, the main distinction among them
being the management of the pheromone
trail. Nevertheless, few of them seek to perfect
learning by modifying the internal structure of the
trail. In this paper, a new pheromone trail
structure is proposed that is specifically adapted
to the type of constraints in the car-sequencing
problem. The quality of the results obtained when
solving three sets of benchmark problems is superior
to that of the best solutions found in literature
and shows the efficiency of the specialized trail.},
number = 3,
journal = {European Journal of Operational Research},
year = 2009,
keywords = {Ant colony {optimization,Car-sequencing}
{problem,Pheromone} {trail,Scheduling}},
pages = {1185--1191}
}
@article{MorMerLarMilTor09,
author = {Mora, A. M. and Juan-Juli{\'a}n Merelo and Jim{\'e}nez Laredo, Juan Luis and Millan, C. and Torrecillas, J.},
title = {{CHAC}, a {MOACO} algorithm for computation of bi-criteria
military unit path in the battlefield: Presentation and first
results},
journal = {International Journal of Intelligent Systems},
volume = 24,
number = 7,
publisher = {Wiley Subscription Services, Inc., A Wiley Company},
pages = {818--843},
year = 2009
}
@article{MorMit1995,
title = {Exploratory designs for computational experiments},
author = {Morris, Max D. and Mitchell, Toby J.},
journal = {Journal of Statistical Planning and Inference},
year = 1995,
number = 3,
pages = {381--402},
volume = 43,
doi = {10.1016/0378-3758(94)00035-T},
keywords = {Bayesian prediction}
}
@article{MosFon1990,
title = {Stochastic Versus Deterministic Update in Simulated Annealing},
author = { Pablo Moscato and Jos{\'e} F. Fontanari },
journal = {Physics Letters A},
volume = 146,
number = 4,
pages = {204--208},
year = 1990,
publisher = {Elsevier}
}
@article{MotMurOls1991,
author = {John Mote and Ishwar Murthy and David L. Olson},
title = {A parametric approach to solving bicriterion shortest path
problems},
journal = {European Journal of Operational Research},
volume = 53,
number = 1,
pages = {81--92},
year = 1991,
doi = {10.1016/0377-2217(91)90094-C}
}
@article{MotOlsVen1988moprog,
author = {John Mote and David L. Olson and M. A. Venkataramanan},
doi = {10.1016/0895-7177(88)90085-4},
year = 1988,
publisher = {Elsevier {BV}},
volume = 10,
number = 10,
pages = {719--729},
title = {A comparative multiobjective programming study},
journal = {Mathematical and Computer Modelling},
keywords = {artificial DM, interactive},
annote = {The purpose of this study was to systematically evaluate a
number of multiobjective programming concepts relative to
reflection of utility, assurance of nondominated solutions
and practicality for larger problems using conventional
software. In the problem used, the nonlinear simulated DM
utility function applied resulted in a nonextreme point
solution. Very often, the preferred solution could end up
being an extreme point solution, in which case the techniques
relying upon LP concepts would work as well if not better
than utilizing constrained objective attainments. The point
is that there is no reason to expect linear or near linear
utility.}
}
@article{MouDevHen2012,
author = {S\'ebastien Mouthuy and Yves Deville and van Hentenryck, Pascal },
title = {Constraint-based Very Large-Scale Neighborhood Search},
journal = {Constraints},
year = 2012,
volume = 17,
number = 2,
pages = {87--122},
doi = {10.1007/s10601-011-9114-7}
}
@article{MouKesDha2019,
author = {Mousin, Lucien and Marie-El{\'e}onore Kessaci and Dhaenens, Clarisse },
title = {Exploiting Promising Sub-Sequences of Jobs to solve the No-Wait Flowshop Scheduling Problem},
journal = {Arxiv preprint arXiv:1903.09035},
year = 2019,
url = {http://arxiv.org/abs/1903.09035}
}
@article{MouPetMcC2014mopso,
author = {Al Moubayed, Noura and Petrovski, Andrei and McCall, John },
title = {{$D^2MOPSO$}: {MOPSO} based on decomposition and dominance
with archiving using crowding distance in objective and
solution spaces},
journal = {Evolutionary Computation},
year = 2014,
volume = 22,
number = 1,
pages = {47--77}
}
@article{MouSlo1998:jgo,
author = { Vincent Mousseau and Roman S{\l}owi{\'n}ski },
year = 1998,
title = {Inferring an {ELECTRE TRI} model from assignment examples},
journal = {Journal of Global Optimization},
volume = 12,
number = 2,
pages = {157--174}
}
@article{MueSba2012ecj,
title = {Energy Landscapes of Atomic Clusters as Black Box
Optimization Benchmarks},
author = {M\"uller, Christian L. and Sbalzarini, Ivos F.},
journal = {Evolutionary Computation},
year = 2012,
number = 4,
pages = {543--573},
volume = 20,
doi = {10.1162/EVCO_a_00086},
publisher = {MIT Press}
}
@article{Muhlenbein93,
author = { H. M{\"u}hlenbein and D. Schlierkamp-Voosen },
title = {Predictive models for the breeder genetic algorithm},
journal = {Evolutionary Computation},
year = 1993,
volume = 1,
number = 1,
pages = {25--49},
note = {},
keywords = {crossover, intermediate, line}
}
@article{MunSmi2020ec,
author = { Mario A. Mu{\~{n}}oz and Kate Smith{-}Miles },
title = {Generating New Space-Filling Test Instances for Continuous
Black-Box Optimization},
doi = {10.1162/evco_a_00262},
year = 2020,
month = sep,
publisher = {{MIT} Press},
volume = 28,
number = 3,
pages = {379--404},
journal = {Evolutionary Computation}
}
@article{MunSunKirHal2015sel,
title = {Algorithm selection for black-box continuous optimization
problems: a survey on methods and challenges},
author = { Mario A. Mu{\~{n}}oz and Sun, Yuan and Kirley, Michael and
Halgamuge, Saman K.},
journal = {Information Sciences},
volume = 317,
pages = {224--245},
year = 2015
}
@article{MunVilBaaSmi2018ismlc,
author = { Mario A. Mu{\~{n}}oz and Villanova, Laura and Baatar, Davaatseren and Kate Smith{-}Miles },
title = {Instance Spaces for Machine Learning Classification},
journal = {Machine Learning},
year = 2018,
volume = 107,
number = 1,
pages = {109--147},
doi = {10.1007/s10994-017-5629-5}
}
@article{NagKob2013,
author = {Yuichi Nagata and Shigenobu Kobayashi},
title = {A Powerful Genetic Algorithm Using Edge Assembly Crossover
for the Traveling Salesman Problem},
journal = {INFORMS Journal on Computing},
year = 2013,
volume = 25,
number = 2,
pages = {346--363},
doi = {10.1287/ijoc.1120.0506},
keywords = {TSP, EAX, evolutionary algorithms},
abstract = {This paper presents a genetic algorithm (GA) for solving the
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.}
}
@article{NagRosMar2019:eo,
title = {High-performing heuristics to minimize flowtime in no-idle permutation flowshop},
author = {Marcelo S. Nagano and Fernando L. Rossi and N{\'a}dia J. Martarelli},
journal = {Engineering Optimization},
volume = 51,
number = 2,
pages = {185--198},
year = 2019
}
@article{NagSol2012,
author = {Yuichi Nagata and David Soler},
title = {A New Genetic Algorithm for the Asymmetric {TSP}},
journal = {Expert Systems with Applications},
year = 2012,
volume = 39,
number = 10,
pages = {8947--8953}
}
@article{NalOliHerSud2019,
title = {On the Analysis of Trajectory-Based Search Algorithms:
When is it Beneficial to Reject Improvements?},
author = { Samadhi Nallaperuma and Oliveto, Pietro S. and Heredia, Jorge P{\'e}rez and Dirk Sudholt },
journal = {Algorithmica},
volume = 81,
number = 2,
pages = {858--885},
year = 2019,
publisher = {Springer}
}
@article{NanShaIsh2020reverse,
title = {Reverse strategy for non-dominated archiving},
author = {Nan, Yang and Shang, Ke and Ishibuchi, Hisao and He, Linjun},
journal = {{IEEE} Access},
year = 2020,
pages = {119458--119469},
volume = 8,
publisher = {IEEE}
}
@article{NarSetTan2016sc,
author = {Narukawa, Kaname and Setoguchi, Yu and Tanigaki, Yuki and
Olhofer, Markus and Sendhoff, Bernhard and Ishibuchi, Hisao },
title = {Preference representation using {Gaussian} functions on a
hyperplane in evolutionary multi-objective optimization},
journal = {Soft Computing},
year = 2016,
volume = 20,
number = 7,
pages = {2733--2757},
month = jul,
doi = {10.1007/s00500-015-1674-9}
}
@article{NasVar2011optimx,
title = {Unifying Optimization Algorithms to Aid Software System
Users: \rpackage{optimx} for \proglang{R}},
author = {Nash, John and Varadhan, Ravi},
journal = {Journal of Statistical Software},
year = 2011,
number = 9,
pages = {1--14},
volume = 43
}
@article{NawEnsHam83,
author = { M. Nawaz and Enscore, Jr, E. and I. Ham},
title = {A Heuristic Algorithm for the $m$-Machine, $n$-Job
Flow-Shop Sequencing Problem},
journal = {Omega},
year = 1983,
volume = 11,
number = 1,
pages = {91--95},
keywords = {NEH heuristic}
}
@article{NebLopGarCoe2023automopso,
author = { 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 = {On the automatic design of multi-objective particle swarm
optimizers: experimentation and analysis},
journal = {Swarm Intelligence},
year = 2023,
doi = {10.1007/s11721-023-00227-2},
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
generate a MOPSO that significantly outperforms two
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.}
}
@article{NebLunAlb2008abyss,
author = { Nebro, Antonio J. and F. Luna and Alba, Enrique and Bernab{\'e} Dorronsoro and Durillo, Juan J. and A. Beham},
title = {{AbYSS}: Adapting Scatter Search to Multiobjective Optimization},
journal = {IEEE Transactions on Evolutionary Computation},
volume = 12,
number = 4,
month = aug,
year = 2008,
pages = {439--457},
doi = {10.1109/TEVC.2007.913109}
}
@article{NerCot2012,
author = {F. Nerri and Carlos Cotta },
title = {Memetic algorithms and memetic computing optimization:
A literature review},
journal = {Swarm and Evolutionary Computation},
year = 2012,
volume = 2,
pages = {1--14},
doi = {10.1016/j.swevo.2011.11.003}
}
@article{NeuSudWit2009:swarm,
author = { Frank Neumann and Dirk Sudholt and Carsten Witt },
title = {Analysis of different {MMAS} {ACO} algorithms on
unimodal functions and plateaus},
journal = {Swarm Intelligence},
year = 2009,
volume = 3,
number = 1,
pages = {35--68}
}
@article{NeuWit2006:eccc,
author = { Frank Neumann and Carsten Witt },
title = {Runtime Analysis of a Simple Ant Colony Optimization
Algorithm},
journal = {Electronic Colloquium on Computational Complexity (ECCC)},
volume = 13,
number = 084,
year = 2006
}
@article{NewSim1976cacm,
author = {Newell, Allen and Simon, Herbert A.},
title = {Computer Science as Empirical Inquiry: Symbols and Search},
year = 1976,
volume = 19,
number = 3,
issn = {0001-0782},
doi = {10.1145/360018.360022},
abstract = {Computer science is the study of the phenomena surrounding
computers. The founders of this society understood this very
well when they called themselves the Association for
Computing Machinery. The machine-not just the hardware, but
the programmed, living machine-is the organism we study.},
journal = {Communications of the ACM},
month = mar,
pages = {113--126},
numpages = 14,
keywords = {cognition, Turing, search, problem solving, symbols,
heuristics, list processing, computer science, artificial
intelligence, science, empirical}
}
@article{NguPriPro2012:eaai,
author = {Viet-Phuong Nguyen and Christian Prins and Caroline Prodhon},
title = {A Multi-start Iterated Local Search with Tabu List and Path Relinking
for the Two-echelon Location-routing Problem},
journal = {Engineering Applications of Artificial Intelligence},
year = 2012,
volume = 25,
number = 1,
pages = {56--71}
}
@article{NguReiRig2014,
title = {A review on simulation-based optimization methods applied to
building performance analysis},
journal = {Applied Energy},
volume = 113,
pages = {1043--1058},
year = 2014,
doi = {10.1016/j.apenergy.2013.08.061},
author = {Anh-Tuan Nguyen and Sigrid Reiter and Philippe Rigo}
}
@article{NguYanBra2012:swec,
author = {Trung Thanh Nguyen and Shengxiang Yang and J{\"u}rgen Branke },
title = {Evolutionary Dynamic Optimization: A Survey of the State of the Art},
journal = {Swarm and Evolutionary Computation},
year = 2012,
volume = 6,
pages = {1--24}
}
@article{NguZhaJohChe14:ec,
author = {Su Nguyen and Zhang, Mengjie and Mark Johnston and Tan, Kay Chen },
title = {Genetic Programming for Evolving Due-Date Assignment Models in Job Shop Environments},
journal = {Evolutionary Computation},
year = 2014,
volume = 22,
number = 1,
pages = {105--138}
}
@article{NguZhaJohChe14:tec,
author = {Su Nguyen and Zhang, Mengjie and Mark Johnston and Tan, Kay Chen },
title = {Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2014,
volume = 18,
number = 2,
pages = {193--208}
}
@article{NigAkgGen++2017:aij,
author = {Peter Nightingale and \"Ozgu\"ur Akg\"un and Ian P. Gent and Christopher Jefferson and Ian Miguel and Patrick Spracklen},
title = {Automatically Improving Constraint Models in {Savile} {Row}},
journal = {Artificial Intelligence},
year = 2017,
volume = 251,
pages = {35--61}
}
@article{NinYou2019optunc,
author = {Chao Ning and Fengqi You},
title = {Optimization under uncertainty in the era of big data and
deep learning: When machine learning meets mathematical
programming},
journal = {Computers \& Chemical Engineering},
year = 2019,
volume = 125,
pages = {434--448},
doi = {10.1016/j.compchemeng.2019.03.034},
keywords = {Data-driven optimization, Decision making under uncertainty,
Big data, Machine learning, Deep learning},
abstract = {This paper reviews recent advances in the field of
optimization under uncertainty via a modern data lens,
highlights key research challenges and promise of data-driven
optimization that organically integrates machine learning and
mathematical programming for decision-making under
uncertainty, and identifies potential research
opportunities. A brief review of classical mathematical
programming techniques for hedging against uncertainty is
first presented, along with their wide spectrum of
applications in Process Systems Engineering. A comprehensive
review and classification of the relevant publications on
data-driven distributionally robust optimization, data-driven
chance constrained program, data-driven robust optimization,
and data-driven scenario-based optimization is then
presented. This paper also identifies fertile avenues for
future research that focuses on a closed-loop data-driven
optimization framework, which allows the feedback from
mathematical programming to machine learning, as well as
scenario-based optimization leveraging the power of deep
learning techniques. Perspectives on online learning-based
data-driven multistage optimization with a
learning-while-optimizing scheme are presented.}
}
@article{NisTanSugMiy2019item,
title = {Item listing optimization for e-commerce websites based on
diversity},
author = {Nishimura, Naoki and Tanahashi, Kotaro and Suganuma, Koji and
Miyama, Masamichi J. and Ohzeki, Masayuki},
journal = {Frontiers in Computer Science},
volume = 1,
pages = 2,
year = 2019,
publisher = {Frontiers},
keywords = {Quantum Annealing}
}
@article{Nitivatta96,
author = { Vilas Nitivattananon and Elaine C. Sadowski and Rafael G. Quimpo },
title = {Optimization of Water Supply System Operation},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
volume = 122,
number = 5,
pages = {374--384},
date = {1996-09/1996-10},
year = 1996,
month = sep # { / } # oct
}
@article{NogPinSub2017:ol,
author = {Bruno Nogueira and Rian G. S. Pinheiro and Anand Subramanian},
title = {A Hybrid Iterated Local Search Heuristic for the Maximum
Weight Independent Set Problem},
journal = {Optimization Letters},
year = 2018,
volume = 12,
number = 3,
pages = {567--583},
doi = {10.1007/s11590-017-1128-7}
}
@article{NosAltBank2015promoting,
doi = {10.1126/science.aab2374},
year = 2015,
month = jun,
publisher = {American Association for the Advancement of Science ({AAAS})},
volume = 348,
number = 6242,
pages = {1422--1425},
author = {B. A. Nosek and G. Alter and G. C. Banks and D. Borsboom and
S. D. Bowman and S. J. Breckler and S. Buck and
C. D. Chambers and G. Chin and G. Christensen and
M. Contestabile and A. Dafoe and E. Eich and J. Freese and
R. Glennerster and D. Goroff and D. P. Green and B. Hesse and
M. Humphreys and J. Ishiyama and D. Karlan and A. Kraut and
A. Lupia and P. Mabry and T. Madon and N. Malhotra and
E. Mayo-Wilson and M. McNutt and E. Miguel and E. L. Paluck
and U. Simonsohn and C. Soderberg and B. A. Spellman and
J. Turitto and G. VandenBos and S. Vazire and
E. J. Wagenmakers and R. Wilson and T. Yarkoni},
title = {Promoting an open research culture},
journal = {Science}
}
@article{NosEbeHav2018preregistration,
author = {Nosek, Brian A. and Ebersole, Charles R. and DeHaven,
Alexander C. and Mellor, David T.},
title = {The Preregistration Revolution},
volume = 115,
issn = {0027-8424, 1091-6490},
doi = {10.1073/pnas.1708274114},
language = {en},
number = 11,
journal = {Proceedings of the National Academy of Sciences},
month = mar,
year = 2018,
pages = {2600--2606},
abstract = {Progress in science relies in part on generating hypotheses
with existing observations and testing hypotheses with new
observations. This distinction between postdiction and
prediction is appreciated conceptually but is not respected
in practice. Mistaking generation of postdictions with
testing of predictions reduces the credibility of research
findings. However, ordinary biases in human reasoning, such
as hindsight bias, make it hard to avoid this mistake. An
effective solution is to define the research questions and
analysis plan before observing the research outcomes--a
process called preregistration. Preregistration distinguishes
analyses and outcomes that result from predictions from those
that result from postdictions. A variety of practical
strategies are available to make the best possible use of
preregistration in circumstances that fall short of the ideal
application, such as when the data are preexisting. Services
are now available for preregistration across all disciplines,
facilitating a rapid increase in the practice. Widespread
adoption of preregistration will increase distinctiveness
between hypothesis generation and hypothesis testing and will
improve the credibility of research findings.}
}
@article{NouAnd1998,
title = {A Comparison of Simulated Annealing Cooling Strategies},
author = { Yaghout Nourani and Bjarne Andresen },
journal = {Journal of Physics A},
volume = 31,
number = 41,
pages = {8373--8385},
year = 1998,
publisher = {IOP Publishing}
}
@article{NowSmu1996:ms,
author = {Nowicki, Eugeniusz and Smutnicki, Czeslaw},
title = {A Fast Taboo Search Algorithm for the Job Shop Problem},
journal = {Management Science},
year = 1996,
volume = 42,
number = 6,
pages = {797--813}
}
@article{NowSmu1996ejor,
title = {A fast tabu search algorithm for the permutation flow-shop problem},
author = {Nowicki, Eugeniusz and Smutnicki, Czes{\l}aw},
journal = {European Journal of Operational Research},
volume = 91,
number = 1,
pages = {160--175},
year = 1996
}
@article{OSC2015estimating,
title = {Estimating the reproducibility of psychological science },
author = {{Open Science Collaboration}},
journal = {Science},
volume = 349,
number = 6251,
numpages = 7,
pages = {aac4716},
year = 2015,
doi = {10.1126/science.aac4716}
}
@article{OchVer2018,
title = {Mapping the global structure of {TSP} fitness landscapes},
author = { Gabriela Ochoa and Veerapen, Nadarajen },
journal = {Journal of Heuristics},
volume = 24,
number = 3,
pages = {265--294},
year = 2018,
publisher = {Springer}
}
@article{OddCesPolSmi2008,
author = { Angelo Oddi and Amadeo Cesta and Nicola Policella and Stephen F. Smith },
title = {Combining Variants of Iterative Flattening Search},
journal = {Engineering Applications of Artificial Intelligence},
year = 2008,
volume = 21,
number = 5,
pages = {683--690}
}
@article{OddCesPolSmi2010,
author = { Angelo Oddi and Amadeo Cesta and Nicola Policella and Stephen F. Smith },
title = {Iterative Flattening Search for Resource Constrained Scheduling},
journal = {Journal of Intelligent Manufacturing},
year = 2010,
volume = 21,
number = 1,
pages = {17--30}
}
@article{OgbuSmith1990,
author = { F. A. Ogbu and David K. Smith },
title = {The Application of the Simulated Annealing Algorithm to the Solution of the {n/m/C} Max Flowshop Problem},
journal = {Computers \& Operations Research},
volume = 17,
number = 3,
pages = {243--253},
year = 1990,
publisher = {Elsevier}
}
@article{OhlTho07tsptw_sa,
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},
volume = 605,
pages = {21--41},
year = 2015,
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},
journal = {Decision Sciences},
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.}
}
@article{Oltean05,
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}
}
@article{OneRya2001tec,
title = {Grammatical Evolution},
author = { O'Neill, Michael and Ryan, Conor},
journal = {IEEE Transactions on Evolutionary Computation},
volume = 5,
number = 4,
pages = {349--358},
year = 2001
}
@article{Ormsbee89,
author = { Lindell E. Ormsbee and Thomas M. Walski and Donald V. Chase and W. W. Sharp },
title = {Methodology for improving pump operation efficiency},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
year = 1989,
volume = 115,
number = 2,
pages = {148--164},
note = {}
}
@article{Ormsbee94,
author = { Lindell E. Ormsbee and Kevin E. Lansey },
title = {Optimal Control of Water Supply Pumping Systems},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
year = 1994,
volume = 120,
number = 2,
pages = {237--252}
}
@article{Ormsbee95,
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
}
@article{OroJac2002,
title = {Analysis of Static Simulated Annealing Algorithms},
author = { Jeffrey E. Orosz and Sheldon H. Jacobson },
journal = {Journal of Optimization Theory and Applications},
volume = 115,
number = 1,
pages = {165--182},
year = 2002,
publisher = {Springer}
}
@article{OsmPot89,
author = { Ibrahim H. Osman and Chris N. Potts },
title = {Simulated Annealing for Permutation Flow-Shop
Scheduling},
journal = {Omega},
year = 1989,
volume = 17,
number = 6,
pages = {551--557}
}
@article{OwMor1988:ijpr,
author = {Ow, P. S. and Morton, T. E.},
title = {Filtered Beam Search in Scheduling},
journal = {International Journal of Production Research},
year = 1988,
volume = 26,
pages = {297--307},
annote = {Proposed beam search}
}
@article{OzeKar2007:jors,
author = { G{\"u}l {\"O}zerol and Esra Karasakal },
title = {Interactive outranking approaches for multicriteria
decision-making problems with imprecise information},
journal = {Journal of the Operational Research Society},
year = 2007,
volume = 59,
pages = {1253--1268}
}
@article{PadRin1991siamr,
title = {A branch-and-cut algorithm for the resolution of large-scale symmetric traveling salesman problems},
author = {Padberg, Manfred and Rinaldi, Giovanni},
journal = {SIAM Review},
volume = 33,
number = 1,
pages = {60--100},
year = 1991
}
@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}
}
@article{PalGooSor2014,
author = {Daniel {Palhazi Cuervo} and Peter Goos and Kenneth S{\"o}rensen and Emely Arr{\'{a}}iz},
title = {An Iterated Local Search Algorithm for the Vehicle Routing Problem
with Backhauls},
journal = {European Journal of Operational Research},
year = 2014,
volume = 237,
number = 2,
pages = {454--464}
}
@article{Palubeckis2006,
title = {Iterated tabu search for the unconstrained binary quadratic
optimization problem},
author = {Palubeckis, Gintaras},
journal = {Informatica},
volume = 17,
number = 2,
pages = {279--296},
year = 2006,
publisher = {Institute of Mathematics and Informatics},
doi = {10.15388/Informatica.2006.138}
}
@article{PanRui2012ejor,
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 },
journal = {Computers \& Operations Research},
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 },
title = {Iterated Search Methods for Earliness and Tardiness Minimization in Hybrid Flowshops with Due Windows},
journal = {Computers \& Operations Research},
year = 2017,
volume = 80,
pages = {50--60}
}
@article{PanTasLia2008,
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}
}
@article{PanWanZha2008,
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}
}
@article{PanYang2009,
title = {A survey on transfer learning},
author = {Pan, Sinno Jialin and Yang, Qiang},
journal = {IEEE Transactions on Knowledge and Data Engineering},
volume = 22,
number = 10,
pages = {1345--1359},
year = 2009
}
@article{PaqSchStu07:aor,
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.}
}
@article{PaqStu06:mqap,
author = { Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {A study of stochastic local search algorithms for
the biobjective {QAP} with correlated flow matrices},
journal = {European Journal of Operational Research},
year = 2006,
volume = 169,
number = 3,
pages = {943--959}
}
@article{PaqStu09:cor,
author = { Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {Design and analysis of stochastic local search for
the multiobjective traveling salesman problem},
journal = {Computers \& Operations Research},
year = 2009,
volume = 36,
number = 9,
pages = {2619--2631},
doi = {10.1016/j.cor.2008.11.013}
}
@article{ParDoeHar2009,
author = {Parragh, S. N. and Karl F. Doerner and Richard F. Hartl and Xavier Gandibleux },
title = {A heuristic two-phase solution approach for the
multi-objective dial-a-ride problem},
journal = {Networks},
volume = 54,
number = 4,
pages = {227--242},
year = 2009
}
@article{ParJoh1997,
author = {Rebecca Parsons and Mark Johnson},
title = {A Case Study in Experimental Design Applied to Genetic Algorithms with Applications to {DNA} Sequence Assembly},
journal = {American Journal of Mathematical and Management Sciences},
publisher = {Taylor \& Francis},
volume = 17,
number = {3-4},
pages = {369--396},
year = 1997,
doi = {10.1080/01966324.1997.10737444}
}
@article{ParKim98,
author = {Moon-Won Park and Yeong-Dae Kim},
title = {A systematic procedure for setting parameters in simulated annealing algorithms},
journal = {Computers \& Operations Research},
volume = 25,
number = 3,
pages = {207--217},
year = 1998,
doi = {10.1016/S0305-0548(97)00054-3}
}
@article{ParLopFre2002:ieee-tec,
author = {R. S. Parpinelli and H. S. Lopes and A. A. Freitas},
title = {Data Mining with an Ant Colony Optimization Algorithm},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2002,
volume = 6,
number = 4,
pages = {321--332}
}
@article{ParQuo1995antlr,
author = {Parr, Terence J. and Quong, Russell W.},
title = {{ANTLR}: A predicated-{LL} (k) parser generator},
journal = {Software --- Practice \& Experience},
year = 1995,
volume = 25,
number = 7,
pages = {789--810}
}
@article{ParVas2007:ejor,
author = { R. O. Parreiras and J. A. Vascocelos },
title = {A multiplicative version of {PROMETHEE II} applied to
multiobjective optimization problems},
journal = {European Journal of Operational Research},
year = 2007,
volume = 183,
pages = {729--740}
}
@article{Paul2010,
title = {Comparative performance of tabu search and simulated annealing heuristics for the quadratic assignment problem},
author = { Paul, Gerald },
journal = {Operations Research Letters},
volume = 38,
number = 6,
pages = {577--581},
year = 2010,
publisher = {Elsevier}
}
@article{Pearl2019:cacm,
title = {The seven tools of causal inference, with reflections on machine learning},
author = { Judea Pearl },
journal = {Communications of the ACM},
volume = 62,
number = 3,
pages = {54--60},
year = 2019
}
@article{PedNesCan2011:asc,
author = {Mart{\'i}n Pedemonte and Sergio Nesmachnow and H{\'e}ctor Cancela},
title = {A survey on parallel ant colony optimization},
journal = {Applied Soft Computing},
volume = 11,
number = 8,
pages = {5181--5197},
year = 2011
}
@article{PelBirStu2012:si,
author = { Paola Pellegrini and Mauro Birattari and Thomas St{\"u}tzle },
title = {A Critical Analysis of Parameter Adaptation in Ant Colony Optimization},
journal = {Swarm Intelligence},
year = 2012,
volume = 6,
number = 1,
pages = {23--48},
doi = {10.1007/s11721-011-0061-0}
}
@article{PelCasPes2012its,
author = { Paola Pellegrini and L. Castelli and R. Pesenti},
title = {Metaheuristic algorithms for the simultaneous slot
allocation problem},
journal = {IET Intelligent Transport Systems},
year = 2012,
volume = 6,
number = 4,
pages = {453--462},
month = dec,
doi = {10.1049/iet-its.2011.0179}
}
@article{PelMasBirStu14,
author = { Paola Pellegrini and Franco Mascia and Thomas St{\"u}tzle and Mauro Birattari },
title = {On the Sensitivity of Reactive Tabu Search to its
Meta-parameters},
journal = {Soft Computing},
year = 2014,
volume = 18,
number = 11,
pages = {2177--2190},
doi = {10.1007/s00500-013-1192-6}
}
@article{PenSubOch2013:joh,
author = {Puca Huachi {Vaz Penna} and Anand Subramanian and Luiz Satoru Ochi },
title = {An Iterated Local Search Heuristic for the Heterogeneous Fleet Vehicle Routing Problem},
journal = {Journal of Heuristics},
year = 2013,
volume = 19,
number = 2,
pages = {201--232}
}
@article{Per2020challenge,
title = {Challenge to scientists: does your ten-year-old code still
run?},
author = {Jeffrey M. Perkel},
journal = {Nature},
volume = 584,
pages = {556--658},
year = 2020,
doi = {10.1038/d41586-020-02462-7},
keywords = {reproducibility; software engineering; ReScience C; Ten Years
Reproducibility Challenge; code reusability}
}
@article{PerLopStu2015si,
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 limited budget of evaluations},
journal = {Swarm Intelligence},
year = 2015,
doi = {10.1007/s11721-015-0106-x},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2015-004},
volume = 9,
number = {2-3},
pages = {103--124}
}
@article{PerRuiNes2018,
author = {P{\'{e}}res, Matias and Ruiz, Germ{\'{a}}n and Nesmachnow,
Sergio and Olivera, Ana C.},
journal = {Applied Soft Computing},
keywords = {Multiobjective evolutionary
algorithms,Pollution,Simulation,Traffic flow},
title = {Multiobjective evolutionary optimization of traffic flow and
pollution in {Montevideo}, {Uruguay}},
year = 2018,
volume = 70,
pages = {472--485},
publisher = {Elsevier}
}
@article{PesUchAraRod2010,
author = {A. Pessoa and E. Uchoa and M. Arag{\~a}o and R. Rodrigues},
title = {Exact Algorithm over an Arc-time-indexed formulation for Parallel Machine Scheduling Problems},
journal = {Mathematical Programming Computation},
year = 2010,
volume = 2,
number = {3--4},
pages = {259--290}
}
@article{Pesant98tsptw,
author = { Gilles Pesant and Michel Gendreau and Jean-Yves Potvin and J.-M. Rousseau },
title = {An Exact Constraint Logic Programming Algorithm
for the Traveling Salesman Problem with Time Windows},
journal = {Transportation Science},
year = 1998,
volume = 32,
pages = {12--29}
}
@article{Pet1998boeing,
author = {Charles W. Petit},
title = {Touched by nature: putting evolution to work on the assembly
line},
journal = {U.S. News \& World Report},
volume = 125,
number = 4,
month = jul,
year = 1998,
pages = {43--45},
url = {http://dynamics.org/~altenber/IN_THE_MEDIA/US_NEWS.7-27-98/index.html},
annote = {Evolutionary optimization of turbine design of the
Boeing~777~GE}
}
@article{PetBonGre2014gtoc5,
author = {Petropoulos, Anastassios E. and Bonfiglio, Eugene P. and
Grebow, Daniel J. and Lam, Try and Parker, Jeffrey S. and
Arrieta, Juan and Landau, Damon F. and Anderson, Rodney
L. and Gustafson, Eric D. and Whiffen, Gregory J. and
Finlayson, Paul A. and Sims, Jon A.},
title = {{GTOC5}: Results from Jet Propulsion Lab},
journal = {Acta Futura},
year = 2014,
volume = 8,
pages = {21--27},
doi = {10.2420/AF08.2014.21},
abstract = {We present the methods and results of the Jet Propulsion
Laboratory team in the 5th Global Trajectory Optimization
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.}
}
@article{PetHarHarLan2018gi,
author = {Petke, Justyna and Haraldsson, Saemundur O. and Harman, Mark and Langdon, William B. and White, David R. and John R. Woodward},
journal = {IEEE Transactions on Evolutionary Computation},
title = {Genetic Improvement of Software: A Comprehensive Survey},
year = 2018,
volume = 22,
number = 3,
pages = {415--432},
doi = {10.1109/TEVC.2017.2693219}
}
@article{PetZil2006parallel,
author = {Petrik, Marek and Shlomo Zilberstein },
journal = {Annals of Mathematics and Artificial Intelligence},
number = 1,
pages = {85--106},
title = {Learning parallel portfolios of algorithms},
volume = 48,
year = 2006,
keywords = {algorithm selection}
}
@article{Pezeshk96,
author = { S. Pezeshk and O. J. Helweg },
title = {Adaptative Search Optimisation in reducing pump operation
costs},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
volume = 122,
number = 1,
pages = {57--63},
date = {1996-01/1996-02},
year = 1996,
month = jan # { / } # feb
}
@article{PheKok2003interactive,
title = {An interactive evolutionary metaheuristic for multiobjective
combinatorial optimization},
author = {Phelps, Selcen and Murat K{\"o}ksalan },
journal = {Management Science},
volume = 49,
number = 12,
pages = {1726--1738},
year = 2003
}
@article{PieKhuSav2007investigation,
title = {An investigation on preference order ranking scheme for
multiobjective evolutionary optimization},
author = {di Pierro, Francesco and Khu, Soon-Thiam and Dragan A. Savic },
journal = {IEEE Transactions on Evolutionary Computation},
volume = 11,
number = 1,
pages = {17--45},
year = 2007
}
@article{PinVinSin2020improving,
title = {Improving Reproducibility in Machine Learning Research (A
Report from the {NeurIPS} 2019 Reproducibility Program)},
author = {Joelle Pineau and Philippe Vincent-Lamarre and Koustuv Sinha
and Vincent Larivière and Alina Beygelzimer and Florence
d'Alché-Buc and Emily Fox and Hugo Larochelle},
year = 2020,
journal = {Arxiv preprint arXiv:2003.12206 [cs.LG]},
url = {https://arxiv.org/abs/2003.12206}
}
@article{Pis2005where,
author = { David Pisinger },
journal = {Computers \& Operations Research},
number = 9,
pages = {2271--2284},
title = {Where are the hard knapsack problems?},
volume = 32,
year = 2005
}
@article{PisRop2007:cor,
author = { David Pisinger and Stefan Ropke },
title = {A General Heuristic for Vehicle Routing Problems},
journal = {Computers \& Operations Research},
year = 2007,
volume = 34,
number = 8,
pages = {2403--2435}
}
@article{PitAlmDoe06,
author = {Rapeepan Pitakaso and Christian Almeder and Karl F. Doerner and Richard F. Hartl },
title = {Combining exact and population-based methods for the
Constrained Multilevel Lot Sizing Problem},
journal = {International Journal of Production Research},
year = 2006,
volume = 44,
number = 22,
pages = {4755--4771}
}
@article{PitAlmDoe07,
author = {Rapeepan Pitakaso and Christian Almeder and Karl F. Doerner and Richard F. Hartl },
title = {A {\MaxMinAntSystem} for unconstrained multi-level
lot-sizing problems},
volume = 34,
number = 9,
journal = {Computers \& Operations Research},
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.}
}
@article{Ples2018repro,
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}
}
@article{PorGonAllHsu2017:ejor,
author = {Daniel Porumbel and Gilles Goncalves and Hamid Allaoui and Tient{\'e} Hsu},
title = {Iterated Local Search and Column Generation to solve Arc-Routing as a Permutation Set-Covering Problem},
journal = {European Journal of Operational Research},
year = 2017,
volume = 256,
number = 2,
pages = {349--367}
}
@article{PorParDoa++2013,
author = {Juan Porta and Jorge Parapar and Ram{\'o}n Doallo and Vasco Barbosa and In{\'e}s Sant{\'e} and Rafael Crecente and Carlos D{\'i}az},
title = {A Population-based Iterated Greedy Algorithm for the Delimitation and Zoning of Rural Settlements},
journal = {Computers, Environment and Urban Systems},
year = 2013,
volume = 39,
pages = {12--26}
}
@article{PotBen96vrptw_gene,
author = { Jean-Yves Potvin and S. Bengio },
title = {The Vehicle Routing Problem with Time Windows Part
{II}: Genetic Search},
journal = {INFORMS Journal on Computing},
year = 1996,
volume = 8,
pages = {165--172}
}
@article{Pra2009jwrpm,
author = { T. Devi Prasad },
title = {Design of pumped water distribution networks with
storage},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
year = 2009,
volume = 136,
number = 4,
pages = {129--136}
}
@article{PraPac2015:joh,
author = {Marco Pranzo and D. Pacciarelli},
title = {An Iterated Greedy Metaheuristic for the Blocking Job Shop Scheduling Problem},
journal = {Journal of Heuristics},
year = 2016,
volume = 22,
number = 4,
pages = {587--611},
doi = {10.1007/s10732-014-9279-5}
}
@article{PraAveLem2019learning,
author = {Prates, Marcelo and Avelar, Pedro H. C. and Lemos, Henrique
and Lamb, Luis C. and Vardi, Moshe Y.},
title = {Learning to Solve {NP-Complete} Problems: A Graph Neural
Network for Decision {TSP}},
journal = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
year = 2019,
volume = 33,
number = 01,
pages = {4731--4738},
month = jul,
issn = {2159-5399},
doi = {10.1609/aaai.v33i01.33014731},
publisher = {Association for the Advancement of Artificial Intelligence
(AAAI)}
}
@article{PriKumSug2023trial,
author = {Kenneth V. Price and Abhishek Kumar and Ponnuthurai N. Suganthan },
title = {Trial-based dominance for comparing both the speed and
accuracy of stochastic optimizers with standard
non-parametric tests},
journal = {Swarm and Evolutionary Computation},
year = 2023,
volume = 78,
pages = 101287,
doi = {10.1016/j.swevo.2023.101287},
keywords = {Benchmarking, Two-variable non-parametric tests, Evolutionary
algorithms, Dominance, Stochastic optimization, Numerical
optimization, Mann-Whitney test}
}
@article{Prim1957,
title = {Shortest connection networks and some generalizations},
author = {Prim, Robert Clay},
journal = {{Bell System Technical Journal}},
volume = 36,
number = 6,
pages = {1389--1401},
year = 1957
}
@article{ProBisBou2018tuna,
title = {Tunability: Importance of Hyperparameters of Machine Learning
Algorithms},
author = {Probst, Philipp and Bernd Bischl and Boulesteix,
Anne-Laure},
journal = {Arxiv preprint arXiv:1802.09596},
year = 2018,
url = {https://arxiv.org/abs/1802.09596},
keywords = {parameter importance}
}
@article{ProBisBou2019tuna,
author = {Probst, Philipp and Bernd Bischl and Boulesteix,
Anne-Laure},
title = {Tunability: Importance of Hyperparameters of Machine Learning
Algorithms},
journal = {Journal of Machine Learning Research},
year = 2019,
volume = 20,
number = 53,
pages = {1--32}
}
@article{ProMue2012,
title = {Design of computer experiments: space filling and beyond},
author = {Pronzato, Luc and M\"uller, Werner G.},
journal = {Statistics and Computing},
year = 2012,
number = 3,
pages = {681--701},
volume = 22,
keywords = {Kriging; Entropy; Design of experiments; Space-filling;
Sphere packing; Maximin design; Minimax design}
}
@article{Psaraftis1995:aor,
author = {Harilaos N. Psaraftis},
title = {Dynamic Vehicle Routing: Status and Prospects},
journal = {Annals of Operations Research},
year = 1995,
volume = 61,
pages = {143--164}
}
@article{PukHei2006,
title = {Optimizing heuristic search in forest planning},
author = {Pukkala, Timo and Heinonen, Tero},
journal = {Nonlinear Analysis: Real World Applications},
volume = 7,
number = 5,
year = 2006,
pages = {1284--1297},
publisher = {Elsevier}
}
@article{PulTac2009aqme,
title = {A self-adaptive multi-engine solver for quantified {Boolean}
formulas},
author = {Pulina, Luca and Tacchella, Armando},
journal = {Constraints},
volume = 14,
number = 1,
pages = {80--116},
year = 2009
}
@article{PurFle2007tec,
title = {On the Evolutionary Optimization of Many Conflicting
Objectives},
author = { Robin C. Purshouse and Peter J. Fleming },
journal = {IEEE Transactions on Evolutionary Computation},
year = 2007,
doi = {10.1109/TEVC.2007.910138},
volume = 11,
number = 6,
pages = {770--784}
}
@article{QiMaLiu2014moead,
title = {{MOEA/D} with adaptive weight adjustment},
author = {Qi, Yutao and Ma, Xiaoliang and Liu, Fang and Jiao, Licheng
and Sun, Jianyong and Wu, Jianshe},
journal = {Evolutionary Computation},
year = 2014,
number = 2,
pages = {231--264},
volume = 22,
publisher = {MIT Press},
annote = {Uses an external population},
doi = {10.1162/EVCO_a_00109}
}
@article{QuiReeKel2017direct,
title = {Direct policy search for robust multi-objective management of
deeply uncertain socio-ecological tipping points},
author = {Quinn, Julianne D. and Patrick M. Reed and Keller, Klaus},
journal = {Environmental Modelling \& Software},
volume = 92,
pages = {125--141},
year = 2017,
publisher = {Elsevier}
}
@article{RadRuiBor2009,
title = {New High Performing Heuristics for Minimizing Makespan in Permutation Flowshops},
author = { Shahriar Farahmand Rad and Rub{\'e}n Ruiz and Naser Boroojerdian },
journal = {Omega},
year = 2009,
number = 2,
pages = {331--345},
volume = 37
}
@article{Raj93,
title = {Heuristic algorithm for scheduling in a flowshop to
minimize total flowtime},
author = {C. Rajendran},
journal = {International Journal of Production Economics},
volume = 29,
number = 1,
pages = {65--73},
year = 1993
}
@article{RajZie04,
author = {C. Rajendran and H. Ziegler },
title = {Ant-colony algorithms for permutation flowshop
scheduling to minimize makespan/total flowtime of
jobs},
journal = {European Journal of Operational Research},
volume = 155,
number = 2,
pages = {426--438},
year = 2004
}
@article{RajZie1997,
author = {C. Rajendran and H. Ziegler },
title = {An efficient heuristic for scheduling in a flowshop to
minimize total weighted flowtime of jobs},
journal = {European Journal of Operational Research},
volume = 103,
number = 1,
pages = {129--138},
year = 1997,
issn = {0377 -- 2217},
doi = {10.1016/S0377-2217(96)00273-1}
}
@article{RamBir2020appsci,
title = {Automatic Design of Collective Behaviors for Robots that Can Display and Perceive Colors},
author = { David Garz{\'o}n Ramos and Mauro Birattari },
journal = {Applied Sciences},
volume = 10,
number = 13,
pages = {4654},
year = 2020,
publisher = {Multidisciplinary Digital Publishing Institute}
}
@article{RamMirSer2021foodplan,
author = {Ramos-Pérez, Juan-Manuel and Miranda, Gara and Segredo,
Eduardo and León, Coromoto and RodrÃguez-León, Casiano},
title = {Application of Multi-Objective Evolutionary Algorithms for
Planning Healthy and Balanced School Lunches},
journal = {Mathematics},
month = dec,
publisher = {{MDPI} {AG}},
volume = 9,
year = 2021,
number = 1,
pages = 80,
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.}
}
@article{RamMonMor2011jors,
title = {Extending the use of scenario planning and {MCDA} for the
evaluation of strategic options},
author = {Ram, Camelia and Montibeller, Gilberto and Morton, Alec},
journal = {Journal of the Operational Research Society},
volume = 62,
number = 5,
pages = {817--829},
year = 2011
}
@article{RaoSal2007hydroinf,
author = { Zhengfu Rao and Elad Salomons},
title = {Development of a real-time, near-optimal control
process for water-distribution networks},
journal = { Journal of Hydroinformatics },
year = 2007,
volume = 9,
number = 1,
doi = {10.2166/hydro.2006.015},
pages = {25--37}
}
@article{RarUzs2001:joh,
author = {Ronald L. Rardin and Reha Uzsoy},
title = {Experimental Evaluation of Heuristic Optimization Algorithms: A Tutorial},
journal = {Journal of Heuristics},
year = 2001,
volume = 7,
number = 3,
pages = {261--304}
}
@article{RasMusKar2019vrp,
author = {Jussi Rasku and Musliu, Nysret and Tommi K{\"a}rkk{\"a}inen},
title = {On automatic algorithm configuration of vehicle routing
problem solvers},
journal = {Journal on Vehicle Routing Algorithms},
year = 2019,
month = feb,
volume = 2,
number = {1-4},
pages = {1--22},
doi = {10.1007/s41604-019-00010-9},
keywords = {irace, SMAC, GGA, REVAC, VRP}
}
@article{Rech2000case,
title = {Case studies in evolutionary experimentation and computation},
author = { Rechenberg, Ingo },
journal = {Computer Methods in Applied Mechanics and Engineering},
volume = 186,
number = {2-4},
pages = {125--140},
year = 2000,
doi = {10.1016/S0045-7825(99)00381-3},
publisher = {Elsevier}
}
@article{ReeEre2004jors,
author = { Colin R. Reeves and Eremeev, A. V.},
title = {Statistical analysis of local search landscapes},
journal = {Journal of the Operational Research Society},
pages = {687--693},
volume = 55,
number = 7,
year = 2004,
epub = {http://www.jstor.org/stable/4102015}
}
@article{ReeGon1989imcdm,
title = {A comparison of two interactive {MCDM} procedures},
author = {Reeves, Gary R. and Gonzalez, Juan J.},
journal = {European Journal of Operational Research},
volume = 41,
number = 2,
pages = {203--209},
year = 1989,
doi = {10.1016/0377-2217(89)90385-8},
publisher = {Elsevier},
keywords = {artificial DM, interactive}
}
@article{ReeHadHerKas2013water,
title = {Evolutionary multiobjective optimization in water resources:
The past, present, and future},
author = { Patrick M. Reed and David Hadka and Herman, Jonathan D. and Kasprzyk, Joseph R. and Kollat, Joshua B. },
journal = {Advances in Water Resources},
volume = 51,
pages = {438--456},
year = 2013
}
@article{CheLiYao2019,
title = {Standing on the shoulders of giants: Seeding search-based
multi-objective optimization with prior knowledge for
software service composition},
author = {Chen, Tao and Li, Miqing and Xin Yao },
journal = {Information and Software Technology},
year = 2019,
pages = {155--175},
volume = 114,
publisher = {Elsevier},
annote = {Example of deteroriation in archiving}
}
@article{RehZaeFisRud2022bench,
author = {Rehbach, Frederik and Martin Zaefferer and Andreas Fischbach and G{\"u}nther Rudolph and Thomas Bartz-Beielstein },
title = {Benchmark-Driven Configuration of a Parallel Model-Based
Optimization Algorithm},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2022,
volume = 26,
number = 6,
pages = {1365--1379},
doi = {10.1109/TEVC.2022.3163843}
}
@article{Rei91a,
author = { Gerhard Reinelt },
title = {{TSPLIB} --- A Traveling Salesman Problem Library},
journal = {ORSA Journal on Computing},
year = 1991,
volume = 3,
number = 4,
pages = {376--384}
}
@article{ReiDoeHar04:cor,
author = { Marc Reimann and Karl F. Doerner and Richard F. Hartl },
title = {{D}-ants: {Savings} based ants divide and conquer
the vehicle routing problems},
journal = {Computers \& Operations Research},
year = 2004,
volume = 31,
number = 4,
pages = {563--591}
}
@article{ReiLau06,
author = { Marc Reimann and Marco Laumanns },
title = {Savings based ant colony optimization for the
capacitated minimum spanning tree problem},
volume = 33,
number = 6,
journal = {Computers \& Operations Research},
year = 2006,
keywords = {Ant colony Optimization, Capacitated minimum
spanning tree problem},
pages = {1794--1822},
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.}
}
@article{RenFenKeZha2010:cie,
author = {Zhi-Gang Ren and Zu-Ren Feng and Liang-Jun Ke and Zhao-Jun Zhang},
title = {New Ideas for Applying Ant Colony Optimization to the Set Covering Problem},
journal = {Computers and Industrial Engineering},
year = 2010,
volume = 58,
number = 4,
pages = {774--784}
}
@article{ReyCoe06,
author = {M. Reyes-Sierra and Carlos A. {Coello Coello} },
title = {Multi-objective particle swarm optimizers: A survey of the
state-of-the-art},
year = 2006,
journal = {International Journal of Computational Intelligence Research},
volume = 2,
number = 3,
pages = {287--308}
}
@article{Reynolds1987,
author = {Craig W. Reynolds},
title = {Flocks, Herds, and Schools: A Distributed Behavioral Model},
journal = {{ACM} Computer Graphics},
volume = 21,
number = 4,
pages = {25--34},
year = 1987
}
@article{RezAraMeh2022anchoring,
title = {Analyzing anchoring bias in attribute weight elicitation of
{SMART}, {Swing}, and best-worst method},
doi = {10.1111/itor.13171},
journal = {International Transactions in Operational Research},
author = {Rezaei, Jafar and Arab, Alireza and Mehregan, Mohammadreza},
year = 2022,
keywords = {anchoring bias, best-worst method, cognitive bias, MADM,
multi-attribute weighting, SMART, Swing}
}
@article{RezHej2005,
title = {Flowshop-scheduling Problems with Makespan Criterion: A Review},
author = {S. Reza Hejazi and S. Saghafian},
journal = {International Journal of Production Research},
year = 2005,
number = 14,
pages = {2895--2929},
volume = 43
}
@article{RibComTor2011:omega,
title = {An iterated greedy algorithm for the flowshop
scheduling problem with blocking},
journal = {Omega},
volume = 39,
number = 3,
pages = {293 -- 301},
year = 2011,
author = {Imma Ribas and Ramon Companys and Xavier
Tort-Martorell}
}
@article{RibComTor2013,
author = {Imma Ribas and Ramon Companys and Xavier Tort-Martorell},
title = {An Efficient Iterated Local Search Algorithm for the Total Tardiness Blocking Flow Shop Problem},
journal = {International Journal of Production Research},
year = 2013,
volume = 51,
number = 17,
pages = {5238--5252}
}
@article{RibUrr2007,
author = { Celso C. Ribeiro and Sebasti{\'a}n Urrutia},
title = {Heuristics for the Mirrored Traveling Tournament Problem},
journal = {European Journal of Operational Research},
year = 2007,
volume = 179,
number = 3,
pages = {775--787}
}
@article{RicBea2004:joh,
author = {A. J. Richmond and John E. Beasley },
title = {An Iterative Construction Heuristic for the Ore Selection Problem},
journal = {Journal of Heuristics},
year = 2004,
volume = 10,
number = 2,
pages = {153--167}
}
@article{Rice1976,
author = {John R. Rice},
title = {The Algorithm Selection Problem},
journal = {Advances in Computers},
year = 1976,
volume = 15,
pages = {65--118},
doi = {10.1016/S0065-2458(08)60520-3},
abstract = {The problem of selecting an effective algorithm arises in a
wide variety of situations. This chapter starts with a
discussion on abstract models: the basic model and associated
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.}
}
@article{RivAfrPri2015:coa,
author = {Juan Carlos Rivera and H. Murat Afsar and Christian Prins },
title = {A Multistart Iterated Local Search for the Multitrip Cumulative Capacitated
Vehicle Routing Problem},
journal = {Computational Optimization and Applications},
year = 2015,
volume = 61,
number = 1,
pages = {159--187}
}
@article{RivYanLop2021tweet,
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}
}
@article{Rob1995statcomp,
author = {Robert, C. P.},
title = {Simulation of truncated normal variables},
journal = {Statistics and Computing},
year = 1995,
volume = 5,
number = 2,
pages = {121--125},
month = jun
}
@article{RomKraArn2012protein,
author = {P. A. Romero and A. Krause and F. H. Arnold},
title = {Navigating the Protein Fitness Landscape with {Gaussian}
Processes},
doi = {10.1073/pnas.1215251110},
year = 2012,
month = dec,
volume = 110,
number = 3,
pages = {E193--E201},
journal = {Proceedings of the National Academy of Sciences},
keywords = {Combinatorial Black-box Expensive}
}
@article{RomSan1991,
title = {A Theoretical Framework for Simulated Annealing},
author = { Fabio Romeo and Alberto Sangiovanni-Vincentelli },
journal = {Algorithmica},
volume = 6,
number = {1-6},
pages = {302--345},
year = 1991,
publisher = {Springer}
}
@article{Roos2001science,
title = {Bioinformatics--trying to swim in a sea of data},
author = {Roos, David S.},
journal = {Science},
volume = 291,
number = 5507,
pages = {1260--1261},
year = 2001,
publisher = {American Association for the Advancement of Science}
}
@article{RopPis2006:ejor,
author = { Stefan Ropke and David Pisinger },
title = {A Unified Heuristic for a Large Class of Vehicle Routing Problems
with Backhauls},
journal = {European Journal of Operational Research},
year = 2006,
volume = 171,
number = 3,
pages = {750--775}
}
@article{RopPis2006:ts,
author = { Stefan Ropke and David Pisinger },
title = {An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problme with Time Windows},
journal = {Transportation Science},
year = 2006,
volume = 40,
number = 4,
pages = {455--472}
}
@article{Ros2014mutual,
title = {Mutual Information between Discrete and Continuous Data Sets},
author = {Ross, Brian C.},
doi = {10.1371/journal.pone.0087357},
journal = {PLoS One},
publisher = {Public Library of Science},
year = 2014,
month = feb,
volume = 9,
pages = {1--5},
number = 2,
abstract = {Mutual information (MI) is a powerful method for detecting
relationships between data sets. There are accurate methods
for estimating MI that avoid problems with ``binning'' when
both data sets are discrete or when both data sets are
continuous. We present an accurate, non-binning MI estimator
for the case of one discrete data set and one continuous data
set. This case applies when measuring, for example, the
relationship between base sequence and gene expression level,
or the effect of a cancer drug on patient survival time. We
also show how our method can be adapted to calculate the
Jensen-Shannon divergence of two or more data sets.}
}
@article{RosKleWol1990,
title = {Temperature measurement and equilibrium dynamics of simulated annealing placements},
author = {Rose, Jonathan and Klebsch, Wolfgang and Wolf, J{\"u}rgen},
journal = {IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems},
volume = 9,
number = 3,
pages = {253--259},
year = 1990
}
@article{Rothberg2007,
title = {An evolutionary algorithm for polishing mixed integer programming solutions},
author = {Rothberg, Edward},
journal = {INFORMS Journal on Computing},
volume = 19,
number = 4,
pages = {534--541},
year = 2007,
publisher = {{INFORMS}}
}
@article{Rothman1985,
title = {Nonlinear inversion, statistical mechanics, and residual statics estimation},
author = {Rothman, Daniel H.},
journal = {Geophysics},
volume = 50,
number = 12,
pages = {2784--2796},
year = 1985,
publisher = {Society of Exploration Geophysicists}
}
@article{Rothman1986,
title = {Automatic estimation of large residual statics corrections},
author = {Rothman, Daniel H.},
journal = {Geophysics},
volume = 51,
number = 2,
pages = {332--346},
year = 1986,
publisher = {Society of Exploration Geophysicists}
}
@article{Roy2010:ejor,
author = { Bernard Roy },
title = {Robustness in operational research and decision
aiding: A multi-faceted issue},
journal = {European Journal of Operational Research},
volume = 200,
number = 3,
pages = {629--638},
year = 2010,
doi = {10.1016/j.ejor.2008.12.036}
}
@article{RudCapRou2023improved_pnb,
author = { Isaac Rudich and Quentin Cappart and Louis-Martin Rousseau },
title = {Improved {Peel}-and-{Bound}: Methods for Generating Dual Bounds
with Multivalued Decision Diagrams},
journal = {Journal of Artificial Intelligence Research},
year = 2023,
volume = 77,
pages = {1489--1538},
month = aug,
doi = {10.1613/jair.1.14607},
publisher = {{AI} Access Foundation}
}
@article{RudSchGri2016coa,
author = { G{\"u}nther Rudolph and Oliver Sch{\"u}tze and Christian Grimme and Christian Dom{\'i}nguez-Medina and Heike Trautmann },
title = {Optimal averaged {Hausdorff} archives for bi-objective
problems: theoretical and numerical results},
journal = {Computational Optimization and Applications},
volume = 64,
number = 2,
year = 2016,
pages = {589--618}
}
@article{Rudolph1994:tnn,
title = {Convergence analysis of canonical genetic algorithms},
author = { G{\"u}nther Rudolph },
journal = {IEEE Transactions on Neural Networks},
volume = 5,
number = 1,
pages = {96--101},
year = 1994
}
@article{RuiMar05,
author = { Rub{\'e}n Ruiz and C. Maroto },
title = {A Comprehensive Review and Evaluation of Permutation
Flow\-shop Heuristics},
journal = {European Journal of Operational Research},
year = 2005,
volume = 165,
number = 2,
pages = {479--494}
}
@article{RuiMar06,
author = { Rub{\'e}n Ruiz and C. Maroto and Javier Alcaraz},
title = {Two new robust genetic algorithms for the flowshop scheduling problem},
journal = {Omega},
publisher = {Elsevier},
volume = 34,
number = 5,
pages = {461--476},
year = 2006,
doi = {10.1016/j.omega.2004.12.006}
}
@article{RuiSabLuq2015wasfga,
author = { Ruiz, Ana Bel{\'e}n and Rub{\'{e}}n Saborido and Mariano Luque },
title = {A preference-based evolutionary algorithm for multiobjective
optimization: the weighting achievement scalarizing function
genetic algorithm},
journal = {Journal of Global Optimization},
year = 2015,
volume = 62,
number = 1,
pages = {101--129},
month = may,
annote = {Proposed WASF-GA},
doi = {10.1007/s10898-014-0214-y},
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.}
}
@article{RuiStu04:ejor,
author = { Rub{\'e}n Ruiz and Thomas St{\"u}tzle },
title = {A Simple and Effective Iterated Greedy Algorithm for
the Permutation Flowshop Scheduling Problem},
journal = {European Journal of Operational Research},
year = 2007,
volume = 177,
number = 3,
pages = {2033--2049}
}
@article{RuiStu2008:ejor,
author = { Rub{\'e}n Ruiz and Thomas St{\"u}tzle },
title = {An {Iterated} {Greedy} heuristic for the sequence
dependent setup times flowshop problem with makespan
and weighted tardiness objectives },
journal = {European Journal of Operational Research},
volume = 187,
number = 3,
pages = {1143 -- 1159},
year = 2008
}
@article{Russell95,
author = {Robert A. Russell},
title = {Hybrid Heuristics for the Vehicle Routing Problem with Time Windows},
journal = {Transportation Science},
year = 1995,
volume = 29,
number = 2,
pages = {156--166}
}
@article{SabAyoKen2013tec,
author = {N. R. {Sabar} and M. {Ayob} and Graham Kendall and R. {Qu}},
journal = {IEEE Transactions on Evolutionary Computation},
title = {Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems},
year = 2013,
volume = 17,
number = 6,
pages = {840--861}
}
@article{SabAyoKen2015tcyb,
author = {N. R. {Sabar} and M. {Ayob} and Graham Kendall and R. {Qu}},
journal = {IEEE Transactions on Cybernetics},
title = {A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems},
year = 2015,
volume = 45,
number = 2,
pages = {217--228}
}
@article{SabAyoKen2015tec,
author = {N. R. Sabar and M. Ayob and Graham Kendall and R. Qu},
journal = {IEEE Transactions on Evolutionary Computation},
title = {Automatic Design of a Hyper-Heuristic Framework With Gene Expression Programming for Combinatorial Optimization Problems},
year = 2015,
volume = 19,
number = 3,
pages = {309--325}
}
@article{SacDuvMai2018ego-ls-svm,
title = {A classification approach to efficient global optimization in
presence of non-computable domains},
author = {Sacher, Matthieu and Duvigneau, R{\'e}gis and Le Maitre,
Olivier and Durand, Mathieu and Berrini, Elisa and Hauville,
Fr{\'e}d{\'e}ric and Astolfi, Jacques-Andr{\'e}},
journal = {Structural and Multidisciplinary Optimization},
volume = 58,
number = 4,
pages = {1537--1557},
year = 2018,
publisher = {Springer},
doi = {10.1007/s00158-018-1981-8},
keywords = {Safe optimization; CMA-ES, Gaussian processes; Least-Squares
Support Vector Machine},
annote = {Proposed EGO-LS-SVM}
}
@article{SadFow2012nosql,
title = {{NoSQL} distilled},
author = {Sadalage, Pramod J. and Fowler, Martin},
journal = {AddisonWesley Professional},
year = 2012
}
@article{SakarWQ00,
author = { A. Burcu Altan Sakarya and Larry W. Mays },
title = {Optimal Operation of Water Distribution Pumps Considering
Water Quality},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
volume = 126,
number = 4,
pages = {210--220},
date = {2000-07/2000-08},
year = 2000,
month = jul # { / } # aug
}
@article{SamPelDarRodPac2016ant,
author = {Marcela Sam\`a and Paola Pellegrini and Andrea D'Ariano and Joaquin Rodriguez and Dario Pacciarelli},
title = {Ant colony optimization for the real-time train routing selection problem},
journal = {Transportation Research Part B: Methodological},
volume = 85,
pages = {89--108},
year = 2016,
doi = {10.1016/j.trb.2016.01.005},
keywords = {irace}
}
@article{Sambridge1999,
title = {Geophysical inversion with a neighbourhood algorithm--I. Searching a parameter space},
author = {Sambridge, Malcolm},
journal = {Geophysical Journal International},
volume = 138,
number = 2,
pages = {479--494},
year = 1999
}
@article{SanDorNeb2019fame,
title = {A novel multi-objective evolutionary algorithm with fuzzy
logic based adaptive selection of operators: {FAME}},
journal = {Information Sciences},
volume = 471,
pages = {233--251},
year = 2019,
issn = {0020-0255},
doi = {10.1016/j.ins.2018.09.005},
author = {Alejandro Santiago and Bernab{\'e} Dorronsoro and Nebro, Antonio J. and Durillo, Juan J. and Oscar Castillo and H{\'e}ctor
J. Fraire},
keywords = {Multi-objective optimization, density estimation,
evolutionary algorithm, adaptive algorithm, fuzzy logic, spatial spread deviation}
}
@article{SanGalRub2008tec,
author = {S\'anchez, Javier and Gal\'an, Manuel and Rubio, Enrique},
title = {Applying a traffic lights evolutionary optimization technique
to a real case: ``{Las} {Ramblas}'' area in {Santa} {Cruz} de
{Tenerife}},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2008,
volume = 12,
number = 1,
pages = {25--40},
keywords = {Cellular automata, Combinatorial optimization, Genetic
algorithms, Microscopic traffic simulator, Traffic lights
optimization}
}
@article{SanGalRub2010tec,
author = {J. J. S\'anchez-Medina and M. J. Gal\'an-Moreno and
E. Rubio-Royo},
title = {Traffic Signal Optimization in ``{La} {Almozara}'' District
in {Saragossa} Under Congestion Conditions, Using Genetic
Algorithms, Traffic Microsimulation, and Cluster Computing},
journal = {IEEE Transactions on Intelligent Transportation Systems},
year = 2010,
volume = 11,
number = 1,
pages = {132--141},
month = mar,
keywords = {cellular automata; genetic algorithms; road traffic;traffic
light programming;urban traffic congestion},
doi = {10.1109/TITS.2009.2034383},
issn = {1524-9050}
}
@article{SanOst2018wrr,
title = {Stochastic Scenario Evaluation in Evolutionary Algorithms
Used for Robust Scenario-Based Optimization},
author = {Sankary, Nathan and Avi Ostfeld },
journal = {Water Resources Research},
volume = 54,
number = 4,
pages = {2813--2833},
year = 2018
}
@article{SanRopHva2018joh,
author = {Alberto Santini and Stefan Ropke and Hvattum, Lars Magnus},
title = {A comparison of acceptance criteria for the adaptive large neighbourhood search metaheuristic},
journal = {Journal of Heuristics},
volume = 24,
pages = {783--815},
year = 2018,
doi = {10.1007/s10732-018-9377-x}
}
@article{Sandgren1988,
author = {Sandgren, E.},
title = {Nonlinear integer and discrete programming in mechanical
design optimization},
volume = 112,
number = 2,
journal = {Journal of Mechanical Design},
pages = {223--229},
year = 1990,
doi = {10.1115/1.2912596}
}
@article{SasBerBie2022deepcave,
author = {Sass, René and Bergman, Eddie and Biedenkapp, Andr{\'e} and Frank Hutter and Marius Thomas Lindauer },
title = {{DeepCAVE}: An Interactive Analysis Tool for Automated
Machine Learning},
journal = {Arxiv preprint arXiv:2206.03493 [cs.LG]},
year = 2022,
doi = {10.48550/arXiv.2206.03493}
}
@article{Savelsbergh85tw,
title = {Local search in routing problems with time windows},
volume = 4,
doi = {10.1007/BF02022044},
abstract = {We develop local search algorithms for routing
problems with time windows. The presented algorithms
are based on thek-interchange concept. The presence
of time windows introduces feasibility constraints,
the checking of which normally requires O(N)
time. Our method reduces this checking effort to
O(1) time. We also consider the problem of finding
initial solutions. A complexity result is given and
an insertion heuristic is described.},
number = 1,
journal = {Annals of Operations Research},
author = { Martin W. P. Savelsbergh },
month = dec,
year = 1985,
pages = {285--305}
}
@article{SaxDurDebZha2013pca,
title = {Objective Reduction in Many-Objective Optimization: Linear
and Nonlinear Algorithms},
author = { Saxena, Dhish Kumar and Jo{\~a}o A. Duro and Tiwari, Anish and Kalyanmoy Deb and Zhang, Qingfu },
journal = {IEEE Transactions on Evolutionary Computation},
volume = 17,
number = 1,
pages = {77--99},
year = 2013,
doi = {10.1109/TEVC.2012.2185847}
}
@article{SchDoeHar09,
author = { Michael Schilde and Karl F. Doerner and Richard F. Hartl and Guenter
Kiechle},
title = {Metaheuristics for the bi-objective orienteering
problem},
number = 3,
journal = {Swarm Intelligence},
year = 2009,
pages = {179--201},
volume = 3,
doi = {10.1007/s11721-009-0029-5},
abstract = {In this paper, heuristic solution
techniques for the multi-objective orienteering
problem are developed. The motivation stems from the
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
Pareto optimal solutions, two metaheuristic search
techniques are developed and applied. We use the
Pareto ant colony optimization algorithm and extend
the design of the variable neighborhood search
method to the multi-objective case. Both methods are
hybridized with path relinking procedures. The
performances of the two algorithms are tested on
several benchmark instances as well as on real world
instances from different Austrian regions and the
cities of Vienna and Padua. The computational
results show that both implemented methods are well
performing algorithms to solve the multi-objective
orienteering problem.}
}
@article{SchEgeBan2009aco,
title = {Extended ant colony optimization for non-convex mixed integer
nonlinear programming},
author = {Schl{\"u}ter, Martin and Egea, Jose A. and Banga, Julio R. },
journal = {Computers \& Operations Research},
volume = 36,
number = 7,
pages = {2217--2229},
year = 2009,
doi = {10.1016/j.cor.2008.08.015}
}
@article{SchEsqLarCoe2012tec,
author = { Oliver Sch{\"u}tze and X. Esquivel and A. Lara and Carlos A. {Coello Coello} },
journal = {IEEE Transactions on Evolutionary Computation},
title = {Using the Averaged {Hausdorff} Distance as a Performance
Measure in Evolutionary Multiobjective Optimization},
year = 2012,
volume = 16,
number = 4,
pages = {504--522}
}
@article{SchHan79,
author = {Josef Schmee and Gerald J. Hahn},
title = {A Simple Method for Regression Analysis with Censored Data},
journal = {Technometrics},
year = 1979,
volume = 21,
number = 4,
pages = {417--432},
publisher = {Taylor \& Francis},
doi = {10.2307/1268280}
}
@article{SchHarSka2017safe,
title = {Safe active learning and safe {Bayesian} optimization for
tuning a {PI}-controller},
author = {Schillinger, Mark and Hartmann, Benjamin and Skalecki, Patric
and Meister, Mona and Nguyen-Tuong, Duy and Nelles, Oliver},
journal = {{IFAC}-{PapersOnLine}},
volume = 50,
number = 1,
pages = {5967--5972},
year = 2017,
doi = {10.1016/j.ifacol.2017.08.1258},
publisher = {Elsevier}
}
@article{SchHenSie2004hiv,
author = {Julie R. Schames and Richard H. Henchman and Jay S. Siegel
and Christoph A. Sotriffer and Haihong Ni and J. Andrew
McCammon},
title = {Discovery of a Novel Binding Trench in {HIV} Integrase},
journal = {Journal of Medicinal Chemistry},
volume = 47,
number = 8,
pages = {1879--1881},
year = 2004,
doi = {10.1021/jm0341913},
annote = {Evolutionary optimization of the first clinically approved
anti-viral drug for HIV}
}
@article{SchHerTal2019archiver,
title = {Archivers for the representation of the set of approximate
solutions for {MOPs}},
author = { Oliver Sch{\"u}tze and Carlos Hern{\'a}ndez and Talbi, El-Ghazali and Sun, Jian-Qiao
and Naranjani, Yousef and Xiong, F-R},
journal = {Journal of Heuristics},
year = 2019,
pages = {71--105},
volume = 25,
doi = {10.1007/s10732-018-9383-z},
keywords = {archiving, nearly optimality, epsilon-dominance, epsilon-approximation, hausdorff convergence}
}
@article{SchKoe2009,
year = 2009,
volume = 123,
number = 4,
pages = {421--433},
author = {Jeffrey C. Schank and Thomas J. Koehnle},
title = {Pseudoreplication is a pseudoproblem},
journal = {Journal of Comparative Psychology}
}
@article{SchLarCoe2011tec,
author = { Oliver Sch{\"u}tze and A. Lara and Carlos A. {Coello Coello} },
journal = {IEEE Transactions on Evolutionary Computation},
title = {On the Influence of the Number of Objectives on the Hardness
of a Multiobjective Optimization Problem},
year = 2011,
volume = 15,
number = 4,
pages = {444--455}
}
@article{SchLauCoeDelTal2008,
title = {Convergence of stochastic search algorithms to finite size
{Pareto} set approximations},
author = { Oliver Sch{\"u}tze and Marco Laumanns and Carlos A. {Coello Coello} and Dellnitz,
Michael and Talbi, El-Ghazali },
journal = {Journal of Global Optimization},
volume = 41,
number = 4,
pages = {559--577},
year = 2008
}
@article{SchLauTanCoeTal2010,
title = {Computing gap free {Pareto} front approximations with
stochastic search algorithms},
author = { Oliver Sch{\"u}tze and Marco Laumanns and Emilia Tantar and Carlos A. {Coello Coello} and Talbi, El-Ghazali },
journal = {Evolutionary Computation},
volume = 18,
number = 1,
pages = {65--96},
year = 2010
}
@article{SchMar1999,
author = {G. R. Schreiber and Olivier Martin },
title = {Cut Size Statistics of Graph Bisection Heuristics},
journal = {SIAM Journal on Optimization},
year = 1999,
volume = 10,
number = 1,
pages = {231--251}
}
@article{SchSchStaDue2000,
title = {Record Breaking Optimization Results Using the Ruin
and Recreate Principle},
journal = {Journal of Computational Physics},
volume = 159,
number = 2,
pages = {139--171},
year = 2000,
author = {Gerhard Schrimpf and Schneider, Johannes and Hermann
Stamm-Wilbrandt and Gunter Dueck}
}
@article{SchSchoTho2019min,
title = {Min-ordering and max-ordering scalarization methods for
multi-objective robust optimization},
author = {Schmidt, Marie and Sch{\"o}bel, Anita and Thom, Lisa},
journal = {European Journal of Operational Research},
volume = 275,
number = 2,
pages = {446--459},
year = 2019,
publisher = {Elsevier}
}
@article{SchSpeKra2018gptut,
author = {Schulz, Eric and Speekenbrink, Maarten and Krause, Andreas},
year = 2018,
month = aug,
pages = {1--16},
title = {A tutorial on {Gaussian} process regression: {Modelling},
exploring, and exploiting functions},
volume = 85,
journal = {Journal of Mathematical Psychology},
doi = {10.1016/j.jmp.2018.03.001}
}
@article{SchStu2004:jmma,
author = { Tommaso Schiavinotto and Thomas St{\"u}tzle },
title = {The Linear Ordering Problem: Instances, Search Space Analysis
and Algorithms},
journal = {Journal of Mathematical Modelling and Algorithms},
year = 2004,
volume = 3,
number = 4,
pages = {367--402}
}
@article{SchStu2007:cor,
author = { Tommaso Schiavinotto and Thomas St{\"u}tzle },
title = {A Review of Metrics on Permutations for Search Space Analysis},
journal = {Computers \& Operations Research},
year = 2007,
volume = 34,
number = 10,
pages = {3143--3153}
}
@article{SchTacWuiSamStu2013,
author = {Tom Schrijvers and Guido Tack and Pieter Wuille and Horst Samulowitz and Peter J. Stuckey},
title = {Search Combinators},
journal = {Constraints},
year = 2013,
volume = 18,
number = 2,
pages = {269--305}
}
@article{SchVasCoe2011space,
author = { Oliver Sch{\"u}tze and Massimiliano Vasile and Carlos A. {Coello Coello} },
title = {Computing the Set of Epsilon-Efficient Solutions in
Multiobjective Space Mission Design},
journal = {Journal of Aerospace Computing, Information, and
Communication},
year = 2011,
volume = 8,
number = 3,
pages = {53--70},
doi = {10.2514/1.46478},
publisher = {American Institute of Aeronautics and Astronautics ({AIAA})}
}
@article{SchWelJon1998,
author = {Matthias Schonlau and William J. Welch and Donald R. Jones},
title = {Global versus Local Search in Constrained Optimization of
Computer Models},
journal = {Lecture Notes-Monograph Series},
year = 1998,
volume = 34,
pages = {11--25},
editor = {Nancy Flournoy and William F. Rosenberger and Weng Kee Wong},
publisher = {Institute of Mathematical Statistics},
doi = {10.2307/4356058}
}
@article{ScheBraTor2022jair,
author = {Schede, Elias and Brandt, Jasmin and Tornede, Alexander and
Wever, Marcel and Bengs, Viktor and Eyke H{\"u}llermeier and Kevin Tierney },
title = {A survey of methods for automated algorithm configuration},
journal = {Journal of Artificial Intelligence Research},
year = 2022,
volume = 75,
pages = {425--487},
doi = {10.1613/jair.1.13676}
}
@article{Scipy2020natmet,
fullauthor = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
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}},
journal = {Nature Methods},
year = 2020,
volume = 17,
pages = {261--272},
epub = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2}
}
@article{Sha1970bfgs,
author = {David F. Shanno},
title = {Conditioning of Quasi-Newton Methods for Function
Minimization},
journal = {Mathematics of Computation},
year = 1970,
volume = 24,
number = 111,
pages = {647--656},
annote = {One of the four papers that proposed BFGS.},
publisher = {American Mathematical Society},
issn = {00255718, 10886842},
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}
}
@article{ShiBac2009niching,
title = {Niching with derandomized evolution strategies in artificial
and real-world landscapes},
author = { Shir, Ofer M. and Thomas B{\"a}ck },
journal = {Natural Computing},
volume = 8,
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},
journal = {Information Sciences},
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},
journal = {Reliability Engineering \& System Safety},
year = 2016,
pages = {96--108},
volume = 148
}
@article{ShmHoo05:bmc,
author = { A. Shmygelska and Holger H. Hoos },
title = {An Ant Colony Optimisation Algorithm for the {2D}
and {3D} Hydrophobic Polar Protein Folding Problem},
journal = {BMC Bioinformatics},
year = 2005,
volume = 6,
pages = 30,
doi = {10.1186/1471-2105-6-30}
}
@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}
}
@article{SimLebNel2010anchoring,
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},
journal = {IIE Transactions},
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}
}
@article{SocBlu07,
author = { Krzysztof Socha and Christian Blum },
title = {An ant colony optimization algorithm for continuous
optimization: An application to feed-forward neural
network training},
journal = {Neural Computing \& Applications},
year = 2007,
volume = 16,
number = 3,
pages = {235--247}
}
@article{SocDor2008:ejor,
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}
}
@article{Sol2002:tec,
author = { Christine Solnon },
title = {Ants Can Solve Constraint Satisfaction Problems},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2002,
volume = 6,
number = 4,
pages = {347--357}
}
@article{SolMarMic2008,
author = {D. Soler and E. Mart{\'i}nez and J. C. Mic\'o},
title = {A Transformation for the Mixed General Routing Problem with Turn Penalties},
journal = {Journal of the Operational Research Society},
year = 2008,
volume = 59,
pages = {540--547}
}
@article{Solomon87vrptw,
author = { M. M. Solomon },
title = {Algorithms for the Vehicle Routing and Scheduling
Problems with Time Windows},
journal = {Operations Research},
year = 1987,
volume = 35,
pages = {254--265}
}
@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}
}
@article{SorOchSotBur2017:ejor,
author = {Jorge A. Soria-Alcaraz and Gabriela Ochoa and Marco A. Sotelo-Figeroa and Edmund K. Burke },
title = {A Methodology for Determining an Effective Subset of Heuristics in Selection Hyper-heuristics},
journal = {European Journal of Operational Research},
year = 2017,
volume = 260,
pages = {972--983}
}
@article{SouRitLop2021cap,
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},
journal = {Computers \& Operations Research},
doi = {10.1016/j.cor.2021.105615},
year = 2022,
volume = 139,
pages = 105615,
supplement = {https://github.com/souzamarcelo/supp-cor-capopt},
abstract = {Automatic configuration techniques are widely and
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
optimize the cost of the best solution found, given a
predefined running time limit. We propose new capping methods
for the automatic configuration of optimization
algorithms. They use the previous executions to determine a
performance envelope, which is used to evaluate new
executions and cap those that do not satisfy the envelope
conditions. We integrate the capping methods into the irace
configurator and evaluate them on different optimization
scenarios. Our results show that the proposed methods can
save from about 5\% to 78\% of the configuration effort,
while finding configurations of the same quality. Based on
the computational analysis, we identify two conservative and
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.}
}
@article{Souilah1995,
title = {Simulated annealing for manufacturing systems layout design},
author = {Souilah, Abdelghani},
journal = {European Journal of Operational Research},
volume = 82,
number = 3,
pages = {592--614},
year = 1995,
publisher = {Elsevier}
}
@article{Spearman1904,
title = {The proof and measurement of association between two things},
author = {Charles Spearman},
journal = {The American journal of psychology},
volume = 15,
number = 1,
pages = {72--101},
year = 1904,
publisher = {University of Illinois Press}
}
@article{SpecCPU2000,
author = {J. L. Henning},
journal = {Computer},
title = {{SPEC} {CPU2000}: measuring {CPU} performance in the New Millennium},
publisher = {IEEE Computer Society Press},
year = 2000,
volume = 33,
number = 7,
pages = {28--35},
doi = {10.1109/2.869367}
}
@article{SpiTen2004,
title = {Smoothed analysis of algorithms: Why the simplex algorithm
usually takes polynomial time},
author = {Spielman, Daniel A. and Teng, Shang-Hua},
journal = {Journal of the ACM},
volume = 51,
number = 3,
pages = {385--463},
year = 2004,
publisher = {ACM Press}
}
@article{SprHarDre1997ors,
author = {Sprecher, Arno and Hartmann, S{\"o}nke and Drexl, Andreas},
title = {An exact algorithm for project scheduling with
multiple modes},
volume = 19,
doi = {10.1007/BF01545587},
number = 3,
journal = {OR Spektrum},
year = 1997,
keywords = {branch-and-bound, multi-mode resource-constrained
project scheduling, project scheduling},
pages = {195--203}
}
@article{SprKolDre1995ejor,
author = {Sprecher, Arno and Kolisch, Rainer and Drexl,
Andreas},
title = {Semi-active, active, and non-delay schedules for the
resource-constrained project scheduling problem},
volume = 80,
doi = {10.1016/0377-2217(93)E0294-8},
abstract = {We consider the resource-constrained project
scheduling problem ({RCPSP).} The focus of the paper
is on a formal definition of semi-active, active,
and non-delay schedules. Traditionally these
schedules establish basic concepts within the job
shop scheduling literature. There they are usually
defined in a rather informal way which does not
create any substantial problems. Using these
concepts in the more general {RCPSP} without giving
a formal definition may cause serious
problems. After providing a formal definition of
semi-active, active, and non-delay schedules for the
{RCPSP} we outline some of these problems occurring
within the disjunctive arc concept.},
number = 1,
journal = {European Journal of Operational Research},
year = 1995,
keywords = {active schedules, Branch-and-bound methods,
non-delay schedules, Resource-constrained project
scheduling, Semi-active schedules},
pages = {94--102}
}
@article{SriDeb1994:nsga,
author = {N. Srinivas and Kalyanmoy Deb },
title = {Multiobjective Optimization Using Nondominated
Sorting in Genetic Algorithms},
journal = {Evolutionary Computation},
year = 1994,
volume = 2,
pages = {221--248},
number = 3
}
@article{Ste1996jmcda,
author = { T. J. Stewart },
title = {Robustness of Additive Value Function Methods in {MCDM}},
journal = {Journal of Multi-Criteria Decision Analysis},
year = 1996,
volume = 5,
number = 4,
pages = {301--309},
keywords = {machine decision-making}
}
@article{Ste1999ejor,
author = { T. J. Stewart },
title = {Evaluation and refinement of aspiration-based methods in {MCDM}},
journal = {European Journal of Operational Research},
year = 1999,
volume = 113,
number = 3,
pages = {643--652},
keywords = {machine decision-making}
}
@article{Ste2005jors,
author = { T. J. Stewart },
title = {Goal programming and cognitive biases in decision-making},
volume = 56,
doi = {10.1057/palgrave.jors.2601948},
number = 10,
journal = {Journal of the Operational Research Society},
year = 2005,
keywords = {machine decision making},
pages = {1166--1175}
}
@article{SteFreRio2013omg,
title = {Integrating multicriteria decision analysis and scenario
planning: Review and extension},
author = { T. J. Stewart and Simon French and Jesus Rios},
journal = {Omega},
year = 2013,
number = 4,
pages = {679--688},
volume = 41,
doi = {10.1016/j.omega.2012.09.003},
keywords = {Multicriteria decision analysis}
}
@article{SteHeiHah2020,
author = {Stegherr, Helena and Heider, Michael and H\"ahner, J\"org},
title = {Classifying Metaheuristics: Towards a unified multi-level classification system},
journal = {Natural Computing},
year = 2020,
doi = {10.1007/s11047-020-09824-0}
}
@article{SteRad2008computing,
title = {Computing all efficient solutions of the biobjective minimum
spanning tree problem},
author = {Steiner, Sarah and Radzik, Tomasz},
journal = {Computers \& Operations Research},
year = 2008,
number = 1,
pages = {198--211},
volume = 35
}
@article{Sto2014edge,
author = {Stodden, Victoria},
title = {What scientific idea is ready for retirement?
Reproducibility},
journal = {Edge},
year = 2014,
url = {https://www.edge.org/annual-question/2014/response/25340},
annote = {Introduces computational reproducibility, empirical
reproducibility and statistical reproducibility}
}
@article{StoAlb2014,
author = {Stolfi, Daniel H. and Alba, Enrique },
doi = {10.1016/j.asoc.2014.07.014},
journal = {Applied Soft Computing},
keywords = {Evolutionary algorithm,Road traffic,Smart city,Smart
mobility,Traffic light,WiFi connections},
pages = {181--195},
title = {Red Swarm: Reducing travel times in smart cities by using
bio-inspired algorithms},
volume = 24,
year = 2014,
abstract = {This article presents an innovative approach to solve one of
the most relevant problems related to smart mobility: the
reduction of vehicles' travel time. Our original approach,
called Red Swarm, suggests a potentially customized route to
each vehicle by using several spots located at traffic lights
in order to avoid traffic jams by using \{V2I\}
communications. That is quite different from other existing
proposals, as it deals with real maps and actual streets, as
well as several road traffic distributions. We propose an
evolutionary algorithm (later efficiently parallelized) to
optimize our case studies which have been imported from
OpenStreetMap into \{SUMO\} as they belong to a real city. We
have also developed a Rerouting Algorithm which accesses the
configuration of the Red Swarm and communicates the route
chosen to vehicles, using the spots (via WiFi
link). Moreover, we have developed three competing algorithms
in order to compare their results to those of Red Swarm and
have observed that Red Swarm not only achieved the best
results, but also outperformed the experts' solutions in a
total of 60 scenarios tested, with up to 19\% shorter travel
times.}
}
@article{StoMcNutBai2016enhancing,
doi = {10.1126/science.aah6168},
year = 2016,
month = dec,
publisher = {American Association for the Advancement of Science ({AAAS})},
volume = 354,
number = 6317,
pages = {1240--1241},
author = {Stodden, Victoria and Marcia McNutt and David H. Bailey and Ewa Deelman and
Yolanda Gil and Brooks Hanson and Michael A. Heroux and John P. A. Ioannidis and Michela Taufer},
title = {Enhancing reproducibility for computational methods},
journal = {Science}
}
@article{StoPri1997:de,
author = {Storn, Rainer and Price, Kenneth},
title = {Differential Evolution -- A Simple and Efficient Heuristic
for Global Optimization over Continuous Spaces},
journal = {Journal of Global Optimization},
year = 1997,
volume = 11,
number = 4,
pages = {341--359},
annote = {Proposed differential evolution},
doi = {10.1023/A:1008202821328}
}
@article{StoSeiZha2018pnas,
author = {Stodden, Victoria and Jennifer Seiler and Zhaokun Ma},
title = {An empirical analysis of journal policy effectiveness for
computational reproducibility},
doi = {10.1073/pnas.1708290115},
year = 2018,
month = mar,
journal = {Proceedings of the National Academy of Sciences},
volume = 115,
number = 11,
pages = {2584--2589}
}
@article{StrKir1991,
title = {Analysis of Finite Length Annealing Schedules},
author = { Philip N. Strenski and Scott Kirkpatrick },
journal = {Algorithmica},
volume = 6,
number = {1-6},
pages = {346--366},
year = 1991,
publisher = {Springer}
}
@article{StrLopBroLee2020,
title = {General Northern English: Exploring regional variation in the
North of England with machine learning},
author = { Strycharczuk, Patrycja and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Brown, Georgina and Adrian Leemann },
journal = { Frontiers in Artificial Intelligence },
year = 2020,
volume = 3,
number = 48,
keywords = {vowels, accent features, dialect leveling, Random forest
(bagging), Feature selecion},
doi = {10.3389/frai.2020.00048},
abstract = {In this paper, we present a novel computational approach to
the analysis of accent variation. The case study is dialect
leveling in the North of England, manifested as reduction of
accent variation across the North and emergence of General
Northern English (GNE), a pan-regional standard accent
associated with middle-class speakers. We investigated this
instance of dialect leveling using random forest
classification, with audio data from a crowd-sourced corpus
of 105 urban, mostly highly-educated speakers from five
northern UK cities: Leeds, Liverpool, Manchester, Newcastle
upon Tyne, and Sheffield. We trained random forest models to
identify individual northern cities from a sample of other
northern accents, based on first two formant measurements of
full vowel systems. We tested the models using unseen
data. We relied on undersampling, bagging (bootstrap
aggregation) and leave-one-out cross-validation to address
some challenges associated with the data set, such as
unbalanced data and relatively small sample size. The
accuracy of classification provides us with a measure of
relative similarity between different pairs of cities, while
calculating conditional feature importance allows us to
identify which input features (which vowels and which
formants) have the largest influence in the prediction. We do
find a considerable degree of leveling, especially between
Manchester, Leeds and Sheffield, although some differences
persist. The features that contribute to these differences
most systematically are typically not the ones discussed in
previous dialect descriptions. We propose that the most
systematic regional features are also not salient, and as
such, they serve as sociolinguistic regional indicators. We
supplement the random forest results with a more traditional
variationist description of by-city vowel systems, and we use
both sources of evidence to inform a description of the
vowels of General Northern English.}
}
@article{Stu06:ejor,
author = { Thomas St{\"u}tzle },
title = {Iterated Local Search for the Quadratic Assignment Problem},
journal = {European Journal of Operational Research},
year = 2006,
volume = 174,
number = 3,
pages = {1519--1539}
}
@article{StuDor2002:tec,
author = { Thomas St{\"u}tzle and Marco Dorigo },
title = {A Short Convergence Proof for a Class of {ACO}
Algorithms},
volume = 6,
number = 4,
pages = {358--365},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2002
}
@article{StuHoo2000:fgcs,
author = { Thomas St{\"u}tzle and Holger H. Hoos },
title = {{\MaxMinAntSystem}},
journal = {Future Generation Computer Systems},
year = 2000,
volume = 16,
number = 8,
pages = {889--914}
}
@article{SuZhaYue2021enhanced,
title = {Enhanced Constraint Handling for Reliability-Constrained
Multiobjective Testing Resource Allocation},
author = {Su, Zhaopin and Zhang, Guofu and Yue, Feng and Zhan, Dezhi and Li, Miqing and Li, Bin and Xin Yao },
journal = {IEEE Transactions on Evolutionary Computation},
year = 2021,
number = 3,
pages = {537--551},
volume = 25,
publisher = {IEEE}
}
@article{SubBat2013,
author = { Anand Subramanian and Maria Battarra},
title = {An Iterated Local Search Algorithm for the Travelling Salesman Problem with Pickups and Deliveries},
journal = {Journal of the Operational Research Society},
year = 2013,
volume = 64,
number = 3,
pages = {402--409}
}
@article{SubBatPot2014,
author = { Anand Subramanian and Maria Battarra and Chris N. Potts },
title = {An Iterated Local Search Heuristic for the Single Machine Total Weighted Tardiness Scheduling Problem with Sequence-dependent Setup Times},
journal = {International Journal of Production Research},
year = 2014,
volume = 52,
number = 9,
pages = {2729--2742}
}
@article{SuiZhuBur2018arxiv,
author = {Sui, Yanan and Zhuang, Vincent and Burdick, Joel W. and Yue,
Yisong},
title = {Stagewise Safe {Bayesian} Optimization with {Gaussian}
Processes},
journal = {Arxiv preprint arXiv:1806.07555},
year = 2018,
note = {Published as \cite{SuiZhuBur2018stageopt}},
url = {https://arxiv.org/abs/1806.07555},
abstract = {Enforcing safety is a key aspect of many problems pertaining
to sequential decision making under uncertainty, which
require the decisions made at every step to be both
informative of the optimal decision and also safe. For
example, we value both efficacy and comfort in medical
therapy, and efficiency and safety in robotic control. We
consider this problem of optimizing an unknown utility
function with absolute feedback or preference feedback
subject to unknown safety constraints. We develop an
efficient safe Bayesian optimization algorithm, StageOpt,
that separates safe region expansion and utility function
maximization into two distinct stages. Compared to existing
approaches which interleave between expansion and
optimization, we show that StageOpt is more efficient and
naturally applicable to a broader class of problems. We
provide theoretical guarantees for both the satisfaction of
safety constraints as well as convergence to the optimal
utility value. We evaluate StageOpt on both a variety of
synthetic experiments, as well as in clinical practice. We
demonstrate that StageOpt is more effective than existing
safe optimization approaches, and is able to safely and
effectively optimize spinal cord stimulation therapy in our
clinical experiments.},
keywords = {Safe Optimization, StageOpt}
}
@article{SunYenYi2019igd,
title = {{IGD} Indicator-based Evolutionary Algorithm for
Many-objective Optimization Problems},
author = {Sun, Yanan and Yen, Gary G. and Yi, Zhang},
journal = {IEEE Transactions on Evolutionary Computation},
publisher = {IEEE},
volume = 23,
number = 2,
pages = {173--187},
year = 2019,
doi = {10.1109/TEVC.2018.2791283}
}
@article{SupSefPar2000,
title = {A simulated annealing algorithm for multiobjective optimization},
author = {Suppapitnarm, A. and Seffen, K. A. and Parks, G. T. and Clarkson, P. J.},
journal = {Engineering Optimization},
volume = 33,
number = 1,
pages = {59--85},
year = 2000
}
@article{SuyVan1999least,
title = {Least Squares Support Vector Machine Classifiers},
author = {Suykens, Johan A. K. and Vandewalle, Joos},
journal = {Neural Processing Letters},
volume = 9,
number = 3,
pages = {293--300},
year = 1999,
publisher = {Springer},
doi = {10.1023/A:1018628609742},
keywords = {LS-SVM}
}
@article{SwaAdrBar2019extending,
author = { Jerry Swan and Steven Adriaensen and Barwell, Adam D. and Hammond,
Kevin and White, David R.},
title = {Extending the ``Open-Closed Principle'' to Automated
Algorithm Configuration},
journal = {Evolutionary Computation},
year = 2019,
volume = 27,
number = 1,
pages = {173--193},
doi = {10.1162/evco_a_00245}
}
@article{SwaAdrBro2022metah,
author = { Jerry Swan and Steven Adriaensen and Alexander
E. I. Brownlee and Kevin Hammond and Colin
G. Johnson and Ahmed Kheiri and Faustyna Krawiec and Juan-Juli{\'a}n Merelo and Leandro L. Minku and Ender {\"O}zcan and Gisele Pappa and Pablo Garc{\'i}a-S{\'a}nchez and Kenneth S{\"o}rensen and Stefan Vo{\ss} and Markus Wagner and David R. White},
doi = {10.1016/j.ejor.2021.05.042},
year = 2022,
month = mar,
volume = 297,
number = 2,
pages = {393--406},
title = {Metaheuristics ``In the Large''},
journal = {European Journal of Operational Research}
}
@article{SwaWooOzc2014cogcomp,
author = { Jerry Swan and John R. Woodward and Ender {\"O}zcan and Graham Kendall and Edmund K. Burke },
title = {Searching the Hyper-heuristic Design Space},
journal = {Cognitive Computation},
year = 2014,
volume = 6,
number = 1,
pages = {66--73},
month = mar,
doi = {10.1007/s12559-013-9201-8}
}
@article{SzuHar1987,
title = {Fast Simulated Annealing},
author = { Harold Szu and Ralph Hartley },
journal = {Physics Letters A},
volume = 122,
number = 3,
pages = {157--162},
year = 1987,
publisher = {Elsevier}
}
@article{Tai90,
author = { {\'E}ric D. Taillard },
title = {Some Efficient Heuristic Methods for the Flow Shop
Sequencing Problem},
journal = {European Journal of Operational Research},
year = 1990,
volume = 47,
number = 1,
pages = {65--74}
}
@article{Tai91,
author = { {\'E}ric D. Taillard },
title = {Robust Taboo Search for the Quadratic Assignment Problem},
journal = {Parallel Computing},
year = 1991,
volume = 17,
number = {4-5},
pages = {443--455},
annote = {faster 2-exchange delta evaluation in QAP}
}
@article{Tai93,
author = { {\'E}ric D. Taillard },
title = {Benchmarks for Basic Scheduling Problems},
journal = {European Journal of Operational Research},
year = 1993,
volume = 64,
number = 2,
pages = {278--285}
}
@article{Tai95,
author = { {\'E}ric D. Taillard },
title = {Comparison of Iterative Searches for the Quadratic
Assignment Problem},
journal = {Location Science},
year = 1995,
volume = 3,
number = 2,
pages = {87--105}
}
@article{Talbi02,
author = { Talbi, El-Ghazali },
title = {A Taxonomy of Hybrid Metaheuristics},
journal = {Journal of Heuristics},
year = 2002,
volume = 8,
number = 5,
pages = {541--564}
}
@article{Tam1992,
title = {A Simulated Annealing Algorithm for Allocating Space to Manufacturing Cells},
author = { Tam, Kar Yan },
journal = {International Journal of Production Research},
volume = 30,
number = 1,
pages = {63--87},
year = 1992,
publisher = {Taylor \& Francis}
}
@article{TamJonEld1995,
author = {Tamiz, M. and Jones, D. F. and El-Darzi, E.},
title = {A review of {Goal} {Programming} and its applications},
journal = {Annals of Operations Research},
year = 1995,
volume = 58,
number = 1,
pages = {39--53},
month = jan,
doi = {10.1007/BF02032309},
abstract = {This paper presents a review of the current literature on the
branch of multi-criteria decision modelling known as Goal
Programming (GP). The result of our indepth investigations of
the two main GP methods, lexicographic and weighted GP
together with their distinct application areas is
reported. Some guidelines to the scope of GP as an
application tool are given and methods of determining which
problem areas are best suited to the different GP approaches
are proposed. The correlation between the method of assigning
weights and priorities and the standard of the results is
also ascertained.},
language = {en},
keywords = {Goal Programming, lexicographic, weighted}
}
@article{TanAra2013,
author = {Shunji Tanaka and Mituhiko Araki},
title = {An Exact Algorithm for the Single-machine Total Weighted Tardiness
Problem with Sequence-dependent Setup Times},
journal = {Computers \& Operations Research},
year = 2013,
volume = 40,
number = 1,
pages = {344--352}
}
@article{TanIsh2020re,
author = {Ryoji Tanabe and Ishibuchi, Hisao },
title = {An easy-to-use real-world multi-objective optimization
problem suite},
journal = {Applied Soft Computing},
volume = 89,
pages = 106078,
year = 2020,
annote = {Proposed the RE benchmark suite}
}
@article{TanIshOya2017,
author = {Ryoji Tanabe and Ishibuchi, Hisao and Akira Oyama},
journal = {{IEEE} Access},
title = {Benchmarking Multi- and Many-Objective Evolutionary
Algorithms Under Two Optimization Scenarios},
year = 2017,
volume = 5,
pages = {19597--19619},
annote = {compared a number of MOEAs using a wide range of numbers of
objectives and stopping criteria, with and without archivers; unbounded archive}
}
@article{TanWan2006,
author = {Lixin Tang and Xianpeng Wang},
title = {Iterated local search algorithm based on very large-scale
neighborhood for prize-collecting vehicle routing problem},
journal = {International Journal of Advanced Manufacturing Technology},
year = 2006,
volume = 29,
number = 11,
pages = {1246--1258}
}
@article{Tarquin89,
author = { A. J. Tarquin and J. Dowdy },
title = {Optimal pump operation in water distribution},
journal = { Journal of Hydraulic Engineering, {ASCE}},
year = 1989,
volume = 115,
number = 2,
pages = {158--169 or 496--501},
month = feb,
note = {}
}
@article{TasKizPanSug2017,
author = {M. F. Tasgetiren and D. Kizilay and Quan-Ke Pan and Ponnuthurai N. Suganthan },
title = {Iterated Greedy Algorithms for the Blocking Flowshop
Scheduling Problem with Makespan Criterion},
journal = {Computers \& Operations Research},
year = 2017,
volume = 77,
pages = {111--126}
}
@article{TasLiaSevGen2007,
author = {M. Fatih Tasgetiren and Yun-Chia Liang and Mehmet Sevkli and
Gunes Gencyilmaz},
title = {A particle swarm optimization algorithm for makespan and
total flowtime minimization in the permutation flowshop
sequencing problem},
journal = {European Journal of Operational Research},
year = 2007,
volume = 177,
number = 3,
pages = {1930--1947},
doi = {10.1016/j.ejor.2005.12.024}
}
@article{TasPanSugBuy2013:cor,
author = {M. Fatih Tasgetiren and Quan-Ke Pan and Ponnuthurai N. Suganthan and Ozge
Buyukdagli},
title = {A variable iterated greedy algorithm with differential
evolution for the no-idle permutation flowshop scheduling
problem},
journal = {Computers \& Operations Research},
year = 2013,
volume = 40,
number = 7,
pages = {1729--1743}
}
@article{TayHo2008cie,
author = {Joc Cing Tay and Nhu Binh Ho},
title = {Evolving dispatching rules using genetic programming for
solving multi-objective flexible job-shop problems},
journal = {Computers and Industrial Engineering},
volume = 54,
number = 3,
pages = {453 -- 473},
year = 2008,
doi = {10.1016/j.cie.2007.08.008}
}
@article{TeiCovStuCun11itor,
author = {Cristina Teixeira and Jos{\'e} Covas and Thomas St{\"u}tzle and Ant{\'o}nio Gaspar{-}Cunha },
title = {Engineering an Efficient Two-Phase Local Search for the
Co-Rotating Twin-Screw Configuration Problem},
journal = {International Transactions in Operational Research},
year = 2011,
volume = 18,
number = 2,
pages = {271--291}
}
@article{TeiCovStuCun12eo,
author = {Cristina Teixeira and Jos{\'e} Covas and Thomas St{\"u}tzle and Ant{\'o}nio Gaspar{-}Cunha },
title = {Multi-Objective Ant Colony Optimization for Solving the
Twin-Screw Extrusion Configuration Problem},
journal = {Engineering Optimization},
year = 2012,
volume = 44,
number = 3,
pages = {351--371}
}
@article{TeiCovStuCun14asc,
author = {Cristina Teixeira and Jos{\'e} Covas and Thomas St{\"u}tzle and Ant{\'o}nio Gaspar{-}Cunha },
title = {Hybrid Algorithms for the Twin-Screw Extrusion Configuration
Problem},
journal = {Applied Soft Computing},
year = 2014,
volume = 23,
pages = {298--307}
}
@article{Teklu2007,
author = {Teklu, Fitsum and Sumalee, Agachai and Watling, David},
doi = {10.1111/j.1467-8667.2006.00468.x},
journal = {Computer-Aided Civil and Infrastructure Engineering},
month = jan,
number = 1,
pages = {31--43},
title = {A Genetic Algorithm Approach for Optimizing Traffic Control
Signals Considering Routing},
volume = 22,
year = 2007
}
@article{TenSilLan2000,
author = {J. B. Tenenbaum and V. D. Silva and J. C. Langford},
title = {A global geometric framework for nonlinear dimensionality
reduction},
journal = {Science},
volume = 290,
number = 5500,
year = 2000,
pages = {2319--2323}
}
@article{TeoAbb2004ec,
author = {J. Teo and Abbass, Hussein A. },
title = {Automatic generation of controllers for embodied
legged organisms: A {Pareto} evolutionary
multi-objective approach},
journal = {Evolutionary Computation},
year = 2004,
volume = 12,
number = 3,
pages = {355--394},
doi = {10.1162/1063656041774974}
}
@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.}
}
@article{Thi2011cw,
author = {Patrick Thibodeau},
title = {Machine-based decision-making is coming},
journal = {Computer World},
year = 2011,
month = nov,
url = {http://www.computerworld.com/s/article/359630/Machine_Based_Decision_Making_Is_Coming},
note = {Last accessed: 15 January 2014}
}
@article{ThiMieKorMol2009ec,
author = { Lothar Thiele and Kaisa Miettinen and Pekka Korhonen and Molina, Juli{\'a}n },
title = {A Preference-Based Evolutionary Algorithm for Multi-Objective
Optimization},
journal = {Evolutionary Computation},
volume = 17,
number = 3,
pages = {411--436},
year = 2009,
doi = {10.1162/evco.2009.17.3.411},
abstract = { Abstract In this paper, we discuss the idea of incorporating
preference information into evolutionary multi-objective
optimization and propose a preference-based evolutionary
approach that can be used as an integral part of an
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
Pareto optimal set does not have to be generated with equal
accuracy. The approach is demonstrated by numerical
examples. }
}
@article{TiaCheZha2017indicator,
title = {An Indicator-Based Multiobjective Evolutionary Algorithm With
Reference Point Adaptation for Better Versatility},
author = {Ye Tian and Ran Cheng and Xingyi Zhang and Fan Cheng and Yaochu Jin },
journal = {IEEE Transactions on Evolutionary Computation},
year = 2018,
volume = 22,
number = 4,
pages = {609--622},
doi = {10.1109/TEVC.2017.2749619},
annote = {IGD-based archiver}
}
@article{TinRaoLoo2003joh,
author = { Tiew-On Ting and M. V. C. Rao and C. K. Loo and S. S. Ngu },
title = {Solving Unit Commitment Problem Using Hybrid
Particle Swarm Optimization},
journal = {Journal of Heuristics},
year = 2003,
volume = 9,
number = 6,
pages = {507--520},
doi = {10.1023/B:HEUR.0000012449.84567.1a}
}
@article{TiwFadDeb2011amga2,
title = {{AMGA2}: Improving the performance of the archive-based
micro-genetic algorithm for multi-objective optimization},
author = {Tiwari, Santosh and Fadel, Georges and Kalyanmoy Deb },
journal = {Engineering Optimization},
year = 2011,
number = 4,
pages = {377--401},
volume = 43
}
@article{TkiMonTer2002ejor,
author = { V. {T'Kindt} and Nicolas Monmarch{\'e} and F. Tercinet and D. La{\"u}gt},
title = {An ant colony optimization algorithm to solve a
2-machine bicriteria flowshop scheduling problem},
journal = {European Journal of Operational Research},
year = 2002,
volume = 142,
number = 2,
pages = {250--257}
}
@article{TomKad2019decomposition,
title = {Decomposition-based interactive evolutionary algorithm for
multiple objective optimization},
author = { Tomczyk, Micha{\l} K and Kadzi{\'n}ski, Mi{\l}osz },
journal = {IEEE Transactions on Evolutionary Computation},
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}
}
@article{TomKad2019emosor,
author = { Tomczyk, Micha{\l} K and Kadzi{\'n}ski, Mi{\l}osz },
title = {{EMOSOR}: Evolutionary multiple objective optimization guided
by interactive stochastic ordinal regression},
journal = {Computers \& Operations Research},
volume = 108,
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.}
}
@article{TorRosKefetal2010:si,
author = {C. E. Torres and L. F. Rossi and J. Keffer and K. Li and
C.-C. Shen},
title = {Modeling, analysis and simulation of ant-based network
routing protocols},
journal = {Swarm Intelligence},
volume = 4,
number = 3,
pages = {221--244},
year = 2010
}
@article{TraMeh2009eo,
author = { Heike Trautmann and J\"{o}rn Mehnen},
title = {Preference-based {Pareto} optimization in certain and
noisy environments},
journal = {Engineering Optimization},
volume = 41,
number = 1,
pages = {23--38},
month = jan,
year = 2009
}
@article{TriLop2015plos,
author = { Vito Trianni and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Advantages of Task-Specific Multi-Objective Optimisation in
Evolutionary Robotics},
journal = {PLoS One},
year = 2015,
volume = 10,
number = 8,
pages = {e0136406},
doi = {10.1371/journal.pone.0136406},
abstract = {The application of multi-objective optimisation to
evolutionary robotics is receiving increasing attention. A
survey of the literature reveals the different possibilities
it offers to improve the automatic design of efficient and
adaptive robotic systems, and points to the successful
demonstrations available for both task-specific and
task-agnostic approaches (i.e., with or without reference to
the specific design problem to be tackled). However, the
advantages of multi-objective approaches over
single-objective ones have not been clearly spelled out and
experimentally demonstrated. This paper fills this gap for
task-specific approaches: starting from well-known results in
multi-objective optimisation, we discuss how to tackle
commonly recognised problems in evolutionary robotics. In
particular, we show that multi-objective optimisation (i)
allows evolving a more varied set of behaviours by exploring
multiple trade-offs of the objectives to optimise, (ii)
supports the evolution of the desired behaviour through the
introduction of objectives as proxies, (iii) avoids the
premature convergence to local optima possibly introduced by
multi-component fitness functions, and (iv) solves the
bootstrap problem exploiting ancillary objectives to guide
evolution in the early phases. We present an experimental
demonstration of these benefits in three different case
studies: maze navigation in a single robot domain, flocking
in a swarm robotics context, and a strictly collaborative
task in collective robotics.}
}
@article{TriNol2011al,
author = { Vito Trianni and Nolfi, S.},
title = {Engineering the evolution of self-organizing
behaviors in swarm robotics: A case study},
journal = {Artificial Life},
year = 2011,
volume = 17,
number = 3,
pages = {183--202}
}
@article{TriSriSanGho2016survey,
title = {A survey of multiobjective evolutionary algorithms based on
decomposition},
author = {Trivedi, Anupam and Srinivasan, Dipti and Sanyal, Krishnendu
and Ghosh, Abhiroop},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2016,
number = 3,
pages = {440--462},
volume = 21,
publisher = {IEEE}
}
@article{TseLin09,
author = { L.-Y. Tseng and Y.-T. Lin },
title = {A hybrid genetic local search algorithm for the permutation
flowshop scheduling problem},
journal = {European Journal of Operational Research},
volume = 198,
number = 1,
pages = {84--92},
year = 2009
}
@article{Tsu2007tjsai,
author = {Tsutsui, S.},
title = {Ant Colony Optimization with Cunning Ants},
journal = {Transactions of the Japanese Society for Artificial Intelligence},
keywords = {ant colony optimization, traveling salesman problem, cunning
ant, donor ant, local search},
year = 2007,
volume = 22,
pages = {29--36},
doi = {10.1527/tjsai.22.29}
}
@article{TugThrFil2017jbe,
title = {Conceptual Design of Modular Bridges Including Layout
Optimization and Component Reusability},
author = {Tugilimana, Alexis and Thrall, Ashley P. and Filomeno Coelho,
Rajan},
journal = {Journal of Bridge Engineering},
volume = 22,
number = 11,
pages = 04017094,
year = 2017,
keywords = {scenario-based},
doi = {10.1061/(ASCE)BE.1943-5592.0001138},
publisher = {American Society of Civil Engineers}
}
@article{TurSorHva2021meta,
author = {Renata Turkeš and Kenneth S{\"o}rensen and Lars Magnus Hvattum},
title = {Meta-analysis of metaheuristics: Quantifying the effect of
adaptiveness in adaptive large neighborhood search},
journal = {European Journal of Operational Research},
volume = 292,
number = 2,
pages = {423--42},
year = 2021,
doi = {10.1016/j.ejor.2020.10.045},
keywords = {Metaheuristics, Meta-analysis, Adaptive large neighborhood
search}
}
@article{TusFil2014mpe,
author = { Tea Tu{\v s}ar and Bogdan Filipi{\v c}},
title = {Visualizing Exact and Approximated {3D} Empirical Attainment
Functions},
journal = {Mathematical Problems in Engineering},
volume = 2014,
note = {Article ID 569346, 18 pages},
year = 2014,
doi = {10.1155/2014/569346},
comment = {This is how Tea Tusar suggests to encode this entry in
bibtex.}
}
@article{TusFil2015tec,
title = {Visualization of {Pareto} front approximations in
evolutionary multiobjective optimization: A critical review
and the prosection method},
author = { Tea Tu{\v s}ar and Bogdan Filipi{\v c}},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2015,
volume = 19,
number = 2,
pages = {225--245},
doi = {10.1109/TEVC.2014.2313407}
}
@article{Tuyttens00,
author = {D. Tuyttens and Jacques Teghem and Philippe Fortemps and K. Van Nieuwenhuyze},
title = {Performance of the {MOSA} Method for the Bicriteria Assignment Problem},
journal = {Journal of Heuristics},
year = 2000,
volume = 6,
pages = {295--310}
}
@article{TveKah1974judgment,
title = {Judgment under uncertainty: Heuristics and biases},
author = { Tversky, Amos and Kahneman, Daniel },
journal = {Science},
volume = 185,
number = 4157,
pages = {1124--1131},
year = 1974,
publisher = {American association for the advancement of science}
}
@article{TveKah1991,
author = { Tversky, Amos and Kahneman, Daniel },
year = 1991,
title = {Loss aversion in riskless choice: a reference-dependent
model},
journal = {The Quarterly Journal of Economics},
volume = 106,
number = 4,
pages = {1039--1061}
}
@article{Tversky1972,
author = { Tversky, Amos },
year = 1972,
title = {Choice by elimination},
journal = {Journal of Mathematical Psychology},
volume = 9,
number = 4,
pages = {341--367}
}
@article{TwoStuDorManBir2010is,
author = {Colin Twomey and Thomas St{\"u}tzle and Marco Dorigo and Max Manfrin and Mauro Birattari },
title = {An Analysis of Communication Policies for Homogeneous Multi-colony {ACO} Algorithms},
journal = {Information Sciences},
year = 2010,
volume = 180,
number = 12,
pages = {2390--2404},
doi = {10.1016/j.ins.2010.02.017}
}
@article{UluTeg1995,
author = { E. Ulungu and Jacques Teghem },
title = {The two phases method: An efficient procedure
to solve bi-objective combinatorial optimization
problems},
journal = {Foundations of Computing and Decision Sciences},
volume = 20,
number = 2,
pages = {149--165},
year = 1995
}
@article{UluTegFor1999,
author = { E. Ulungu and Jacques Teghem and Fortemps, P. H. and Tuyttens, D.},
title = {{MOSA} method: a tool for solving multiobjective
combinatorial optimization problems},
journal = {Journal of Multi-Criteria Decision Analysis},
number = 4,
pages = {221--236},
volume = 8,
year = 1999
}
@article{UrlRuiSef2010,
author = {Thijs Urlings and Rub{\'e}n Ruiz and F. Sivrikaya-\c{S}erifo\u{g}lu},
title = {Genetic Algorithms for Complex Hybrid Flexible Flow Line Problems},
journal = {International Journal of Metaheuristics},
year = 2010,
volume = 1,
number = 1,
pages = {30--54}
}
@article{UrlRuiStu2010ejor,
author = {Thijs Urlings and Rub{\'e}n Ruiz and Thomas St{\"u}tzle },
title = {Shifting Representation Search for Hybrid Flexible Flowline Problems},
journal = {European Journal of Operational Research},
year = 2010,
volume = 207,
number = 2,
pages = {1086--1095},
doi = {10.1016/j.ejor.2010.05.041}
}
@article{VaeAarLen1998cor,
author = { Rob J. M. Vaessens and Emile H. L. Aarts and Jan Karel Lenstra },
title = {A Local Search Template},
journal = {Computers \& Operations Research},
volume = 25,
number = 11,
year = 1998,
pages = {969--979},
doi = {10.1016/S0305-0548(97)00093-2}
}
@article{ValMarWanDeb2021mipdom,
title = {Analyzing dominance move ({MIP}-{DoM}) indicator for
multi-and many-objective optimization},
author = {do Val Lopes, Claudio Lucio and Martins, Fl{\'a}vio
Vin{\'i}cius Cruzeiro and Wanner, Elizabeth F. and Kalyanmoy Deb },
journal = {IEEE Transactions on Evolutionary Computation},
year = 2021,
publisher = {IEEE}
}
@article{ValRui2010,
author = { Eva Vallada and Rub{\'e}n Ruiz },
title = {Genetic algorithms with path relinking for the minimum tardiness permutation flowshop problem},
journal = {Omega},
volume = 38,
number = {1--2},
pages = {57--67},
year = 2010,
doi = {10.1016/j.omega.2009.04.002}
}
@article{ValRuiFra2015,
author = { Eva Vallada and Rub{\'e}n Ruiz and Jose M. Frami{\~n}{\'a}n },
title = {New hard benchmark for flowshop scheduling problems minimising makespan },
journal = {European Journal of Operational Research},
volume = 240,
number = 3,
pages = {666--677},
year = 2015,
doi = {10.1016/j.ejor.2014.07.033}
}
@article{ValRuiMin08,
author = { Eva Vallada and Rub{\'e}n Ruiz and Gerardo Minella },
title = {Minimising total tardiness in the m-machine flowshop
problem: A review and evaluation of heuristics and
metaheuristics},
journal = {Computers \& Operations Research},
volume = 35,
number = 4,
pages = {1350--1373},
year = 2008
}
@article{VanMou2014:ejor,
author = { Pieter Vansteenwegen and Manuel Mateo},
title = {An Iterated Local Search Algorithm for the Single-vehicle Cyclic Inventory
Routing Problem},
journal = {European Journal of Operational Research},
year = 2014,
volume = 237,
number = 3,
pages = {802--813}
}
@article{VanSouBer2009:cor,
author = { Pieter Vansteenwegen and Wouter Souffriau and Vanden Berghe, Greet and Dirk Van Oudheusden },
title = {Iterated Local Search for the Team Orienteering Problem with Time
Tindows},
journal = {Computers \& Operations Research},
year = 2009,
volume = 36,
number = 12,
pages = {3281--3290}
}
@article{Vanschoren2014openml,
author = { Joaquin Vanschoren and van Rijn, Jan N. and Bernd Bischl and Luis Torgo},
title = {{OpenML}: Networked Science in Machine Learning},
journal = {{ACM} {SIGKDD} Explorations Newsletter},
year = 2014,
volume = 15,
number = 2,
pages = {49--60},
month = jun,
doi = {10.1145/2641190.2641198}
}
@article{VarDel2000effsize,
author = {Vargha, A. and Delaney, H. D.},
title = {A critique and improvement of the {CL} common language effect
size statistics of {McGraw} and {Wong}},
journal = {Journal of Educational and Behavioral Statistics},
year = 2000,
volume = 25,
number = 2,
pages = {101--132},
keywords = {effect size test, A12 test}
}
@article{VarRaj05,
author = { T. K. Varadharajan and C. Rajendran},
title = {A multi-objective simulated-annealing algorithm for
scheduling in flowshops to minimize the makespan and
total flowtime of jobs},
journal = {European Journal of Operational Research},
volume = 167,
number = 3,
pages = {772--795},
year = 2005
}
@article{VasPas2006preliminary,
author = {Vasile, Massimiliano and De Pascale, Paolo},
title = {Preliminary design of multiple gravity-assist trajectories},
journal = {Journal of Spacecraft and Rockets},
year = 2006,
volume = 43,
number = 4,
pages = {794--805},
doi = {10.2514/1.17413}
}
@article{VasShaPar2017transformer,
title = {Attention Is All You Need},
author = {Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob
Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz
Kaiser and Illia Polosukhin},
journal = {Arxiv preprint arXiv:1706.03762},
year = 2017,
url = {http://arxiv.org/abs/1706.03762},
abstract = {The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.}
}
@article{VasSim2010jwrpm,
author = {A. Vasan and Slobodan P. Simonovic },
title = {Optimization of Water Distribution Network Design
Using Differential Evolution},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
year = 2010,
volume = 136,
number = 2,
pages = {279--287}
}
@article{VasYan2005effic,
title = {Efficiently computing succinct trade-off curves},
author = {Vassilvitskii, Sergei and Mihalis Yannakakis },
journal = {Theoretical Computer Science},
year = 2005,
number = {2-3},
pages = {334--356},
volume = 348,
publisher = {Elsevier}
}
@article{VazOch2010,
author = { J. A. V{\'a}zquez-Rodr{\'i}guez and Gabriela Ochoa },
title = {On the Automatic Discovery of Variants of the {NEH} Procedure
for Flow Shop Scheduling Using Genetic Programming},
journal = {Journal of the Operational Research Society},
year = 2010,
volume = 62,
number = 2,
pages = {381--396}
}
@article{VazPaqFonKla2015rep,
author = {Daniel Vaz and Lu{\'i}s Paquete and Carlos M. Fonseca and Kathrin Klamroth and Michael Stiglmayr},
title = {Representation of the non-dominated set in biobjective
discrete optimization},
journal = {Computers \& Operations Research},
volume = 63,
pages = {172--186},
year = 2015,
issn = {0305-0548},
doi = {10.1016/j.cor.2015.05.003}
}
@article{VelLam2000ec,
author = { David A. {Van Veldhuizen} and Gary B. Lamont },
title = {Multiobjective Evolutionary Algorithms: {Analyzing}
the State-of-the-art},
journal = {Evolutionary Computation},
year = 2000,
volume = 8,
number = 2,
pages = {125--147},
doi = {10.1162/106365600568158}
}
@article{VerLew2020penalty,
author = {Amit Verma and Mark Lewis},
title = {Penalty and partitioning techniques to improve performance of
{QUBO} solvers},
journal = {Discrete Optimization},
pages = 100594,
year = 2020,
issn = {1572-5286},
doi = {10.1016/j.disopt.2020.100594},
keywords = {Quadratic Unconstrained Binary Optimization, Nonlinear
optimization, Pseudo-Boolean optimization, Equality
constraint, Inequality constraint}
}
@article{VerLieJou2013ejor,
title = {On the Structure of Multiobjective Combinatorial
Search Space: {MNK}-landscapes with Correlated
Objectives},
author = { Verel, S{\'e}bastien and Arnaud Liefooghe and Laetitia Jourdan and Dhaenens, Clarisse },
journal = {European Journal of Operational Research},
volume = 227,
number = 2,
year = 2013,
pages = {331--342},
doi = {10.1016/j.ejor.2012.12.019}
}
@article{ViaFalPu2006pref,
title = {Preference-based Search using Example-Critiquing with
Suggestions},
author = {Viappiani, Paolo and Faltings, Boi and Pu, Pearl},
journal = {Journal of Artificial Intelligence Research},
volume = 27,
pages = {465--503},
year = 2006
}
@article{ViaPuFal2008pref,
title = {Preference-based Search with Adaptive Recommendations},
author = {Viappiani, Paolo and Pu, Pearl and Faltings, Boi},
journal = {AI Communications},
volume = 21,
number = 2,
pages = {155--175},
year = 2008
}
@article{VidCraGenPri2013ejor,
author = { Thibaut Vidal and Teodor Gabriel Crainic and Michel Gendreau and Christian Prins },
title = {Heuristics for Multi-attribute Vehicle Routing Problems: A Survey and Synthesis},
journal = {European Journal of Operational Research},
year = 2013,
volume = 231,
number = 1,
pages = {1--21}
}
@article{VidCraGenPri2014ejor,
author = { Thibaut Vidal and Teodor Gabriel Crainic and Michel Gendreau and Christian Prins },
title = {A Unified Solution Framework for Multi-attribute Vehicle Routing Problems},
journal = {European Journal of Operational Research},
year = 2014,
volume = 234,
number = 3,
pages = {658--673}
}
@article{VilSanMer2021optimal,
title = {Optimal decision trees for the algorithm selection problem:
integer programming based approaches},
author = {Vilas Boas, Matheus Guedes and Santos, Haroldo Gambini and Merschmann, Luiz Henrique de Campos and Vanden Berghe, Greet },
journal = {International Transactions in Operational Research},
volume = 28,
number = 5,
pages = {2759--2781},
year = 2021,
doi = {10.1111/itor.12724},
publisher = {Wiley Online Library}
}
@article{VouTsa99:ejor,
author = { Christos Voudouris and Edward P. K. Tsang },
title = {Guided Local Search and its Application to the
Travelling Salesman Problem},
journal = {European Journal of Operational Research},
year = 1999,
volume = 113,
number = 2,
pages = {469--499}
}
@article{Wal1975ms,
title = {Comparative Evaluation of Some Interactive Approaches to Multicriterion Optimization},
author = { Wallenius, Jyrki },
journal = {Management Science},
volume = 21,
number = 12,
pages = {1387--1396},
year = 1975
}
@article{WalCro2000:siam-jsc,
author = {C. Walshaw and M. Cross},
title = {Mesh Partitioning: {A} Multilevel Balancing and
Refinement Algorithm},
journal = {SIAM Journal on Scientific Computing},
year = 2000,
volume = 22,
number = 1,
pages = {63--80},
doi = {10.1137/S1064827598337373}
}
@article{WalEveFie2012visualizing,
title = {Visualizing mutually nondominating solution sets in
many-objective optimization},
author = {Walker, David J. and Everson, Richard M. and Jonathan E. Fieldsend },
journal = {IEEE Transactions on Evolutionary Computation},
volume = 17,
number = 2,
pages = {165--184},
year = 2012
}
@article{WanChePro1997,
author = {Chengen Wang and Chengbin Chu and Jean-Marie Proth},
title = {Heuristic Approaches for {n/m/F/$\Sigma$Ci} Scheduling
Problems},
journal = {European Journal of Operational Research},
volume = 96,
number = 3,
pages = {636--644},
year = 1997,
doi = {10.1016/0377-2217(95)00347-9}
}
@article{WanJiaYao2015twoarch2,
title = {{TwoArch2}: An improved two-archive algorithm for
many-objective optimization},
author = {Wang, Handing and Jiao, Licheng and Xin Yao },
journal = {IEEE Transactions on Evolutionary Computation},
year = 2015,
number = 4,
pages = {524--541},
volume = 19
}
@article{WanJinSchOlh2021transfer,
author = {Xilu Wang and Yaochu Jin and Sebastian Schmitt and
Markus Olhofer and Allmendinger, Richard },
title = {Transfer learning based surrogate assisted evolutionary
bi-objective optimization for objectives with different
evaluation times},
journal = {Knowledge-Based Systems},
year = 2021,
volume = 227,
pages = 107190,
doi = {10.1016/j.knosys.2021.107190}
}
@article{WanLuGlo2013cor,
author = {Wang, Yang and L{\"u}, Zhipeng and Fred Glover and Jin-Kao Hao },
title = {Probabilistic {GRASP}-Tabu Search algorithms for the {UBQP} problem},
journal = {Computers \& Operations Research},
volume = 40,
number = 12,
pages = {3100--3107},
year = 2013,
doi = {10.1016/j.cor.2011.12.006}
}
@article{WanLuGlov2013joh,
author = {Wang, Yang and L{\"u}, Zhipeng and Fred Glover and Jin-Kao Hao },
title = {Backbone Guided Tabu Search for Solving the {UBQP} Problem},
year = 2013,
journal = {Journal of Heuristics},
volume = 19,
number = 4,
doi = {10.1007/s10732-011-9164-4},
pages = {679--695}
}
@article{WanPurFle2013tec,
title = {Preference-Inspired Coevolutionary Algorithms for
Many-Objective Optimization},
author = {Wang, Rui and Robin C. Purshouse and Peter J. Fleming },
journal = {IEEE Transactions on Evolutionary Computation},
volume = 17,
number = 4,
pages = {474--494},
year = 2013
}
@article{WanXioHeGao2020multi,
author = {Wang, Rui and Xiong, Jian and He, Min-fan and Gao, Liang and
Wang, Ling},
title = {Multi-objective optimal design of hybrid renewable energy
system under multiple scenarios},
journal = {Renewable Energy},
volume = 151,
pages = {226--237},
year = 2020,
publisher = {Elsevier},
doi = {10.1016/j.renene.2019.11.015}
}
@article{WangEtAl2012ejor,
author = {Wang, Yang and L{\"u}, Zhipeng and Fred Glover and Jin-Kao Hao },
title = {Path relinking for unconstrained binary quadratic
programming},
journal = {European Journal of Operational Research},
year = 2012,
volume = 223,
number = 3,
pages = {595--604},
publisher = {Elsevier},
doi = {10.1016/j.ejor.2012.07.012}
}
@article{WatBarWhiHow02,
author = { Jean-Paul Watson and L. Barbulescu and Darrell Whitley and Adele E. Howe },
title = {Contrasting Structured and Random Permutation
Flow-Shop Scheduling Problems: Search Space Topology
and Algorithm Performance},
journal = {INFORMS Journal on Computing},
year = 2002,
volume = 14,
number = 2,
pages = {98--123}
}
@article{WatBecHowWhi03,
author = { Jean-Paul Watson and J. C. Beck and A. E. Howe and Darrell Whitley },
title = {Problem Difficulty for Tabu Search in Job-Shop
Scheduling},
journal = {Artificial Intelligence},
year = 2003,
volume = 143,
number = 2,
pages = {189--217}
}
@article{WatHowWhi2006,
title = {Deconstructing {Nowicki} and {Smutnicki}'s {i-TSAB} tabu search
algorithm for the job-shop scheduling problem},
author = { Jean-Paul Watson and Howe, Adele E and Darrell Whitley },
journal = {Computers \& Operations Research},
volume = 33,
number = 9,
pages = {2623--2644},
year = 2006,
publisher = {Elsevier}
}
@article{WatKas2017ems,
title = {Incorporating deeply uncertain factors into the many
objective search process},
author = {Watson, Abigail A. and Kasprzyk, Joseph R. },
journal = {Environmental Modelling \& Software},
volume = 89,
pages = {159--171},
year = 2017,
keywords = {scenario-based}
}
@article{Wegman1990pc,
title = {Hyperdimensional data analysis using parallel coordinates},
author = {Wegman, Edward J.},
journal = {Journal of the American Statistical Association},
volume = 85,
number = 411,
pages = {664--675},
year = 1990
}
@article{Weinberger1991,
author = {Weinberger, Edward D.},
title = {Local properties of {Kauffman}'s {N-k} model: A tunably
rugged energy landscape},
journal = {Physical Review A},
year = 1991,
volume = 44,
number = 10,
pages = 6399,
publisher = {APS}
}
@article{WeiKhoWan2016jbd,
title = {A survey of transfer learning},
author = {Weiss, Karl and Khoshgoftaar, Taghi M. and Wang, DingDing},
journal = {Journal of Big Data},
volume = 3,
number = 1,
pages = {1--40},
year = 2016,
publisher = {SpringerOpen}
}
@article{Welch1938significance,
title = {The significance of the difference between two means when the
population variances are unequal},
author = {Welch, Bernard L.},
journal = {Biometrika},
volume = 29,
number = {3/4},
pages = {350--362},
year = 1938
}
@article{WesLop2018ecj,
title = {Latin Hypercube Designs with Branching and Nested Factors for
Initialization of Automatic Algorithm Configuration},
author = { Simon Wessing and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
doi = {10.1162/evco_a_00241},
journal = {Evolutionary Computation},
year = 2018,
volume = 27,
number = 1,
pages = {129--145}
}
@article{Weyland2010,
title = {A Rigorous Analysis of the Harmony Search Algorithm: How the Research Community can be misled by a ``novel'' Methodology},
author = { Dennis Weyland },
journal = {International Journal of Applied Metaheuristic Computing},
volume = 12,
number = 2,
pages = {50--60},
year = 2010
}
@article{Weyland2015,
title = {A critical analysis of the harmony search algorithm: How not
to solve {Sudoku}},
author = { Dennis Weyland },
journal = {Operations Research Perspectives},
volume = 2,
pages = {97--105},
year = 2015
}
@article{WhiArcCla2011:tec,
author = {D. R. White and A. Arcuri and J. A. Clark},
title = {Evolutionary Improvement of Programs},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2011,
volume = 15,
number = 4,
pages = {515--538}
}
@article{WhiBraBar2012tec,
author = {While, L. and Bradstreet, L. and Barone, L.},
journal = {IEEE Transactions on Evolutionary Computation},
title = {A Fast Way of Calculating Exact Hypervolumes},
year = 2012,
volume = 16,
number = 1,
pages = {86--95}
}
@article{WhiRanDzuMat96,
author = { Darrell Whitley and Soraya Rana and John Dzubera and Keith E. Mathias},
title = {Evaluating Evolutionary Algorithms},
journal = {Artificial Intelligence},
year = 1996,
volume = 85,
pages = {245--296}
}
@article{Wil92,
author = {R. J. Williams},
title = {Simple Statistical Gradient-Following Algorithms for
Connectionist Reinforcement Learning},
journal = {Machine Learning},
year = 1992,
pages = {229--256},
volume = 8,
number = 3
}
@article{Winkler1985order,
author = {P. Winkler},
title = {Random Orders},
journal = {Order},
year = 1985,
volume = 1,
pages = {317--331},
annote = {Showed that fraction of Pareto-optimal increases with number
of objectives}
}
@article{Witt2012,
author = { Carsten Witt },
title = {Analysis of an Iterated Local Search Algorithm for Vertex Cover in Sparse Random Graphs},
journal = {Theoretical Computer Science},
year = 2012,
volume = 425,
pages = {117--125}
}
@article{WolMac97:ieee-tec,
author = {D. H. Wolpert and W. G. Macready},
title = {No Free Lunch Theorems for Optimization},
journal = {IEEE Transactions on Evolutionary Computation},
year = 1997,
volume = 1,
number = 1,
pages = {67--82},
doi = {10.1109/4235.585893}
}
@article{WooReeSim2013many,
title = {Many objective visual analytics: rethinking the design of
complex engineered systems},
author = { Matthew J. Woodruff and Patrick M. Reed and Simpson, Timothy W.},
journal = {Structural and Multidisciplinary Optimization},
volume = 48,
number = 1,
pages = {201--219},
year = 2013,
doi = {10.1007/s00158-013-0891-z},
publisher = {Springer}
}
@article{WooRitOpp2011ijmh,
author = { David L. Woodruff and Ulrike Ritzinger and Johan
Oppen},
title = {Research Note: The Point of Diminishing Returns in
Heuristic Search},
journal = {International Journal of Metaheuristics},
year = 2011,
volume = 1,
number = 3,
pages = {222--231},
doi = {10.1504/IJMHeur.2011.041195},
keywords = {anytime}
}
@article{WooYim98,
author = { H. S. Woo and D. S. Yim },
title = {A Heuristic Algorithm for Mean Flowtime Objective in
Flowshop Scheduling},
journal = {Computers \& Operations Research},
volume = 25,
number = 3,
pages = {175--182},
year = 1998
}
@article{WuSchCheLeNorMacKriCaoGaoMac2016gnmt,
title = {Google's neural machine translation system: Bridging the gap
between human and machine translation},
author = {Wu, Yonghui and Schuster, Mike and Chen, Zhifeng and Le, Quoc
V and Norouzi, Mohammad and Macherey, Wolfgang and Krikun,
Maxim and Cao, Yuan and Gao, Qin and Macherey, Klaus and
others},
journal = {Arxiv preprint arXiv:1609.08144 [cs.CL]},
url = {https://arxiv.org/abs/1609.08144},
year = 2016
}
@article{WuZhuWeDin2014mining,
title = {Data mining with big data},
author = {Wu, Xindong and Zhu, Xingquan and Wu, Gong-Qing and Ding,
Wei},
journal = {IEEE Transactions on Knowledge and Data Engineering},
volume = 26,
number = 1,
pages = {97--107},
year = 2014,
publisher = {IEEE}
}
@article{XavXav2011mssc,
title = {Solving the minimum sum-of-squares clustering problem by
hyperbolic smoothing and partition into boundary and
gravitational regions},
journal = {Pattern Recognition},
volume = 44,
number = 1,
pages = {70--77},
year = 2011,
issn = {0031-3203},
doi = {10.1016/j.patcog.2010.07.004},
author = {Xavier, Adilson Elias and Xavier, Vinicius Layter },
keywords = {Cluster analysis, Min-sum-min problems, Nondifferentiable
programming, Smoothing}
}
@article{XinCheChe2018review,
author = {Xin, B. and Chen, L. and Chen, J. and Ishibuchi, Hisao and Hirota, K. and Liu, B.},
journal = {{IEEE} Access},
title = {Interactive Multiobjective Optimization: A Review of the
State-of-the-Art},
year = 2018,
volume = 6,
pages = {41256--41279},
doi = {10.1109/ACCESS.2018.2856832},
keywords = {Decision making, Evolutionary computation, Pareto
optimization, Evolutionary multiobjective optimization,
interactive multiobjective optimization, multiple criteria
decision making, preference information, preference models},
abstract = {Interactive multiobjective optimization (IMO) aims at finding
the most preferred solution of a decision maker with the
guidance of his/her preferences which are provided
progressively. During the process, the decision maker can
adjust his/her preferences and explore only interested
regions of the search space. In recent decades, IMO has
gradually become a common interest of two distinct
communities, namely, the multiple criteria decision making
(MCDM) and the evolutionary multiobjective optimization
(EMO). The IMO methods developed by the MCDM community
usually use the mathematical programming methodology to
search for a single preferred Pareto optimal solution, while
those which are rooted in EMO often employ evolutionary
algorithms to generate a representative set of solutions in
the decision maker's preferred region. This paper aims to
give a review of IMO research from both MCDM and EMO
perspectives. Taking into account four classification
criteria including the interaction pattern, preference
information, preference model, and search engine (i.e.,
optimization algorithm), a taxonomy is established to
identify important IMO factors and differentiate various IMO
methods. According to the taxonomy, state-of-the-art IMO
methods are categorized and reviewed and the design ideas
behind them are summarized. A collection of important issues,
e.g., the burdens, cognitive biases and preference
inconsistency of decision makers, and the performance
measures and metrics for evaluating IMO methods, are
highlighted and discussed. Several promising directions
worthy of future research are also presented.}
}
@article{XuChiGlo98,
author = {Jiefeng Xu and Steve Y. Chiu and Fred Glover },
title = {Fine-tuning a tabu search algorithm with statistical tests},
journal = {International Transactions in Operational Research},
volume = 5,
number = 3,
pages = {233--244},
year = 1998,
doi = {10.1111/j.1475-3995.1998.tb00117.x}
}
@article{XuHutHooLey2008jair,
author = { Lin Xu and Frank Hutter and Holger H. Hoos and Kevin Leyton-Brown },
title = {{SATzilla:} Portfolio-based Algorithm Selection for {SAT}},
journal = {Journal of Artificial Intelligence Research},
year = 2008,
volume = 32,
pages = {565--606},
month = jun,
epub = {http://www.cs.ubc.ca/~hutter/papers/SATzilla.pdf},
doi = {10.1613/jair.2490}
}
@article{XuLuChe2013,
author = {Hongyun Xu and Zhipeng L{\"u} and T. C. E. Cheng},
title = {Iterated Local Search for Single-machine Scheduling with Sequence-dependent Setup Times to Minimize Total Weighted Tardiness},
journal = {Journal of Scheduling},
year = 2014,
volume = 17,
number = 3,
pages = {271--287}
}
@article{XuYan2003ids,
author = { Dong-Ling Xu and Yang, Jian-Bo },
title = {Intelligent Decision System for Self-Assessment},
journal = {Journal of Multi-Criteria Decision Analysis},
year = 2003,
volume = 12,
number = 1,
pages = {43--60},
doi = {10.1002/mcda.343},
abstract = {Many small and medium enterprises (SMEs) in the UK use the
beta (Business Excellence Through Action) approach to the
EFQM Excellence Model to conduct business excellence
self-assessment, which is in essence a multiple criteria
decision analysis (MCDA) problem. This paper introduces a
decision support software package called Intelligent Decision
System (IDS) to implement the beta approach. It is
demonstrated in the paper that the IDS-beta package can
provide not only average scores but also the following
numerical results and graphical displays on: Distributed
assessment results to demonstrate the diversity of company
performances The performance range to cater for incomplete
assessment information Comparisons between current
performances and past performances, among different companies
among different action plans. Strengths and weaknesses The
IDS-beta package also provides a structured knowledge base to
help assessors to make judgements more objectively. The
knowledge base contains guidelines provided by the developers
of the beta approach, best practices gathered from research
on award winning organizations, evidence collected from
companies being assessed and comments provided by assessors
to record the reasons why a specific criterion is assessed to
a certain grade for a company. Four small UK companies, the
industry partners of the research project, have carried out
the preliminary self-assessment using the package. The
results and experience of the application are discussed at
the end of the paper.},
keywords = {decision support system, business excellence, MCDA, quality
award, self-assessment, the evidential reasoning approach}
}
@article{YagKisIba2006,
author = { Mutsunori Yagiura and M. Kishida and Toshihide Ibaraki },
title = {A 3-Flip Neighborhood Local Search for the Set Covering Problem},
journal = {European Journal of Operational Research},
year = 2006,
volume = 172,
number = 2,
pages = {472--499}
}
@article{Yam2018parking,
author = {Yuki Yamada},
title = {How to Crack Pre-registration: Toward Transparent and Open
Science},
doi = {10.3389/fpsyg.2018.01831},
year = 2018,
month = sep,
publisher = {Frontiers Media {SA}},
volume = 9,
journal = {Frontiers in Psychology},
keywords = {HARKing; PARKing}
}
@article{YanEmmDeu2019bayesian,
author = {Kaifeng Yang and Emmerich, Michael T. M. and Andr{\'{e}} H. Deutz and Thomas B{\"a}ck },
journal = {Swarm and Evolutionary Computation},
title = {Multi-Objective {Bayesian} Global Optimization using Expected
Hypervolume Improvement Gradient},
year = 2019,
month = feb,
pages = {945--956},
volume = 44,
doi = {10.1016/j.swevo.2018.10.007},
keywords = {Bayesian Optimisation with preferences},
publisher = {Elsevier {BV}}
}
@article{YanKrePin00:js,
author = {Y. Yang and S. Kreipl and M. L. Pinedo},
title = {Heuristics for Minimizing Total Weighted Tardiness in Flexible Flow Shops},
journal = {Journal of Scheduling},
year = 2000,
volume = 3,
number = 2,
pages = {89--108}
}
@article{YanLiLiuZhe2013,
author = {S. Yang and Li, Miqing and X. Liu and J. Zheng},
journal = {IEEE Transactions on Evolutionary Computation},
title = {A Grid-Based Evolutionary Algorithm for Many-Objective
Optimization},
year = 2013,
volume = 17,
number = 5,
pages = {721--736},
doi = {10.1109/TEVC.2012.2227145},
annote = {epsilon-grid}
}
@article{YeDoeWang2022ac,
author = {Furong Ye and Carola Doerr and Wang, Hao and Thomas B{\"a}ck },
title = {Automated Configuration of Genetic Algorithms by Tuning for
Anytime Performance},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2022,
volume = 26,
number = 6,
pages = {1526--1538},
doi = {10.1109/TEVC.2022.3159087}
}
@article{YuLin2015,
author = {Vincent F. Yu and Shih-Wei Lin},
title = {Iterated Greedy Heuristic for the Time-dependent
Prize-collecting Arc Routing Problem},
journal = {Computers and Industrial Engineering},
year = 2015,
volume = 90,
pages = {54--66}
}
@article{YuPowSte94,
author = { G. Yu and R. S. Powell and M. J. H. Sterling },
title = {Optimized Pump Scheduling in Water Distribution
Systems},
journal = {Journal of Optimization Theory and Applications},
year = 1994,
volume = 83,
number = 3,
pages = {463--488}
}
@article{YuaMonStuBir12:si,
author = { Zhi Yuan and Marco A. {Montes de Oca} and Thomas St{\"u}tzle and Mauro Birattari },
title = {Continuous Optimization Algorithms for Tuning Real and
Integer Algorithm Parameters of Swarm Intelligence Algorithms},
journal = {Swarm Intelligence},
year = 2012,
volume = 6,
number = 1,
pages = {49--75}
}
@article{ZenYan2009cor,
author = {Zeng, Q. and Yang, Z.},
title = {Integrating Simulation and Optimization to Schedule
Loading Operations in Container Terminals},
journal = {Computers \& Operations Research},
year = 2009,
volume = 36,
number = 6,
pages = {1935--1944},
doi = {10.1016/j.cor.2008.06.010}
}
@article{ZhaGeoAna2016,
author = {Tiantian Zhang and Michael Georgiopoulos and Georgios C. Anagnostopoulos},
title = {Multi-Objective Model Selection via Racing},
journal = {IEEE Transactions on Cybernetics},
year = 2016,
volume = 46,
number = 8,
pages = {1863--1876}
}
@article{ZhaLi07:moead,
author = { Zhang, Qingfu and Hui Li},
title = {{MOEA/D}: A Multiobjective Evolutionary Algorithm Based on
Decomposition},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2007,
volume = 11,
number = 6,
pages = {712--731},
doi = {10.1109/TEVC.2007.892759},
annote = {Introduces penalty-based boundary intersection (PBI)
function}
}
@article{ZhaSan2009jade,
title = {{JADE}: Adaptive differential evolution with optional
external archive},
author = {Zhang, Jingqiao and Sanderson, Arthur C.},
journal = {IEEE Transactions on Evolutionary Computation},
volume = 13,
number = 5,
pages = {945--958},
year = 2009,
doi = {10.1109/TEVC.2009.2014613}
}
@article{ZhaSud2004dts,
author = {Zhao, H. and Sudha Ram},
journal = {IEEE Transactions on Knowledge and Data Engineering},
title = {Constrained cascade generalization of decision trees},
year = 2004,
volume = 16,
number = 6,
pages = {727--739},
doi = {10.1109/TKDE.2004.3}
}
@article{ZheCha2012berth,
title = {A bi-objective model for robust berth allocation scheduling},
author = {Zhen, Lu and Chang, Dao-Fang},
journal = {Computers and Industrial Engineering},
volume = 63,
number = 1,
pages = {262--273},
year = 2012
}
@article{ZhoZhaJin2009igdx,
author = {Zhou, A. and Zhang, Qingfu and Yaochu Jin },
title = {Approximating the set of {Pareto}-optimal solutions in both
the decision and objective spaces by an estimation of
distribution algorithm},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2009,
volume = 13,
number = 5,
pages = {1167--1189},
doi = {10.1109/TEVC.2009.2021467},
keywords = {multi-modal, IGDX}
}
@article{Zilberstein96:anytime,
author = { Shlomo Zilberstein },
title = {Using Anytime Algorithms in Intelligent Systems},
journal = {{AI} Magazine},
year = 1996,
volume = 17,
number = 3,
pages = {73--83},
epub = {http://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1232},
keywords = {performance profiles},
doi = {10.1609/aimag.v17i3.1232},
abstract = {Anytime algorithms give intelligent systems the capability to trade deliberation time for quality of results. This capability is essential for successful operation in domains such as signal interpretation, real-time diagnosis and repair, and mobile robot control. What characterizes these domains is that it is not feasible (computationally) or desirable (economically) to compute the optimal answer. This article surveys the main control problems that arise when a system is composed of several anytime algorithms. These problems relate to optimal management of uncertainty and precision. After a brief introduction to anytime computation, I outline a wide range of existing solutions to the metalevel control problem and describe current work that is aimed at increasing the applicability of anytime computation.}
}
@article{ZioWal1983interactive,
title = {An interactive multiple objective linear programming method
for a class of underlying nonlinear utility functions},
author = {Zionts, Stanley and Wallenius, Jyrki },
journal = {Management Science},
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}
}
@inproceedings{AbdKriCha1997,
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}
}
@book{AhoHopUll83:data-structures,
author = { A. Aho and J. Hopcroft and J. Ullman },
title = {Data structures and algorithms},
year = 1983,
publisher = {Addison-Wesley},
address = { Reading, MA}
}
@inproceedings{CheHuhHul2009dt,
address = { New York, NY},
publisher = {ACM Press},
editor = {Andrea Pohoreckyj Danyluk and L{\'{e}}on Bottou and Michael
L. Littman},
year = 2009,
booktitle = {Proceedings of the 26th International Conference on Machine Learning, {ICML} 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,
year = 2006,
volume = 4193,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
editor = {Runarsson, Thomas Philip and Hans-Georg Beyer and Edmund K. Burke and Juan-Juli{\'a}n Merelo and Darrell Whitley and Xin Yao },
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {IX}},
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 Dr{\'e}o },
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, Germany},
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},
year = 2013,
editor = { Christian Blum and Alba, Enrique },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2013},
author = {Basseur, Matthieu and Go{\"e}ffon, Adrien and Arnaud Liefooghe and Verel, S{\'e}bastien },
title = {On Set-based Local Search for Multiobjective Combinatorial
Optimization},
pages = {471--478},
doi = {10.1145/2463372.2463430},
acmid = 2463430
}
@incollection{BasYevDeuEmm2017,
booktitle = {{EVOLVE} - A Bridge between Probability, Set Oriented
Numerics, and Evolutionary Computation {VII}},
address = { Cham, Switzerland},
series = {Studies in Computational Intelligence},
volume = 662,
year = 2017,
publisher = {Springer},
author = {Basto-Fernandes, Vitor and Yevseyeva, Iryna and Andr{\'{e}} Deutz and Emmerich, Michael T. M. },
title = {A survey of diversity oriented optimization: Problems,
indicators, and algorithms},
pages = {3--23},
doi = {10.1007/978-3-319-49325-1_1}
}
@book{BatBruMas08:book,
author = { Roberto Battiti and M. Brunato and Franco Mascia },
title = {Reactive Search and Intelligent Optimization},
publisher = {Springer},
address = { New York, NY},
year = 2008,
series = {Operations Research/Computer Science Interfaces},
volume = 45,
doi = {10.1007/978-0-387-09624-7}
}
@inproceedings{BatCam2009reactive,
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 = {Reactive search optimization: Learning while optimizing. An
experiment in interactive multi-objective optimization},
author = { Roberto Battiti and Paolo Campigotto }
}
@inproceedings{BatSchUrl2014patat,
publisher = {PATAT},
year = 2014,
editor = { Ender {\"O}zcan and Edmund K. Burke and Barry McCollum},
booktitle = {PATAT 2014: Proceedings of the 10th International Conference of the Practice and Theory of Automated Timetabling},
author = {Michele Battistutta and Andrea Schaerf and Tommaso Urli },
title = {Feature-based tuning of single-stage simulated annealing for examination timetabling},
pages = {53--61},
keywords = {F-race}
}
@unpublished{Bau1986a,
author = {E. B. Baum},
title = {Iterated Descent: A Better Algorithm for Local
Search in Combinatorial Optimization Problems},
note = {Manuscript},
year = 1986
}
@inproceedings{Bau1986b,
author = {E. B. Baum},
title = {Towards Practical ``Neural'' Computation for Combinatorial
Optimization Problems},
booktitle = {Neural Networks for Computing, AIP Conference Proceedings},
year = 1986,
pages = {53--64}
}
@inproceedings{BayDerSab2005cie,
author = {A. Baykasoglu and T. Dereli and I. Sabuncu},
title = {A multiple objective ant colony optimization
approach to assembly line balancing problems},
booktitle = {35th International Conference on Computers and
Industrial Engineering (CIE35)},
pages = {263--268},
year = 2005,
address = {Istanbul, Turkey}
}
@inproceedings{BeaGra2002,
title = {Improved Distributed Hypercube Sampling},
author = {Beachkofski, Brian and Grandhi, Ramana},
booktitle = {Proceedings of the 43rd AIAA/ASME/ASCE/AHS/ASC Structures,
Structural Dynamics, and Materials Conference},
year = 2002,
organization = {AIAA paper 2002-1274},
publisher = {American Institute of Aeronautics and Astronautics}
}
@techreport{Beasley1998,
title = {Heuristic algorithms for the unconstrained binary quadratic
programming problem},
author = { John E. Beasley },
address = {London, England},
year = 1998,
institution = {The Management School, Imperial College},
epub = {http://people.brunel.ac.uk/~mastjjb/jeb/bqp.pdf}
}
@incollection{BecGotStu05,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 3871,
editor = { Talbi, El-Ghazali and Pierre Liardet and Pierre
Collet and Evelyne Lutton and Marc Schoenauer},
year = 2005,
booktitle = {Artificial Evolution},
author = {S. Becker and J. Gottlieb and Thomas St{\"u}tzle },
title = {Applications of Racing Algorithms: An Industrial
Perspective},
pages = {271--283}
}
@book{BedBai1982,
title = {Integrated Production Control Systems: Management, Analysis,
Design},
author = {Bedworth, David D. and Bailey, James E.},
volume = 2,
year = 1982,
publisher = {John Wiley \& Sons},
address = { New York, NY}
}
@incollection{BehAffWag2017,
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},
title = {Instance-based algorithm selection on quadratic assignment problem landscapes},
author = {Beham, Andreas and Affenzeller, Michael and Wagner, Stefan},
pages = {1471--1478}
}
@incollection{BelBraEsk2088moointevo,
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 = {Belton, Valerie and J{\"u}rgen Branke and Eskelinen,
Petri and Salvatore Greco and Molina, Juli{\'a}n and Francisco Ruiz and Roman S{\l}owi{\'n}ski },
title = {Interactive Multiobjective Optimization from a
Learning Perspective},
pages = {405--433},
doi = {10.1007/978-3-540-88908-3_15}
}
@incollection{BelDreSavSch2016:ppsn,
isbn = {978-3-319-45822-9},
doi = {10.1007/978-3-319-45823-6},
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 = {Nacim Belkhir and Johann Dr{\'e}o and Pierre Sav{\'{e}}ant and Marc Schoenauer },
title = {Feature Based Algorithm Configuration: A Case Study with Differential Evolution},
pages = {156--166}
}
@incollection{BelDreSavSch2017: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 = {Nacim Belkhir and Johann Dr{\'e}o and Pierre Sav{\'{e}}ant and Marc Schoenauer },
title = {Per Instance Algorithm Configuration of {CMA-ES} with Limited Budget},
pages = {681--688}
}
@inproceedings{Ben90,
author = { Jon Louis Bentley },
title = {Experiments on Traveling Salesman Heuristics},
booktitle = {Proceedings of the First Annual ACM-SIAM Symposium on
Discrete Algorithms},
year = 1990,
editor = {David S. Johnson},
pages = {91--99},
publisher = {Society for Industrial and Applied Mathematics, Philadelphia, PA, USA}
}
@inproceedings{BenCasLus2020interactive,
publisher = {{AAAI} Press},
isbn = {978-1-57735-823-7},
year = 2020,
booktitle = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
key = {AAAI2020},
author = {Nawal Benabbou and Cassandre Leroy and Thibaut Lust },
title = {An Interactive Regret-Based Genetic Algorithm for Solving
Multi-Objective Combinatorial Optimization Problems},
pages = {2335--2342},
doi = {10.1609/aaai.v34i03.5612},
keywords = {interactive, multi-objective, decision-makers}
}
@inproceedings{BenCorManZaf2015,
epub = {http://jmlr.org/proceedings/papers/v37/},
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 = {Alessio Benavoli and Giorgio Corani and Francesca Mangili and
Marco Zaffalon},
title = {A {Bayesian} nonparametric procedure for comparing
algorithms},
pages = {1264--1272},
keywords = {racing}
}
@incollection{BenRit2015:icaps,
year = 2015,
address = { Menlo Park, CA},
publisher = {{AAAI} Press},
booktitle = {Proceedings of the Twenty-Fifth International Conference on
Automated Planning and Scheduling, {ICAPS} 2015},
editor = { Ronen I. Brafman and Carmel Domshlak and Patrik Haslum and Shlomo Zilberstein },
author = {Alexander Javier Benavides and Marcus Ritt},
title = {Iterated Local Search Heuristics for Minimizing Total
Completion Time in Permutation and Non-permutation Flow
Shops},
pages = {34--41},
keywords = {irace}
}
@incollection{BenRolBlu2010,
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 = {Stefano Benedettini and Andrea Roli and Christian Blum },
title = {A Randomized Iterated Greedy Algorithm for the Founder Sequence Reconstruction
Problem},
pages = {37--51}
}
@incollection{BenRolDig2008ants,
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 = {Stefano Benedettini and Andrea Roli and Luca {Di Gaspero}},
title = {Two-level {ACO} for Haplotype Inference under Pure Parsimony},
pages = {179--190}
}
@book{Ber95,
author = {D. Bertsekas},
title = {Dynamic Programming and Optimal Control},
year = 1995,
publisher = {Athena Scientific, Belmont, MA}
}
@book{Ber98,
author = {D. Bertsekas},
title = {Network Optimization: Continuous and Discrete Models},
year = 1998,
publisher = {Athena Scientific, Belmont, MA}
}
@incollection{BerBarBen2011nips,
publisher = {Curran Associates, Red Hook, NY},
year = 2011,
editor = {J. Shawe-Taylor and R. S. Zemel and P. L. Bartlett and
F. Pereira and K. Q. Weinberger},
booktitle = {Advances in Neural Information Processing Systems (NIPS 24)},
title = {Algorithms for Hyper-Parameter Optimization},
author = { James S. Bergstra and Bardenet, R\'{e}mi and Bengio, Yoshua and Bal\'{a}zs K\'{e}gl},
pages = {2546--2554},
url = {http://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf}
}
@book{BerCirHoeHoo2016dd4o,
author = {Bergman, David and Cire, Andr{\'e} A. and van Hoeve, Willem-Jan and John Hooker },
title = {Decision Diagrams for Optimization},
year = 2016,
publisher = {Springer},
address = { Cham, Switzerland},
isbn = {978-3-319-42849-9},
doi = {10.1007/978-3-319-42849-9}
}
@incollection{BerDorLanSerGam1996: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 = { Hughes Bersini and Marco Dorigo and S. Langerman and G. Seront and L. M. Gambardella },
title = {Results of the First International Contest on
Evolutionary Optimisation},
pages = {611--615}
}
@inproceedings{BerSchKra2016safe,
author = {Berkenkamp, Felix and Schoellig, Angela P. and Krause,
Andreas},
title = {Safe controller optimization for quadrotors with {Gaussian}
processes},
booktitle = {2016 IEEE International Conference on Robotics and Automation
(ICRA)},
year = 2016,
pages = {491--496},
organization = {IEEE},
doi = {10.1109/ICRA.2016.7487170},
keywords = {Safe Optimization, SafeOpt}
}
@inproceedings{BerYasCox2013,
url = {http://jmlr.org/proceedings/papers/v28/},
year = 2013,
volume = 28,
booktitle = {Proceedings of the 30th International Conference on Machine Learning, {ICML} 2013},
editor = {Dasgupta, Sanjoy and McAllester, David},
title = {Making a science of model search: Hyperparameter optimization
in hundreds of dimensions for vision architectures},
author = { James S. Bergstra and Yasmin, Daniel and Cox, David},
pages = {115--123}
}
@incollection{BesStuDor00:ppsn,
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 = { Matthijs L. {den Besten} and Thomas St{\"u}tzle and Marco Dorigo },
title = {Ant Colony Optimization for the Total Weighted
Tardiness Problem},
pages = {611--620}
}
@incollection{BesStuDor2001:evoworkshops,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 2037,
editor = {E. J. W. Boers and others},
aeditor = {E. J. W. Boers and J. Gottlieb and P. L. Lanzi and R. E. Smith
and S. Cagnoni and E. Hart and G. R. Raidl and H. Tijink},
year = 2001,
booktitle = {Applications of Evolutionary Computing,
Proceedings of EvoWorkshops 2001},
author = { Matthijs L. {den Besten} and Thomas St{\"u}tzle and Marco Dorigo },
title = {Design of Iterated Local Search Algorithms: An
Example Application to the Single Machine Total
Weighted Tardiness Problem},
pages = {441--452}
}
@inproceedings{BeuRud06:hypervolume,
author = { Nicola Beume and G{\"u}nther Rudolph },
title = {Faster {S}-Metric Calculation by Considering
Dominated Hypervolume as {Klee's} Measure Problem},
booktitle = {Proceedings of the Second IASTED Conference on
Computational Intelligence},
editor = {B. Kovalerchuk},
pages = {231--236},
publisher = {ACTA Press, Anaheim},
year = 2006
}
@incollection{BezLopStu2012:ants,
volume = 7461,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Marco Dorigo and others},
year = 2012,
booktitle = {Swarm Intelligence, 8th International Conference, ANTS 2012},
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Generation of Multi-Objective {ACO}
Algorithms for the Biobjective Knapsack},
pages = {37--48},
doi = {10.1007/978-3-642-32650-9_4},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2012-008/}
}
@misc{BezLopStu2012:ants-supp,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Generation of {MOACO} Algorithms for the Biobjective Bidimensional Knapsack Problem: Supplementary material},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2012-008/}},
year = 2012
}
@misc{BezLopStu2013:evocop-supp,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An Analysis of Local Search for the Bi-objective Bidimensional Knapsack: Supplementary material},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2012-016/}},
year = 2013
}
@misc{BezLopStu2013:lion-supp,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Deconstructing Multi-Objective Evolutionary Algorithms: An Iterative Analysis on the Permutation Flowshop: Supplementary material},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2013-010/}},
year = 2013
}
@incollection{BezLopStu2013evocop,
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 = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An Analysis of Local Search for the Bi-objective
Bidimensional Knapsack Problem},
pages = {85--96},
doi = {10.1007/978-3-642-37198-1_8}
}
@techreport{BezLopStu2014:automoeaTR,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Com\-ponent-Wise Design of Multi-Objective
Evolutionary Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2014,
number = {TR/IRIDIA/2014-012},
month = aug
}
@incollection{BezLopStu2014:lion,
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 = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Deconstructing Multi-Objective Evolutionary Algorithms: An
Iterative Analysis on the Permutation Flowshop},
pages = {57--172},
doi = {10.1007/978-3-319-09584-4_16},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2013-010/}
}
@incollection{BezLopStu2014: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 = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Design of Evolutionary Algorithms for
Multi-Objective Combinatorial Optimization},
doi = {10.1007/978-3-319-10762-2_50},
pages = {508--517}
}
@misc{BezLopStu2014:ppsn-supp,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Design of Evolutionary Algorithms for Multi-Objective Combinatorial Optimization},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2014-007/}},
year = 2014
}
@misc{BezLopStu2015:supp,
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},
howpublished = {\url{https://github.com/iridia-ulb/automoea-tevc-2016}},
year = 2015
}
@misc{BezLopStu2015emoDEsupp,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {To {DE} or Not to {DE}? {Multi}-objective Differential
Evolution Revisited from a Component-Wise Perspective: {Supplementary} material},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2015-001/}},
year = 2015
}
@incollection{BezLopStu2015emode,
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 = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {To {DE} or Not to {DE}? {Multi}-objective Differential
Evolution Revisited from a Component-Wise Perspective},
pages = {48--63},
doi = {10.1007/978-3-319-15934-8_4}
}
@incollection{BezLopStu2015moead,
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 = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Comparing De\-com\-po\-sition-Based and Automatically
Component-Wise Designed Multi-Objective Evolutionary
Algorithms},
pages = {396--410},
doi = {10.1007/978-3-319-15934-8_27}
}
@misc{BezLopStu2016supp,
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},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2015-007/}},
year = 2017
}
@techreport{BezLopStu2017:techreport-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 = feb
}
@incollection{BezLopStu2017emo,
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 = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An Empirical Assessment of the Properties of Inverted
Generational Distance Indicators on Multi- and Many-objective
Optimization},
pages = {31--45},
doi = {10.1007/978-3-319-54157-0_3}
}
@misc{BezLopStu2019ec-supp,
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: Supplementary
material},
howpublished = {\url{https://github.com/iridia-ulb/automoea-ecj-2020}},
year = 2019
}
@inproceedings{WanSunJin2019multi,
year = 2019,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2019 Congress on Evolutionary Computation (CEC 2019)},
key = {IEEE CEC},
title = {A Multi-indicator based Selection Strategy for Evolutionary
Many-objective Optimization},
author = { Wang, Hao and Sun, Chaoli and Yaochu Jin and Qin, Shufen and
Yu, Haibo},
pages = {2042--2049},
annote = {unbounded archive}
}
@incollection{BezLopStu2019gecco,
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 = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Archiver Effects on the Performance of State-of-the-art
Multi- and Many-objective Evolutionary Algorithms},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2019-004/},
doi = {10.1145/3321707.3321789}
}
@misc{BezLopStu2019gecco-supp,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Archiver Effects on the Performance of State-of-the-art Multi- and Many-objective Evolutionary Algorithms: Supplementary material},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2019-004/}},
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},
aeditor = { Juan-Juli{\'a}n Merelo and P. Adamidis and Hans-Georg Beyer and J.-L. Fern\'{a}ndez-Villacanas and Hans-Paul Schwefel },
volume = 2439,
series = {Lecture Notes in Computer Science},
year = 2002,
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {VII}},
author = { Leonora Bianchi and L. M. Gambardella and Marco Dorigo },
title = {An Ant Colony Optimization Approach to the
Probabilistic Traveling Salesman Problem},
pages = {883--892}
}
@inproceedings{Bie14:sat,
publisher = {University of Helsinki},
series = {Science Series of Publications B},
volume = {B-2014-2},
year = 2014,
editor = {A. Belov and D. Diepold and M. Heule and M. J\"{a}rvisalo},
booktitle = {Proceedings of SAT Competition 2014: Solver and Benchmark Descriptions},
title = {Yet another Local Search Solver and {Lingeling} and Friends Entering the {SAT} Competition 2014},
author = {Armin Biere},
pages = {39--40}
}
@incollection{BieBozEim2020dynaac,
publisher = {IOS Press},
editor = {Giuseppe De Giacomo and Alejandro Catala and Bistra Dilkina
and Michela Milano and Senén Barro and Alberto BugarÃn 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)},
author = { Biedenkapp, Andr{\'e} and Bozkurt, H. Furkan and Eimer, Theresa and Frank Hutter and Marius Thomas Lindauer },
title = {Dynamic Algorithm Configuration: Foundation of a New
Meta-Algorithmic Framework},
epub = {https://ecai2020.eu/papers/1237_paper.pdf},
pages = {427--434}
}
@incollection{BieLinEggFraFawHoo2017,
publisher = {{AAAI} Press},
month = feb,
year = 2017,
editor = {Satinder P. Singh and Shaul Markovitch},
booktitle = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
author = { Biedenkapp, Andr{\'e} and Marius Thomas Lindauer and Katharina Eggensperger and Frank Hutter and Chris Fawcett and Holger H. Hoos },
title = {Efficient Parameter Importance Analysis via Ablation with
Surrogates},
doi = {10.1609/aaai.v31i1.10657}
}
@incollection{BieMarLinHut2018lion,
address = { Cham, Switzerland},
series = {Lecture Notes in Computer Science},
volume = 11353,
booktitle = {Learning and Intelligent Optimization, 12th International Conference, LION 12},
publisher = {Springer},
year = 2018,
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}
}
@incollection{BilPar1995:aisb,
booktitle = {Evolutionary Computing, AISB Workshop},
address = { Berlin, Germany},
series = {Lecture Notes in Computer Science},
volume = 993,
year = 1995,
publisher = {Springer},
editor = {T. C. Fogarty},
title = {The Ant Colony Metaphor for Searching Continuous Design
Spaces},
author = {George Bilchev and Ian C. Parmee},
pages = {25--39},
doi = {10.1007/3-540-60469-3_22}
}
@incollection{BirBalDor06,
address = { New York, NY},
series = {Operations Research/Computer Science Interfaces Series},
volume = 39,
editor = {K. F. Doerner and M. Gendreau and P. Greistorfer and
W. J. Gutjahr and R. F. Hartl and M. Reimann},
year = 2006,
publisher = {Springer},
booktitle = {Metaheuristics -- Progress in Complex Systems Optimization},
author = { Mauro Birattari and Prasanna Balaprakash and Marco Dorigo },
title = {The {ACO/F-RACE} algorithm for combinatorial optimization
under uncertainty},
pages = {189--203}
}
@incollection{BirChiSaeStu2011,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
booktitle = {Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2011},
publisher = {Springer},
year = 2011,
editor = {T. Berthold and A. M. Gleixner and S. Heinz and T. Koch},
author = { Mauro Birattari and Marco Chiarandini and Marco Saerens and Thomas St{\"u}tzle },
title = {Learning Graphical Models for Algorithm Configuration}
}
@incollection{BirDicDor02: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 = { Mauro Birattari and Gianni A. {Di Caro} and Marco Dorigo },
title = {Toward the formal foundation of Ant Programming},
pages = {188--201}
}
@book{BirKleLop2009nltk,
title = {Natural language processing with {Python}: analyzing text with
the natural language toolkit},
author = {Bird, Steven and Klein, Ewan and Loper, Edward},
year = 2009,
publisher = {O'Reilly Media, Inc.}
}
@incollection{BirStuPaqVar02:gecco,
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},
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},
epub = {https://dl.acm.org/doi/pdf/10.5555/2955491.2955494}
}
@incollection{BirYuaBal2010:emaoa,
editor = { Thomas Bartz-Beielstein and Marco Chiarandini and Lu{\'i}s Paquete and Mike Preuss },
year = 2010,
address = { Berlin, Germany},
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}
}
@book{Birattari09tuning,
title = {Tuning Metaheuristics: A Machine Learning
Perspective},
doi = {10.1007/978-3-642-00483-4},
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}
}
@inproceedings{BisIzzYam2010:pagmo,
title = {A Global Optimisation Toolbox for Massively Parallel
Engineering Optimisation},
author = {Biscani, Francesco and Dario Izzo and Yam, Chit Hong},
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},
publisher = {ACM Press},
year = 2012,
editor = {Terence Soule and Jason H. Moore},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2012},
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}
}
@book{Bishop2006,
title = {Pattern recognition and machine learning},
author = {Bishop, Christopher M.},
year = 2006,
publisher = {Springer}
}
@inproceedings{BiyMarAli2019acc,
year = 2019,
publisher = {{IEEE}},
booktitle = {2019 American Control Conference ({ACC})},
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 },
title = {Ant Colony Optimization for the Maximum
Edge-Disjoint Paths Problem},
pages = {160--169}
}
@inproceedings{BliMcDPer2006emnlp,
year = 2006,
editor = {Jurafsky, Dan and Gaussier, Eric},
series = {Empirical Methods in Natural Language Processing},
booktitle = {Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP2006},
title = {Domain adaptation with structural correspondence learning},
author = {Blitzer, John and McDonald, Ryan and Pereira, Fernando},
pages = {120--128}
}
@incollection{BloHooJouKesTra2016:lion,
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 },
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}
}
@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},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2017},
author = { Aymeric Blot and Alexis Pernet and Laetitia Jourdan and Marie-El{\'e}onore Kessaci-Marmion and Holger H. Hoos },
title = {Automatically Configuring Multi-objective Local Search Using
Multi-objective Optimisation},
pages = {61--76}
}
@incollection{BluBauPer06: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 = { Christian Blum and J. Bautista and J. Pereira },
title = {{Beam-ACO} applied to assembly line balancing},
pages = {96--107},
doi = {10.1007/11839088_9}
}
@techreport{BluBleLop08: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},
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}}
}
@incollection{BluCotFerGal07:evocop,
address = { Berlin, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 4446,
year = 2007,
editor = { Carlos Cotta and others},
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/}
}
@incollection{BluMas2007hm,
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}
}
@book{BluMer08:si-book,
title = {Swarm Intelligence--Introduction and Applications},
year = 2008,
editor = { Christian Blum and D. Merkle },
series = {Natural Computing Series},
publisher = {Springer Verlag, Berlin, Germany}
}
@book{BluRai2016:book,
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}
}
@incollection{BluRol2008hybrid,
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},
title = {Hybrid metaheuristics: an introduction},
author = { Christian Blum and Andrea Roli },
pages = {1--30}
}
@incollection{BluYab06:hm,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 4030,
editor = {F. Almeida and others},
aeditor = {F. Almeida and M. Blesa and C. Blum and J. M. Moreno
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}
sequencing by hybridization},
pages = {94--109},
doi = {10.1007/11890584}
}
@phdthesis{Boese1996,
author = {K. D. Boese},
title = {Models for Iterative Global Optimization},
school = {University of California, Computer Science Department,
Los Angeles, CA},
year = 1996
}
@book{Bollobas2001,
author = {B{\'e}la Bollob{\'a}s},
title = {Random Graphs},
publisher = {Cambridge University Press},
address = { New York, NY},
year = 2001,
edition = {2nd}
}
@book{BooRumJac2005,
author = {Grady Booch and James E. Rumbaugh and Ivar Jacobson},
title = {The Unified Modeling Language User Guide},
publisher = {Addison-Wesley},
year = 2005,
edition = {2nd}
}
@techreport{Bor1998,
author = {Borges, P. C. and Michael Pilegaard Hansen },
title = {A basis for future successes in multiobjective
combinatorial optimization},
year = 1998,
institution = {Institute of Mathematical Modelling, Technical
University of Denmark},
number = {IMM-REP-1998-8},
address = {Lyngby, Denmark}
}
@book{BorEly1998online,
author = {Borodin, Allan and El-Yaniv, Ran},
title = {Online computation and competitive analysis},
year = 1998,
isbn = {0-521-56392-5},
publisher = {Cambridge University Press},
address = { New York, NY}
}
@book{BorHedHigRot2009metanalysis,
title = {Introduction to Meta-Analysis},
author = {Michael Borenstein and Larry V. Hedges and Julian P. T. Higgins and Hannah R. Rothstein},
year = 2009,
publisher = {Wiley}
}
@inproceedings{BosGuyVap1992,
publisher = {ACM Press},
editor = {David Haussler},
booktitle = {COLT'92},
year = 1992,
author = {Bernhard E. Boser and Isabelle Guyon and Vladimir Vapnik},
title = {A Training Algorithm for Optimal Margin Classifiers},
pages = {144--152},
doi = {10.1145/130385.130401},
annote = {Proposed SVM}
}
@incollection{BosKerNeu2019,
publisher = {{ACM}},
editor = { Tobias Friedrich and Carola Doerr and Arnold, Dirk V.},
year = 2019,
booktitle = {Proceedings of the 15th {ACM}/{SIGEVO} Conference on Foundations of Genetic Algorithms},
author = { Jakob Bossek and Pascal Kerschke and Neumann, Aneta and Markus Wagner and Frank Neumann and Heike Trautmann },
title = {Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators},
pages = {58--71}
}
@inproceedings{Boulos01,
author = { Paul F. Boulos and Chun Hou Orr and Werner de Schaetzen and J. G. Chatila and Michael Moore and Paul Hsiung and Devan Thomas },
title = {Optimal pump operation of water distribution systems
using genetic algorithms},
booktitle = {AWWA Distribution System Symp.},
year = 2001,
address = {Denver, USA},
publisher = {American Water Works Association}
}
@incollection{Bow1976,
author = {Bowman, V. and Joseph, Jr.},
title = {On the Relationship of the {Tchebycheff} Norm and the Efficient
Frontier of Multiple-Criteria Objectives},
year = 1976,
booktitle = {Multiple Criteria Decision Making},
volume = 130,
series = {Lecture Notes in Economics and Mathematical Systems},
pages = {76--86},
editor = {Thiriez, Herv\'e and Zionts, Stanley},
doi = {10.1007/978-3-642-87563-2_5},
publisher = {Springer},
address = {Berlin\slash Heidelberg}
}
@book{BoxDra2007rsm,
title = {Response surfaces, mixtures, and ridge analyses},
author = {Box, George E. P. and Draper, Norman R.},
year = 2007,
publisher = {John Wiley \& Sons}
}
@book{BoxHunHun1978stat,
title = {Statistics for experimenters: an introduction to design, data
analysis, and model building},
author = {Box, G. E. P. and Hunter, W. G. and Hunter, J. S.},
year = 1978,
publisher = {John Wiley \& Sons},
address = { New York, NY}
}
@incollection{Bra88:lnpam,
author = {A. Brandt},
title = {Multilevel Computations: {Review} and Recent Developments},
booktitle = {Multigrid Methods: Theory, Applications, and Supercomputing, Proceedings of the 3rd Copper Mountain Conference on Multigrid Methods},
pages = {35--62},
year = 1988,
editor = {S. F. McCormick},
volume = 110,
series = {Lecture Notes in Pure and Applied Mathematics},
publisher = {Marcel Dekker},
address = { New York, NY}
}
@inproceedings{BraBarWhiHubHin2007wfg,
author = {L. Bradstreet and L. Barone and L. While and S. Huband and
P. Hingston},
booktitle = {{IEEE} Symposium on Computational Intelligence in
Multicriteria Decision-Making, {IEEE} {MCDM}},
title = {Use of the {WFG} Toolkit and {PISA} for Comparison of
{MOEAs}},
year = 2007,
pages = {382--389}
}
@incollection{BarOjaGar2018ppsn,
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 = { Barba-Gonz{\'a}lez, Crist{\'o}bal and Vesa Ojalehto and Jos{\'e} Garc{\'i}a-Nieto and Nebro, Antonio J. and Kaisa Miettinen and Jos{\'{e}} F. Aldana-Montes},
title = {Artificial Decision Maker Driven by {PSO}: An Approach for
Testing Reference Point Based Interactive Methods},
doi = {10.1007/978-3-319-99253-2_22},
pages = {274--285},
keywords = {machine decision-maker}
}
@incollection{BraCorGre2015dagstuhl,
keywords = {multiple criteria decision making, evolutionary
multiobjective optimization},
doi = {10.4230/DagRep.5.1.96},
volume = {5(1)},
year = 2015,
series = {Dagstuhl Reports},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik, Germany},
booktitle = {Understanding Complexity in Multiobjective Optimization
(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,
year = 2006,
volume = 4193,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
editor = {Runarsson, Thomas Philip and Hans-Georg Beyer and Edmund K. Burke and Juan-Juli{\'a}n Merelo and Darrell Whitley and Xin Yao },
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {IX}},
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},
publisher = {Kluwer Academic Publishers},
year = 1998,
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, Germany},
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,
year = 2004,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 2972,
series = {Lecture Notes in Artificial Intelligence},
booktitle = {Proceedings of MICAI},
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}
}
@inproceedings{Cook1971,
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, fANO