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IRIDIA BibTeX Repository

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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.

References

tmp5Taep5kefz.bib

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@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},
  alias = {Addis2008}
}
@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{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,
  alias = {AppCooRoh99},
  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,
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}
@article{ArnSor2019vrp,
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}
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                  and genetic algorithms for facilities location problems},
  author = {Arostegui Jr, Marvin A. and Kadipasaoglu, Sukran N. and Khumawala, Basheer M.},
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}
@article{Arr04,
  title = {A partial enumeration heuristic for multi-objective
                  flowshop scheduling problems},
  author = { Jos{\'e} Elias C. Arroyo  and V. A. Armentano},
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  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},
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  pages = {84--100}
}
@article{Asch01tsptw,
  author = { N. Ascheuer  and  Matteo Fischetti  and  M. Gr{\"o}tschel },
  title = {Solving asymmetric travelling salesman problem with
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}
@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,
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@article{AudDanOrb2014,
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  title = {Optimization of Algorithms with {OPAL}},
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  year = 2014,
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}
@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)},
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  volume = 35,
  publisher = {Zinatne Publishing House, Riga},
  alias = {Audze1977}
}
@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},
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  year = 2012,
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}
@article{AvcTop2017:cor,
  author = {Mustafa Avci and Seyda Topaloglu},
  title = {A Multi-start Iterated Local Search Algorithm for the Generalized Quadratic Multiple Knapsack Problem},
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  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},
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  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},
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  year = 1996,
  volume = 44,
  number = 6,
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}
@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}
}
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  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}
}
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  doi = {10.1016/j.inffus.2019.12.012},
  year = 2020,
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  publisher = {Elsevier {BV}},
  volume = 58,
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  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,
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  url = {https://arxiv.org/abs/2007.03488}
}
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}
@article{BarJohNem1998or,
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                  programs},
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                  L. and  Martin W. P. Savelsbergh  and Vance, Pamela H.},
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@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
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}
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  author = { Elias Bareinboim  and  Judea Pearl },
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}
@article{BarZae2017model,
  author = { Thomas Bartz-Beielstein  and  Martin Zaefferer },
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                  optimization},
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}
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@article{BatPas2010tec,
  author = { Roberto Battiti  and  Andrea Passerini },
  title = {Brain-Computer Evolutionary Multiobjective Optimization: A
                  Genetic Algorithm Adapting to the Decision Maker},
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                  DTLZ8 in \cite{DebThiLau2005dtlz}}
}
@article{BatPro1996:jea,
  author = { Roberto Battiti  and M. Protasi},
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  volume = 2
}
@article{BatSchUrl2017,
  author = {Michele Battistutta and Andrea Schaerf and  Tommaso Urli },
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                  Examination Timetabling},
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@article{BatTec1994:cma,
  title = {Simulated annealing and Tabu search in the long run: A
                  comparison on {QAP} tasks},
  author = { Roberto Battiti  and  Tecchiolli, Giampietro },
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}
@article{BatTec1994:orsa,
  author = { Roberto Battiti  and  Tecchiolli, Giampietro },
  title = {The Reactive Tabu Search},
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  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},
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  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},
  alias = {BezLopStu2016assessment},
  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},
  alias = {Bia++06}
}
@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},
  alias = {BirPelDor2007ieee-tevc}
}
@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},
  alias = {BlumCOR05}
}
@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},
  alias = {Blu08:ijoc}
}
@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},
  alias = {BluYabBle08}
}
@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},
  alias = {Bra++06}
}
@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,
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}
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                  heuristic algorithms to systematically reduce the number of
                  objectives, while preserving as much as possible of the
                  dominance structure of the underlying optimization
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                  making and search for a radar waveform application as well as
                  for well-known test functions.},
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}
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  title = {A distance-based ranking model estimation of distribution
                  algorithm for the flowshop scheduling problem},
  abstract = {The aim of this paper is two-fold. First, we introduce a
                  novel general estimation of distribution algorithm to deal
                  with permutation-based optimization problems. The algorithm
                  is based on the use of a probabilistic model for permutations
                  called the generalized Mallows model. In order to prove the
                  potential of the proposed algorithm, our second aim is to
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                  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
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                  five data sets involving up to 225 assets.  Scope and purpose
                  The standard Markowitz mean-variance approach to portfolio
                  selection involves tracing out an efficient frontier, a
                  continuous curve illustrating the tradeoff between return and
                  risk (variance). This frontier can be easily found via
                  quadratic programming. This approach is well-known and widely
                  applied. However, for practical purposes, it may be desirable
                  to limit the number of assets in a portfolio, as well as
                  imposing limits on the proportion of the portfolio devoted to
                  any particular asset. If such constraints exist, the problem
                  of finding the efficient frontier becomes much harder. This
                  paper illustrates how, in the presence of such constraints,
                  the efficient frontier becomes discontinuous. Three heuristic
                  techniques are applied to the problem of finding this
                  efficient frontier and computational results presented for a
                  number of data sets which are made publicly available.}
}
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  year = 2011,
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                  Multi-objective Ant Colony Algorithm to Solve a
                  Real-world time and Space Assembly Line Balancing
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}
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  keywords = {irace}
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}
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  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
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  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}
}
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  author = { Jean Daunizeau  and  Hanneke E. M. den Ouden  and  Matthias Pessiglione  and  Stefan J. Kiebel  and  Karl J. Friston  and  Klaas E. Stephan },
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  title = {Observing the observer ({I}): meta-{Bayesian} models of learning and decision-making},
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@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
}
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  author = { V. Dekhtyarenko },
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@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,
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  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},
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@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 },
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  number = 2,
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  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},
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  pages = {146--162},
  year = 2016,
  doi = {10.1016/j.cor.2016.01.011},
  keywords = {irace}
}
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  title = {An interactive {MCDM} weight space reduction method utilizing
                  a {Tchebycheff} utility function},
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@article{DemEichFli2015effects,
  title = {On the effects of combining objectives in multi-objective
                  optimization},
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  publisher = {Springer}
}
@article{DenAroGosPas1990,
  author = { Jean-Louis Deneubourg  and S. Aron and S. Goss and
                  J.-M. Pasteels},
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@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 },
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  number = 1,
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  year = 2011
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@article{DerVog2014:joh,
  author = {Ulrich Derigs and Ulrich Vogel},
  title = {Experience with a Framework for Developing Heuristics for
                  Solving Rich Vehicle Routing Problems},
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  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},
  alias = {DIAZ2017266}
}
@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},
  alias = {DIAZ2018976}
}
@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}
}
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  author = { L. C. Dias  and  Vincent Mousseau  and  Jos{\'e} Rui Figueira  and  J. N. Cl{\'i}maco },
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  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
}
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  author = { Gianni A. {Di Caro}  and  Marco Dorigo },
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  volume = 9,
  pages = {317--365},
  year = 1998,
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}
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  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},
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  year = 2005,
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  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},
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  volume = 33,
  year = 2003
}
@article{DilKhaNem2017comments,
  title = {Comments on: On learning and branching: a survey},
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                  L.},
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  volume = 25,
  pages = {242--246},
  year = 2017,
  publisher = {Springer},
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}
@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},
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  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},
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  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},
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  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},
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@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},
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  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
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@article{DoeGutHarStrStu04:aor,
  author = { Karl F. Doerner  and  Gutjahr, Walter J.  and  Richard F. Hartl  and  Christine Strauss  and  Christian Stummer },
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  alias = {DoeGutHarStrStu03}
}
@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},
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  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
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  number = 2,
  year = 2003
}
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                  model, in the sense that every choice probability
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                  orthogonal contrast model class and, in turn, includes the
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                  Fligner and Verducci's multistage ranking model.}
}
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}
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}
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  abstract = {Tuning controller parameters is a recurring and
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}
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                  biomedical disciplines. The United States had published, over
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}
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                  Smart Mobility Scenarios},
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  year = 2016
}
@article{FerGuiRamJua2016,
  author = {Alberto Ferrer and Daniel Guimarans and  Helena {Ramalhinho Louren{\c c}o}  and Angel A. Juan},
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                  Real-World City},
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}
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  author = { Eduardo Fernandez  and  Jorge Navarro  and  Sergio Bernal },
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@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},
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}
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  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
}
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  author = { {\'A}lvaro Fialho  and Luis {Da Costa} and  Marc Schoenauer  and  Mich{\`e}le Sebag },
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  title = {Using unconstrained elite archives for multiobjective
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                  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},
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  author = { Matteo Fischetti  and  Monaci, Michele },
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  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. }
}
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  author = { Matteo Fischetti  and Salvagnin, Domenico},
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}
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  number = 1,
  pages = {1--16},
  volume = 3,
  annote = {Proposed FON benchmark problem}
}
@article{FonFle1998:tsmca,
  author = { Carlos M. Fonseca  and  Peter J. Fleming },
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  author = { Carlos M. Fonseca  and  Peter J. Fleming },
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  keywords = {Interactive optimization, Multi-objective optimization,
                  Evolutionary optimization, Knapsack problem},
  abstract = {We present a new hybrid approach to interactive evolutionary
                  multi-objective optimization that uses a partial preference
                  order to act as the fitness function in a customized genetic
                  algorithm. We periodically send solutions to the decision
                  maker (DM) for her evaluation and use the resulting
                  preference information to form preference cones consisting of
                  inferior solutions. The cones allow us to implicitly rank
                  solutions that the DM has not considered. This technique
                  avoids assuming an exact form for the preference function,
                  but does assume that the preference function is
                  quasi-concave. This paper describes the genetic algorithm and
                  demonstrates its performance on the multi-objective knapsack
                  problem.}
}
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  title = {Integrating and accelerating tabu search, simulated
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  publisher = {Springer}
}
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}
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  author = { Alberto Franzin },
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  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 },
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                  of Control Software for Robot Swarms},
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  number = 2,
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}
@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 },
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                 in Automatic Algorithm Configuration},
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  year = 2018,
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}
@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},
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}
@article{FraStu2019:cor,
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}
@article{FraStu2022:ejor,
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  title = {A Landscape-based Analysis of Fixed Temperature and Simulated Annealing},
  year = 2023,
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}
@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.},
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  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}},
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}
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@article{FueDoeHar09ejor,
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  title = {Metaheuristics for vehicle routing problems with
                  three-dimensional loading constraints},
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}
@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},
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  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.}
}
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}
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                  in parallel, while a model incrementally learns their runtime
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}
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                  be deleted without influencing the set E of all efficient
                  solutions. Such objectives are said to be
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                  realize their individual optimum in a single vertex of the
                  polyhedron generated by the restriction set, the notion of
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                  procedure.}
}
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@article{Gao2016,
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@article{GaoNieLi2019visarxiv,
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}
@article{GarAlbOli2012,
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                  optimization,Realistic traffic instances,SUMO microscopic
                  simulator of urban mobility,Traffic light scheduling},
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@article{GarCorHer07,
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}
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                  design of experiments in computational intelligence and data
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                  Juli{\'a}n and  Francisco Herrera },
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}
@article{GarGloRodLozMar2014,
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}
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}
@article{GarOliAlb2013tec,
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                  Joshua B.},
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  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,
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  publisher = {Springer Science and Business Media {LLC}},
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}
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@article{GirRabPib2016,
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                  and Mathieu, Patrick and Roy, Pascal},
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}
@article{Glo1986,
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}
@article{Glo1990,
  author = { Fred Glover },
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}
@article{GloLuHao2010diversif,
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                  quadratic problems},
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  keywords = {BFGS}
}
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  title = {Probability matching, the magnitude of reinforcement, and
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@article{GonZhaChi2018kbs,
  title = {The optimization ordering model for intuitionistic fuzzy
                  preference relations with utility functions},
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  pages = {174--184},
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  annote = {Special Issue on intelligent decision-making and consensus
                  under uncertainty in inconsistent and dynamic environments},
  issn = {0950-7051},
  doi = {10.1016/j.knosys.2018.07.012},
  author = {Zaiwu Gong and Ning Zhang and Francisco Chiclana},
  keywords = {Intuitionistic fuzzy preference relation, Utility function,
                  Ranking, Multiplicative consistency, Non-archimedean
                  infinitesimal},
  abstract = {Intuitionistic fuzzy sets describe information from the three
                  aspects of membership degree, non-membership degree and
                  hesitation degree, which has more practical significance when
                  uncertainty pervades qualitative decision problems. In this
                  paper, we investigate the problem of ranking intuitionistic
                  fuzzy preference relations (IFPRs) based on various
                  non-linear utility functions. First, we transform IFPRs into
                  their isomorphic interval-value fuzzy preference relations
                  (IVFPRs), and utilise non-linear utility functions, such as
                  parabolic, S-shaped, and hyperbolic absolute risk aversion,
                  to fit the true value of a decision-maker's
                  judgement. Ultimately, the optimization ordering models for
                  the membership and non-membership of IVFPRs based on utility
                  function are constructed, with objective function aiming at
                  minimizing the distance deviation between the multiplicative
                  consistency ideal judgment and the actual judgment,
                  represented by utility function, subject to the
                  decision-maker's utility constraints. The proposed models
                  ensure that more factual and optimal ranking of alternative
                  is acquired, avoiding information distortion caused by the
                  operations of intervals. Second, by introducing a
                  non-Archimedean infinitesimal, we establish the optimization
                  ordering model for IFPRs with the priority of utility or
                  deviation, which realises the goal of prioritising solutions
                  under multi-objective programming. Subsequently, we verify
                  that a close connection exists between the ranking for
                  membership and non-membership degree IVFPRs. Comparison
                  analyses with existing approaches are summarized to
                  demonstrate that the proposed models have advantage in
                  dealing with group decision making problems with IFPRs.}
}
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  author = {Gorski, Jochen and  Kathrin Klamroth  and Ruzika, Stefan},
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  pages = {475--497},
  publisher = {Springer},
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  year = 2011,
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}
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}
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}
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}
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}
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  author = { Salvatore Greco  and  Kadzi{\'n}ski, Mi{\l}osz   and  Vincent Mousseau  and  Roman S{\l}owi{\'n}ski },
  title = {{ELECTRE}$^\mathrm{{GKMS}}$: Robust ordinal regression for outranking methods},
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}
@article{GreMouSlo2014ejor,
  author = { Salvatore Greco  and  Vincent Mousseau  and  Roman S{\l}owi{\'n}ski },
  title = {Robust ordinal regression for value functions handling interacting
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}
@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},
  alias = {Fonseca02a},
  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}
}
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  title = {Flowshop schedules with sequence dependent setup times},
  author = {J. N. D. Gupta},
  journal = {Journal of Operations Research Society of Japan},
  volume = 29,
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  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,
  alias = {Hadka13borg}
}
@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},
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  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,
  alias = {Hardin2004}
}
@article{HarSho87a,
  author = {J. P. Hart and A. W. Shogan},
  title = {Semi-greedy heuristics: An empirical study},
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  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},
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  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},
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  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.},
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}
@article{HelRen1998mp,
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                  cutting planes},
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  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},
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  year = 2000,
  volume = 126,
  pages = {106--130}
}
@article{Helsgaun09,
  author = { Keld Helsgaun },
  title = {General {\it k}-opt Submoves for the {Lin}-{Kernighan} {TSP}
               Heuristic},
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  year = 2009,
  volume = 1,
  number = {2--3},
  pages = {119--163}
}
@article{Her2015toms,
  author = {Michael A. Heroux},
  title = {Editorial: {ACM} {TOMS} Replicated Computational Results
                  Initiative},
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  year = 2015,
  month = jun,
  publisher = {Association for Computing Machinery ({ACM})},
  volume = 41,
  number = 3,
  pages = {1--5},
  journal = {ACM Transactions on Mathematical Software}
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}
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}
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}
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@article{HutHooLeyStu2009jair,
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  volume = 206,
  pages = {79--111},
  doi = {10.1016/j.artint.2013.10.003},
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                  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}
}
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  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},
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  year = 2022,
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  number = 1,
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}
@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},
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  year = 2021,
  annote = {Published in ECJ~\cite{IOHexperimenter2024}},
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}
@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},
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  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},
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  year = 2018,
  month = oct,
  keywords = {Benchmarking; Heuristics},
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}
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}
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                  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.},
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}
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                  the proposed method and its comparison with the
                  existing solver that utilizes the nonlinear
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                  compared to the widespread gradient search methods
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                  gradient of the objective function. It also provides
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                  initial values of the decision variables in the
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                  sensitive to the starting value of the decision
                  variables.}
}
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                  Models Under the {Cayley} Distance},
  doi = {10.1007/s11009-016-9506-7},
  year = 2016,
  month = jun,
  volume = 20,
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@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},
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                  Javier},
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  year = 2020,
  url = {https://arxiv.org/abs/1910.08795}
}
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@article{JiaZouYanYao2022dynamic,
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@article{Jin2005fitness,
  author = { Yaochu Jin },
  title = {A Comprehensive Survey of Fitness Approximation in
                  Evolutionary Computation},
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@article{Jin2011surrogate,
  author = { Yaochu Jin },
  title = {Surrogate-Assisted Evolutionary Computation: Recent
                  Advances and Future Challenges},
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  year = 2011,
  month = jun,
  journal = {Swarm and Evolutionary Computation},
  volume = 1,
  number = 2,
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                  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}
}
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                  simulated annealing, parameter tuning, irace}
}
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  number = 2,
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                  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
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                  toward an efficient, effective, and robust search and improve
                  their performance in terms of solution quality, convergence
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                  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},
  alias = {Ken++2016:jors}
}
@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},
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  volume = 35,
  number = 6,
  pages = {615--623},
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  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
}
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  title = {Sustainable energy futures: Methodological challenges in
                  combining scenarios and participatory multi-criteria
                  analysis},
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                  and Omann, Ines},
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  number = 3,
  pages = {1063--1074},
  year = 2009,
  publisher = {Elsevier}
}
@article{Kra2010,
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  volume = 2,
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  pages = {69--83},
  doi = {10.1007/s12293-010-0032-9},
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}
@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},
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}
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  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
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                  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.},
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  journal = {Management Science},
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                  the experimental optimization of the performance of a system
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                  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
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                  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
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                  interpretation and allows the use of simple but efficient
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                  the use of observations. The approach seems quite promising
                  as a solution to many of the problems of experimental system
                  optimization.},
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}
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                  $\epsilon$-Pareto},
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}
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                  joint distribution as the vine method. The methods are
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                  correlation matrices of given dimensions.},
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                  correlation; LKJ}
}
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  year = 2018,
  number = 1,
  pages = {61--78},
  volume = 22,
  annote = {highly degenerate Pareto fronts}
}
@article{LiShaBah2016traffic,
  author = {Li, Zhiyi and Shahidehpour, Mohammad and Bahramirad, Shay and
                  Khodaei, Amin},
  doi = {10.1109/TSG.2016.2526032},
  alias = {Li2016},
  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{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,
  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{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},
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  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},
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  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}
}
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  title = {Predictive control of a gas--liquid separation plant based on
                  a {Gaussian} process model},
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  publisher = {Elsevier},
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}
@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
}
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  title = {An Effective Heuristic Algorithm for the Traveling
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  title = {The algorithm selection competitions 2015 and 2017},
  author = { Marius Thomas Lindauer  and  van Rijn, Jan N.  and Kotthoff, Lars},
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  pages = {86--100},
  year = 2019
}
@article{LisWit2015tcs,
  title = {Runtime Analysis of Ant Colony Optimization on Dynamic
                  Shortest Path Problems},
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  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,
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}
@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,
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  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},
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  year = 2001,
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}
@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
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}
@article{LodMarMon2002,
  title = {Two-dimensional packing problems: A survey},
  author = { Andrea Lodi  and  Silvano Martello  and  Monaci, Michele },
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}
@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 },
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}
@article{LodMarVig2004tspack,
  title = {{TSpack}: a unified tabu search code for multi-dimensional bin
                  packing problems},
  author = { Andrea Lodi  and  Silvano Martello  and  Vigo, Daniele },
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}
@article{LodZar2017learning,
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  publisher = {Springer}
}
@article{LohHorLin2008antennas,
  author = {Lohn, Jason D. and Hornby, Gregory S. and Linden, Derek S.},
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}
@article{LopBlu2010cor,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Christian Blum },
  title = {Beam-{ACO} for the travelling salesman problem with
                  time windows},
  journal = {Computers \& Operations Research},
  year = 2010,
  doi = {10.1016/j.cor.2009.11.015},
  volume = 37,
  number = 9,
  pages = {1570--1583},
  keywords = {Ant colony optimization, Travelling salesman problem with
                  time windows, Hybridization},
  alias = {LopBlu09tsptw},
  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},
  alias = {LopPaqStu06:jmma},
  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,
  alias = {Lust09}
}
@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 },
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                  and some possible ways forward},
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  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 },
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}
@article{Man1999:joc,
  author = { Vittorio Maniezzo },
  title = {Exact and Approximate Nondeterministic Tree-Search Procedures
                  for the Quadratic Assignment Problem},
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  year = 1999,
  volume = 11,
  number = 4,
  pages = {358--369},
  alias = {Man99:informs}
}
@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},
  alias = {ManCar00}
}
@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},
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  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 = 11,
  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{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}
}
@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,
  alias = {McKay1979},
  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},
  alias = {MerMid02:ai}
}
@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}
}
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}
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}
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}
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}
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  publisher = {{MIT} Press},
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}
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  title = {Algorithm selection for black-box continuous optimization
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}
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  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},
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  year = 2019
}
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}
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}
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}
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}
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@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.}
}
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}
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}
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  year = 2009,
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}
@article{NeuWit2006:eccc,
  author = { Frank Neumann  and  Carsten Witt },
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                  Algorithm},
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}
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                  intelligence, science, empirical}
}
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  author = {Viet-Phuong Nguyen and  Christian Prins  and Caroline Prodhon},
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                  as hindsight bias, make it hard to avoid this mistake. An
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                  that result from postdictions. A variety of practical
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}
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}
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}
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@article{PagStu2016,
  author = { Federico Pagnozzi  and  Thomas St{\"u}tzle },
  title = {Speeding up Local Search for the Insert Neighborhood in the Weighted Tardiness Permutation Flowshop Problem},
  journal = {Optimization Letters},
  year = 2017,
  volume = 11,
  pages = {1283--1292},
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}
@article{PagStu2019:ejor,
  author = { Federico Pagnozzi  and  Thomas St{\"u}tzle },
  title = {Automatic Design of Hybrid Stochastic Local Search Algorithms
                  for Permutation Flowshop Problems},
  journal = {European Journal of Operational Research},
  year = 2019,
  volume = 276,
  pages = {409--421},
  issue = 2,
  doi = {10.1016/j.ejor.2019.01.018},
  keywords = {EMILI},
  abstract = {Stochastic local search methods are at the core of many
                  effective heuristics for tackling different permutation
                  flowshop problems (PFSPs). Usually, such algorithms require a
                  careful, manual algorithm engineering effort to reach high
                  performance. An alternative to the manual algorithm
                  engineering is the automated design of effective SLS
                  algorithms through building flexible algorithm frameworks and
                  using automatic algorithm configuration techniques to
                  instantiate high-performing algorithms. In this paper, we
                  automatically generate new high-performing algorithms for
                  some of the most widely studied variants of the PFSP. More in
                  detail, we (i) developed a new algorithm framework, EMILI,
                  that implements algorithm-specific and problem-specific
                  building blocks; (ii) define the rules of how to compose
                  algorithms from the building blocks; and (iii) employ an
                  automatic algorithm configuration tool to search for high
                  performing algorithm configurations. With these ingredients,
                  we automatically generate algorithms for the PFSP with the
                  objectives makespan, total completion time and total
                  tardiness, which outperform the best algorithms obtained by a
                  manual algorithm engineering process.}
}
@article{PagStu2020:itor,
  author = { Federico Pagnozzi  and  Thomas St{\"u}tzle },
  title = {Evaluating the impact of grammar complexity in automatic algorithm design},
  journal = {International Transactions in Operational Research},
  pages = {1--26},
  doi = {10.1111/itor.12902},
  year = 2020
}
@article{PagStu2021:orp,
  author = { Federico Pagnozzi  and  Thomas St{\"u}tzle },
  title = {Automatic design of hybrid stochastic local search algorithms
                  for permutation flowshop problems with additional
                  constraints},
  journal = {Operations Research Perspectives},
  year = 2021,
  volume = 8,
  pages = 100180,
  doi = {10.1016/j.orp.2021.100180},
  abstract = {Automatic design of stochastic local search algorithms has
                  been shown to be very effective in generating algorithms for
                  the permutation flowshop problem for the most studied
                  objectives including makespan, flowtime and total
                  tardiness. The automatic design system uses a configuration
                  tool to combine algorithmic components following a set of
                  rules defined as a context-free grammar. In this paper we use
                  the same system to tackle two of the most studied additional
                  constraints for these objectives: sequence dependent setup
                  times and no-idle constraint. Additional components have been
                  added to adapt the system to the new problems while keeping
                  intact the grammar structure and the experimental setup. The
                  experiments show that the generated algorithms outperform the
                  state of the art in each case.}
}
@article{PajBlaHerMar2021archiving,
  title = {A Comparison of Archiving Strategies for Characterization of
                  Nearly Optimal Solutions under Multi-Objective Optimization},
  author = {Pajares, Alberto and Blasco, Xavier and Herrero, Juan Manuel
                  and Mart{\'i}nez, Miguel A.},
  journal = {Mathematics},
  year = 2021,
  doi = {10.3390/math9090999},
  volume = 9,
  number = 9,
  pages = {999},
  abstract = {In a multi-objective optimization problem, in addition to
                  optimal solutions, multimodal and/or nearly optimal
                  alternatives can also provide additional useful information
                  for the decision maker. However, obtaining all nearly optimal
                  solutions entails an excessive number of
                  alternatives. Therefore, to consider the nearly optimal
                  solutions, it is convenient to obtain a reduced set, putting
                  the focus on the potentially useful alternatives. These
                  solutions are the alternatives that are close to the optimal
                  solutions in objective space, but which differ significantly
                  in the decision space. To characterize this set, it is
                  essential to simultaneously analyze the decision and
                  objective spaces. One of the crucial points in an
                  evolutionary multi-objective optimization algorithm is the
                  archiving strategy. This is in charge of keeping the solution
                  set, called the archive, updated during the optimization
                  process. The motivation of this work is to analyze the three
                  existing archiving strategies proposed in the literature
                  (ArchiveUpdate$P_{Q,\epsilon}D_{xy}$, Archive\_nevMOGA, and
                  targetSelect) that aim to characterize the potentially useful
                  solutions. The archivers are evaluated on two benchmarks and
                  in a real engineering example. The contribution clearly shows
                  the main differences between the three archivers. This
                  analysis is useful for the design of evolutionary algorithms
                  that consider nearly optimal solutions.},
  keywords = {multi-objective optimization; nearly optimal solutions;
                  non-epsilon dominance; multimodality; decision space
                  diversity; archiving strategy; evolutionary algorithm;
                  non-linear parametric identification}
}
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  author = {Daniel {Palhazi Cuervo} and Peter Goos and  Kenneth S{\"o}rensen  and Emely Arr{\'{a}}iz},
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               with Backhauls},
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  year = 2014,
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}
@article{Palubeckis2006,
  title = {Iterated tabu search for the unconstrained binary quadratic
                  optimization problem},
  author = {Palubeckis, Gintaras},
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  year = 2006,
  publisher = {Institute of Mathematics and Informatics},
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}
@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,
  alias = {PanRui2012}
}
@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},
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  number = 10,
  pages = {1345--1359},
  year = 2009
}
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  author = { Lu{\'i}s Paquete  and  Tommaso Schiavinotto  and  Thomas St{\"u}tzle },
  title = {On Local Optima in Multiobjective Combinatorial
                  Optimization Problems},
  journal = {Annals of Operations Research},
  year = 2007,
  volume = 156,
  pages = {83--97},
  doi = {10.1007/s10479-007-0230-0},
  keywords = {Pareto local search, PLS},
  abstract = {In this article, local optimality in multiobjective
                  combinatorial optimization is used as a baseline for
                  the design and analysis of two iterative improvement
                  algorithms. Both algorithms search in a neighborhood
                  that is defined on a collection of sets of feasible
                  solutions and their acceptance criterion is based on
                  outperformance relations. Proofs of the soundness
                  and completeness of these algorithms are given.}
}
@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,
  alias = {lpaquete:14},
  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,
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  doi = {10.1016/j.cor.2008.11.013}
}
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}
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                  and Vincent Larivière and Alina Beygelzimer and Florence
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                  approach where we use ant colony optimization in
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                  {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
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                  computational time.}
}
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}
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}
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                  Eduardo and León, Coromoto and Rodríguez-León, Casiano},
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  publisher = {{MDPI} {AG}},
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                  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
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                  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
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                  goal of the current work is to demonstrate the
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                  comprehensive experimental assessment carried out over a set
                  of multi-objective evolutionary algorithms applied to
                  different instances. At the same time, we are also interested
                  in validating the multi-objective formulation by performing
                  quantitative and qualitative analyses of the solutions
                  attained when solving it. Computational results show the
                  multi-objective nature of the said formulation, as well as
                  that it allows suitable meal plans to be obtained.}
}
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  keywords = {artificial DM, interactive}
}
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  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. },
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}
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  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 },
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  year = 2019,
  pages = {155--175},
  volume = 114,
  publisher = {Elsevier},
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  title = {Benchmark-Driven Configuration of a Parallel Model-Based
                  Optimization Algorithm},
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  volume = 26,
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  doi = {10.1109/TEVC.2022.3163843}
}
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  author = { Marc Reimann  and  Marco Laumanns },
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                  with known demands to a root node through a minimum
                  cost tree network, subject to capacity constraints
                  on all links is known as the capacitated minimum
                  spanning tree {(CMST)} problem. As the problem is
                  {NP-hard,} we propose a hybrid ant colony
                  optimization {(ACO)} algorithm to tackle it
                  heuristically. The algorithm exploits two important
                  problem characteristics: (i) the {CMST} problem is
                  closely related to the capacitated vehicle routing
                  problem {(CVRP),} and (ii) given a clustering of
                  client nodes that satisfies capacity constraints,
                  the solution is to find a {MST} for each cluster,
                  which can be done exactly in polynomial time. Our
                  {ACO} exploits these two characteristics of the
                  {CMST} by a solution construction originally
                  developed for the {CVRP.} Given the {CVRP} solution,
                  we then apply an implementation of Prim's algorithm
                  to each cluster to obtain a feasible {CMST}
                  solution. Results from a comprehensive computational
                  study indicate the efficiency and effectiveness of
                  the proposed approach.}
}
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  author = {Zhi-Gang Ren and Zu-Ren Feng and Liang-Jun Ke and Zhao-Jun Zhang},
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  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}
}
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                  machinery for the algorithm selection problem. There is a
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}
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  author = {Juan Carlos Rivera and H. Murat Afsar and  Christian Prins },
  title = {A Multistart Iterated Local Search for the Multitrip Cumulative Capacitated
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  author = { Rivadeneira, Luc{\'i}a  and  Yang, Jian-Bo  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Predicting tweet impact using a novel evidential reasoning
                  prediction method},
  journal = {Expert Systems with Applications},
  year = 2021,
  volume = 169,
  pages = 114400,
  month = may,
  doi = {10.1016/j.eswa.2020.114400},
  abstract = {This study presents a novel evidential reasoning (ER)
                  prediction model called MAKER-RIMER to examine how different
                  features embedded in Twitter posts (tweets) can predict the
                  number of retweets achieved during an electoral campaign. The
                  tweets posted by the two most voted candidates during the
                  official campaign for the 2017 Ecuadorian Presidential
                  election were used for this research. For each tweet, five
                  features including type of tweet, emotion, URL, hashtag, and
                  date are identified and coded to predict if tweets are of
                  either high or low impact. The main contributions of the new
                  proposed model include its suitability to analyse tweet
                  datasets based on likelihood analysis of data. The model is
                  interpretable, and the prediction process relies only on the
                  use of available data. The experimental results show that
                  MAKER-RIMER performed better, in terms of misclassification
                  error, when compared against other predictive machine
                  learning approaches. In addition, the model allows observing
                  which features of the candidates' tweets are linked to high
                  and low impact. Tweets containing allusions to the contender
                  candidate, either with positive or negative connotations,
                  without hashtags, and written towards the end of the
                  campaign, were persistently those with the highest
                  impact. URLs, on the other hand, is the only variable that
                  performs differently for the two candidates in terms of
                  achieving high impact. MAKER-RIMER can provide campaigners of
                  political parties or candidates with a tool to measure how
                  features of tweets are predictors of their impact, which can
                  be useful to tailor Twitter content during electoral
                  campaigns.},
  keywords = {Evidential reasoning rule,Belief rule-based inference,Maximum
                  likelihood data analysis,Twitter,Retweet,Prediction}
}
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}
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                  optimization: the weighting achievement scalarizing function
                  genetic algorithm},
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                  Pareto optimal front. In this paper, we suggest a
                  preference-based EMO algorithm called weighting achievement
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                  expressed by means of a reference point. The main purpose of
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                  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
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                  WASF-GA is shown in several test problems in comparison to
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                  outperformed the other algorithms considered in most of the
                  problems.}
}
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                  Olivier and Durand, Mathieu and Berrini, Elisa and Hauville,
                  Fr{\'e}d{\'e}ric and Astolfi, Jacques-Andr{\'e}},
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  publisher = {Springer},
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                  Support Vector Machine},
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}
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                  J. Fraire},
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                  evolutionary algorithm, adaptive algorithm, fuzzy logic, spatial spread deviation}
}
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}
@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,
  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,
  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{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,
  alias = {Shields2016}
}
@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},
  alias = {ShmHoo05:protein}
}
@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},
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  number = 1,
  pages = {1--25}
}
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}
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}
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@article{SorArnPal2017,
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}
@article{SorOchSotBur2017:ejor,
  author = {Jorge A. Soria-Alcaraz and  Gabriela Ochoa  and Marco A. Sotelo-Figeroa and  Edmund K. Burke },
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@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.}
}
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}
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  title = {The proof and measurement of association between two things},
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  author = {Sprecher, Arno and Hartmann, S{\"o}nke and Drexl, Andreas},
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  volume = 19,
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                  Andreas},
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                  resource-constrained project scheduling problem},
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                  and non-delay schedules. Traditionally these
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                  defined in a rather informal way which does not
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}
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}
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}
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}
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  journal = {Applied Soft Computing},
  keywords = {Evolutionary algorithm,Road traffic,Smart city,Smart
                  mobility,Traffic light,WiFi connections},
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                  bio-inspired algorithms},
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                  the most relevant problems related to smart mobility: the
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                  each vehicle by using several spots located at traffic lights
                  in order to avoid traffic jams by using \{V2I\}
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                  well as several road traffic distributions. We propose an
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                  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
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                  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.}
}
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                  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
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                  upon Tyne, and Sheffield. We trained random forest models to
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                  efficient safe Bayesian optimization algorithm, StageOpt,
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                  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
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                  synthetic experiments, as well as in clinical practice. We
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                  safe optimization approaches, and is able to safely and
                  effectively optimize spinal cord stimulation therapy in our
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                  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
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                  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
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                  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
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                  black-box optimization. The Account covers the following
                  algorithms: Bayesian optimization to optimize the chemical or
                  physical properties, an optimization method using a quantum
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                  learning and boundless objective-free exploration, which may
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                  quality and quantity are key for the success of these
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                  robotics are put forward, automated discovery algorithms
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                  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.}
}
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                  interactive multiobjective optimization, multiple criteria
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                  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
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                  (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.,
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                  e.g., the burdens, cognitive biases and preference
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                  highlighted and discussed. Several promising directions
                  worthy of future research are also presented.}
}
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                  EFQM Excellence Model to conduct business excellence
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                  decision analysis (MCDA) problem. This paper introduces a
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}
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@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}
}
@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{AhoHopUll83:data-structures,
  author = { A. Aho  and  J. Hopcroft  and  J. Ullman },
  title = {Data structures and algorithms},
  publisher = {Addison-Wesley, Reading, MA},
  year = 1983
}
@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,
  epub = {https://dl.acm.org/citation.cfm?id=3321707},
  doi = {10.1145/3321707},
  isbn = {978-1-4503-6111-8},
  address = { New York, NY},
  publisher = {ACM Press},
  year = 2019,
  editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Anne Auger  and  Thomas St{\"u}tzle },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2019},
  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},
  alias = {Ansotegui2009},
  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,
  epub = {https://dl.acm.org/citation.cfm?id=3319619},
  isbn = {978-1-4503-6748-6},
  address = { New York, NY},
  publisher = {ACM Press},
  year = 2019,
  editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Anne Auger  and  Thomas St{\"u}tzle },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO Companion 2019},
  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/},
  alias = {Loshchilov2017}
}
@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},
  alias = {Bartz-Beielstein2010},
  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},
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@incollection{BezLopStu2015moead,
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  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,
  alias = {BezLopStu2017tec-supp}
}
@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,
  epub = {https://dl.acm.org/citation.cfm?id=3321707},
  isbn = {978-1-4503-6111-8},
  address = { New York, NY},
  publisher = {ACM Press},
  year = 2019,
  editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Anne Auger  and  Thomas St{\"u}tzle },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2019},
  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 Bugarín and
                  Jérôme Lang},
  series = {Frontiers in Artificial Intelligence and Applications},
  volume = 325,
  year = 2020,
  booktitle = {Proceedings of the 24th European Conference on Artificial Intelligence (ECAI)},
  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},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 11353,
  editor = { Roberto Battiti  and Mauro Brunato and Ilias Kotsireas and  Panos M. Pardalos },
  year = 2018,
  booktitle = {Learning and Intelligent Optimization, 12th International
                  Conference, LION 12},
  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},
  alias = {BilPar1995}
}
@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},
  alias = {adaptive_techreport}
}
@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},
  alias = {Bir09:book},
  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},
  publisher = {Springer},
  volume = 10079,
  editor = {Paola Festa and  Meinolf Sellmann  and  Joaquin Vanschoren },
  series = {Lecture Notes in Computer Science},
  year = 2016,
  booktitle = {Learning and Intelligent Optimization, 10th International
                  Conference, LION 10},
  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}
}
@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,
  alias = {BluEtAl08:hm-book},
  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},
  alias = {borges1998}
}
@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},
  publisher = {Springer},
  volume = 7997,
  editor = { Panos M. Pardalos  and G. Nicosia},
  series = {Lecture Notes in Computer Science},
  year = 2013,
  booktitle = {Learning and Intelligent Optimization, 7th
                  International Conference, LION 7},
  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},
  alias = {bringmann2009don},
  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,
  epub = {https://dl.acm.org/citation.cfm?id=3319619},
  doi = {10.1145/3319619},
  isbn = {978-1-4503-6748-6},
  address = { New York, NY},
  publisher = {ACM Press},
  year = 2019,
  editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Anne Auger  and  Thomas St{\"u}tzle },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO Companion 2019},
  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,
  epub = {https://dl.acm.org/citation.cfm?id=3319619},
  isbn = {978-1-4503-6748-6},
  address = { New York, NY},
  publisher = {ACM Press},
  year = 2019,
  editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Anne Auger  and  Thomas St{\"u}tzle },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO Companion 2019},
  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,
  epub = {https://dl.acm.org/citation.cfm?id=3319619},
  doi = {10.1145/3319619},
  isbn = {978-1-4503-6748-6},
  address = { New York, NY},
  publisher = {ACM Press},
  year = 2019,
  editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Anne Auger  and  Thomas St{\"u}tzle },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO Companion 2019},
  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},
  publisher = {Springer},
  editor = { Christian Blum  and  Roberto Battiti },
  series = {Lecture Notes in Computer Science},
  volume = 6073,
  year = 2010,
  booktitle = {Learning and Intelligent Optimization, 4th International Conference, LION 4},
  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}
}
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  doi = {10.1190/1.1822162},
  annote = {Proposed a reproducibility taxonomy, defined reproducibility
                  and taxonomy}
}
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  author = { Clerc, Maurice  and  J. Kennedy },
  title = {Standard {PSO} 2011},
  howpublished = {Particle Swarm Central},
  year = 2011,
  url = {http://www.particleswarm.info/},
  alias = {pso:central}
}
@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,
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                  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} },
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                  Design, Optimization and Control in Engineering and Sciences},
  pages = {3--18},
  year = 2015,
  doi = {10.1007/978-3-319-11541-2_1},
  publisher = {Springer}
}
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  year = 2007,
  publisher = {Springer},
  address = { New York, NY},
  edition = {2nd},
  doi = {10.1007/978-0-387-36797-2}
}
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  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},
  alias = {Coe2000cec}
}
@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,
  alias = {Coh95}
}
@incollection{Cohen82,
  author = { G. Cohen },
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}
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  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},
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                  Computing},
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  year = 1971,
  location = {Shaker Heights, Ohio, USA},
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  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},
  alias = {CulLuo96}
}
@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},
  publisher = {Springer},
  volume = 10079,
  editor = {Paola Festa and  Meinolf Sellmann  and  Joaquin Vanschoren },
  series = {Lecture Notes in Computer Science},
  year = 2016,
  booktitle = {Learning and Intelligent Optimization, 10th International
                  Conference, LION 10},
  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,
  publisher = {Springer},
  year = 2019,
  editor = {Nikolaos F. Matsatsinis and Yannis Marinakis and  Panos M. Pardalos },
  booktitle = {Learning and Intelligent Optimization, 13th International
                  Conference, LION 13},
  author = {Nguyen Dang and Patrick {De Causmaecker}},
  title = {Analysis of Algorithm Components and Parameters: Some Case
                  Studies},
  pages = {288--303},
  abstract = {Modern high-performing algorithms are usually highly
                  parameterised, and can be configured either manually or by an
                  automatic algorithm configurator. The algorithm performance
                  dataset obtained after the configuration step can be used to
                  gain insights into how different algorithm parameters
                  influence algorithm performance. This can be done by a number
                  of analysis methods that exploit the idea of learning
                  prediction models from an algorithm performance dataset and
                  then using them for the data analysis on the importance of
                  variables. In this paper, we demonstrate the complementary
                  usage of three methods along this line, namely forward
                  selection, fANOVA and ablation analysis with surrogates on
                  three case studies, each of which represents some special
                  situations that the analyses can fall into. By these
                  examples, we illustrate how to interpret analysis results and
                  discuss the advantage of combining different analysis
                  methods.},
  doi = {10.1007/978-3-030-05348-2_25}
}
@incollection{DanDoe2019gecco,
  epub = {https://dl.acm.org/citation.cfm?id=3321707},
  isbn = {978-1-4503-6111-8},
  address = { New York, NY},
  publisher = {ACM Press},
  year = 2019,
  editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Anne Auger  and  Thomas St{\"u}tzle },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2019},
  author = {Nguyen Dang and  Carola Doerr },
  title = {Hyper-parameter tuning for the ({1 + (\(\lambda\), \(\lambda\))}) {GA}},
  pages = {889--897},
  doi = {10.1145/3321707.3321725},
  keywords = {irace; theory}
}
@incollection{DanPerCauStu2017:gecco,
  address = { New York, NY},
  publisher = {ACM Press},
  year = 2017,
  editor = { Peter A. N. Bosman },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2017},
  author = {Nguyen {Dang Thi Thanh} and   P{\'e}rez C{\'a}ceres, Leslie and Patrick {De Causmaecker} and  Thomas St{\"u}tzle },
  title = {Configuring {\rpackage{irace}} Using Surrogate Configuration Benchmarks},
  pages = {243--250},
  keywords = {irace},
  doi = {10.1145/3071178.3071238}
}
@inproceedings{DanPoz2018,
  year = 2018,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  booktitle = {Proceedings of  the 2018 Congress on Evolutionary Computation (CEC 2018)},
  key = {IEEE CEC},
  title = {A Meta-Learning Algorithm Selection Approach for the Quadratic Assignment Problem},
  author = {Dantas, Augusto Lopez and Pozo, Aurora Trinidad Ramirez},
  pages = {1--8}
}
@incollection{Dandy03,
  author = { Graeme C. Dandy  and  Matthew S. Gibbs },
  editor = {Paul Bizier and Paul DeBarry},
  title = {Optimizing System Operations and Water Quality},
  publisher = {ASCE},
  year = 2003,
  booktitle = {Proceedings of World Water and Environmental
                  Resources Congress},
  address = {Philadelphia, USA},
  doi = {10.1061/40685(2003)127},
  note = {on CD-ROM}
}
@phdthesis{Dang2018PhD,
  title = {Data analytics for algorithm design},
  school = {KU Leuven, Belgium},
  author = {Nguyen {Dang Thi Thanh}},
  year = 2018,
  annote = {Supervised by Patrick {De Causmaecker}}
}
@incollection{DaoVerOchTom2012:gecco,
  address = { New York, NY},
  publisher = {ACM Press},
  year = 2012,
  editor = {Terence Soule and Jason H. Moore},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2012},
  author = {Fabio Daolio and  Verel, S{\'e}bastien  and  Gabriela Ochoa  and Marco Tomassini},
  title = {Local Optima Networks and the Performance of Iterated Local
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@incollection{Deb2008introduction,
  editor = { J{\"u}rgen Branke  and  Kalyanmoy Deb  and  Kaisa Miettinen  and  Roman S{\l}owi{\'n}ski },
  address = { Heidelberg, Germany},
  publisher = {Springer},
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  booktitle = {Multiobjective Optimization: Interactive and Evolutionary
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  title = {Introduction to evolutionary multiobjective optimization},
  author = { Kalyanmoy Deb },
  abstract = {In its current state, evolutionary multiobjective
                  optimization (EMO) is an established field of research and
                  application with more than 150 PhD theses, more than ten
                  dedicated texts and edited books, commercial softwares and
                  numerous freely downloadable codes, a biannual conference
                  series running successfully since 2001, special sessions and
                  workshops held at all major evolutionary computing
                  conferences, and full-time researchers from universities and
                  industries from all around the globe. In this chapter, we
                  provide a brief introduction to EMO principles, illustrate
                  some EMO algorithms with simulated results, and outline the
                  current research and application potential of EMO. For
                  solving multiobjective optimization problems, EMO procedures
                  attempt to find a set of well-distributed Pareto-optimal
                  points, so that an idea of the extent and shape of the