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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

tmpjmtxPvftCo.bib

@comment{{This file has been generated by bib2bib 1.98}}
@comment{{Command line: /usr/bin/bib2bib --warn-error --expand --expand-xrefs authors.bib abbrev.bib journals.bib biblio.bib crossref.bib --remove pdf -ob /tmp/tmpjmtxPvftCo.bib -oc /tmp/citefile4M9In8Gi3v}}
@preamble{{\providecommand{\MaxMinAntSystem}{{$\cal MAX$--$\cal MIN$} {Ant} {System}} } # {\providecommand{\rpackage}[1]{{#1}} } # {\providecommand{\softwarepackage}[1]{{#1}} } # {\providecommand{\proglang}[1]{{#1}} }}
@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{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}
}
@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}}
}
@incollection{Aca2004memaco,
  address = { Heidelberg },
  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 },
  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}
}
@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},
  annote = {\url{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
                  Rodolfo Hern{\'a}ndez Jim{\'e}nez},
  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},
  keyword = {Calibra}
}
@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{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}
}
@incollection{AguTan2009:space,
  address = { Heidelberg },
  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}
}
@inproceedings{Aguirre2013,
  year = 2013,
  address = { New York, NY },
  publisher = {ACM Press},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference Companion, 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}
}
@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}
}
@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
}
@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}
}
@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 },
  publisher = {Springer},
  editor = {T. P. Runarsson 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}
}
@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}
}
@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}
}
@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}
}
@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{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{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}
}
@inproceedings{AliSimHar2019,
  aurl = {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}
}
@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
}
@inproceedings{AllKno2011ecta,
  title = {Evolutionary Search in Lethal Environments},
  author = { Allmendinger, Richard  and  Joshua D. Knowles },
  booktitle = {International Conference on Evolutionary Computation Theory
                  and Applications},
  pages = {63--72},
  year = 2011,
  publisher = {SciTePress},
  doi = {10.5220/0003673000630072},
  eprint = {https://www.scitepress.org/papers/2011/36730/36730.pdf}
}
@phdthesis{Allmendinger2012phd,
  title = {Tuning evolutionary search for closed-loop optimization},
  author = { Allmendinger, Richard },
  year = 2012,
  school = {The University of Manchester, UK}
}
@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}
}
@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.}
}
@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}
}
@inproceedings{AmaAliThr2019nips,
  aurl = {http://papers.nips.cc/book/advances-in-neural-information-processing-systems-32-2019},
  year = 2019,
  editor = {Hanna M. Wallach and Hugo Larochelle and Alina Beygelzimer
                  and Florence d'Alch{\'{e}}{-}Buc and Emily B. Fox and Roman
                  Garnett},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS 32)},
  title = {Linear Stochastic Bandits Under Safety Constraints},
  author = {Amani, Sanae and Alizadeh, Mahnoosh and Thrampoulidis,
                  Christos},
  pages = {9256--9266},
  eprint = {http://papers.nips.cc/paper/9124-linear-stochastic-bandits-under-safety-constraints.pdf}
}
@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
}
@incollection{AndVidIve1993,
  publisher = {Springer},
  year = 1993,
  editor = { Ren{\'e} Victor Valqui Vidal },
  booktitle = {Applied Simulated Annealing},
  title = {Design of a Teleprocessing Communication Network Using Simulated Annealing},
  author = { Klaus Andersen  and  Ren{\'e} Victor Valqui Vidal  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 }
}
@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}
}
@incollection{AngBocPaoVec08,
  year = 2008,
  volume = 5361,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  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}
}
@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}
}
@incollection{Angus2007,
  editor = {Marcus Randall and  Abbass, Hussein A.  and Janet Wiles},
  volume = 4828,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  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}
}
@article{AnsBriGou2002qap,
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  number = 3,
  pages = {563--588},
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                  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.}
}
@inproceedings{AnsKamVeeRag2014open,
  year = 2014,
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}
@incollection{AnsMalSamSelTie2015:ijcai,
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  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++},
  doi = {10.5555/2832249.2832351}
}
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  title = {{MaxSAT} by Improved Instance-Specific Algorithm
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  author = { Carlos Ans{\'o}tegui  and Malitsky, Yuri and  Meinolf Sellmann },
  pages = {2594--2600}
}
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  publisher = {Springer},
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  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},
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  doi = {10.1007/978-3-642-04244-7_14},
  alias = {Ansotegui2009},
  keywords = {GGA}
}
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  title = {The Traveling Salesman Problem: A Computational Study},
  publisher = {Princeton University Press, Princeton, NJ},
  year = 2006
}
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                  D. Espinoza and M. Goycoolea  and  Keld Helsgaun },
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}
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}
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}
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}
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}
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@article{Arr04,
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}
@article{ArrArm05,
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  year = 2005,
  keywords = {Multicriteria Scheduling}
}
@article{ArrLeu2017,
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@incollection{ArzCebPer2019qap,
  aurl = {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 Companion, 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,
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  volume = 26,
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                  Attributes},
  pages = {377--393}
}
@article{Asch01tsptw,
  author = { N. Ascheuer  and  Matteo Fischetti  and  M. Gr{\"o}tschel },
  title = {Solving asymmetric travelling salesman problem with
                  time windows by branch-and-cut},
  journal = {Mathematical Programming},
  year = 2001,
  volume = 90,
  pages = {475--506}
}
@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}
}
@article{Ata2003mik,
  author = { Alper Atamt{\"u}rk },
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}
@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,
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  publisher = {Springer},
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  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}
}
@article{AudDanOrb2014,
  author = { Charles Audet  and  Cong-Kien Dang  and  Dominique Orban },
  title = {Optimization of Algorithms with {OPAL}},
  journal = {Mathematical Programming Computation},
  year = 2014,
  volume = 6,
  number = 3,
  pages = {233--254}
}
@article{AudEgla1977,
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  alias = {Audze1977}
}
@article{AudOrb06:mads,
  author = { Charles Audet  and  Dominique Orban },
  title = {Finding Optimal Algorithmic Parameters Using Derivative-Free
                  Optimization},
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  year = 2006,
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}
@article{Aue2002using,
  title = {Using Confidence Bounds for Exploitation-Exploration
                  Trade-offs},
  author = {Auer, Peter},
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  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},
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  number = {2-3},
  pages = {235--256},
  year = 2002
}
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  address = { New York, NY },
  publisher = {ACM Press},
  year = 2009,
  editor = {Franz Rothlauf},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2009},
  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,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2009,
  editor = {Franz Rothlauf},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2009},
  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,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2009,
  editor = {Franz Rothlauf},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2009},
  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}
}
@article{AugBadBroZit2012tcs,
  author = { Anne Auger  and  Johannes Bader  and  Dimo Brockhoff  and  Eckart Zitzler },
  title = {Hypervolume-based multiobjective optimization:
                  Theoretical foundations and practical implications},
  journal = {Theoretical Computer Science},
  volume = 425,
  year = 2012,
  pages = {75--103},
  doi = {10.1016/j.tcs.2011.03.012}
}
@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
}
@incollection{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}
}
@incollection{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}
}
@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}
}
@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}
}
@inproceedings{AydYavOzyYasStu2017,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2017,
  editor = {Peter A. N. Bosman},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference Companion, 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}
}
@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}
}
@inproceedings{AziDoeDre2021,
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  publisher = {ACM Press},
  year = 2021,
  editor = { Chicano, Francisco  and  Krzysztof Krawiec },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference Companion, GECCO Companion 2021},
  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}
}
@article{AziTay2014eaai,
  author = {Mahdi Aziz and {Tayarani-N}, Mohammad-H.},
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  year = 2014,
  doi = {10.1016/j.engappai.2014.07.021},
  keywords = {F-race}
}
@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{BLTZ2003a,
  address = { Heidelberg },
  publisher = {Springer},
  volume = 2632,
  series = {Lecture Notes in Computer Science},
  shorteditor = { Carlos M. Fonseca  and  others},
  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},
  eprint = {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}
}
@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,
  eprint = {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.}
}
@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,
  title = {Evolutionary algorithms in theory and practice: evolution
                  strategies, evolutionary programming, genetic algorithms},
  author = { Thomas B{\"a}ck },
  year = 1996,
  publisher = {Oxford University Press}
}
@article{BadZit2011ec,
  author = { Johannes Bader  and  Eckart Zitzler },
  title = {{HypE}: An Algorithm for Fast Hypervolume-Based
                  Many-Objective Optimization},
  journal = {Evolutionary Computation},
  volume = 19,
  number = 1,
  year = 2011,
  pages = {45--76},
  doi = {10.1162/EVCO_a_00009}
}
@article{BahComLau2019tre,
  title = {Bi-objective multi-layer location--\hspace{0pt}allocation model for the
                  immediate aftermath of sudden-onset disasters},
  author = {Baharmand, Hossein and Comes, Tina and Lauras, Matthieu},
  journal = {Transportation Research Part E: Logistics and Transportation Review},
  volume = 127,
  pages = {86--110},
  year = 2019,
  doi = {10.1016/j.tre.2019.05.002},
  abstract = {Locating distribution centers is critical for humanitarians
                  in the immediate aftermath of a sudden-onset disaster. A
                  major challenge lies in balancing the complexity and
                  uncertainty of the problem with time and resource
                  constraints. To address this problem, we propose a
                  location-allocation model that divides the topography of
                  affected areas into multiple layers; considers constrained
                  number and capacity of facilities and fleets; and allows
                  decision-makers to explore trade-offs between response time
                  and logistics costs. To illustrate our theoretical work, we
                  apply the model to a real dataset from the 2015 Nepal
                  earthquake response. For this case, our method results in a
                  considerable reduction of logistics costs.}
}
@article{Baker2016reprod,
  title = {Is there a reproducibility crisis?},
  author = {Monya Baker},
  journal = {Nature},
  volume = 533,
  pages = {452--454},
  year = 2016
}
@article{Baker83:tsptw,
  author = {Edward K. Baker},
  title = {An Exact Algorithm for the Time-Constrained
                  Traveling Salesman Problem},
  volume = 31,
  doi = {10.1287/opre.31.5.938},
  number = 5,
  journal = {Operations Research},
  year = 1983,
  pages = {938--945},
  anote = {makespan optimization}
}
@article{BalBea2008,
  title = {Facility location in humanitarian relief},
  author = {Balcik, Burcu and Beamon, Benita M.},
  journal = {International Journal of Logistics},
  volume = 11,
  number = 2,
  pages = {101--121},
  year = 2008,
  publisher = {Taylor \& Francis}
}
@incollection{BalBirStu06,
  address = { Heidelberg },
  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 },
  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}
}
@article{BalBirStuDor2009ejor,
  author = {  Prasanna Balaprakash  and  Mauro Birattari  and  Thomas St{\"u}tzle  and  Marco Dorigo },
  title = {Adaptive Sampling Size and Importance Sampling in Estimation-based
Local Search for the Probabilistic Traveling Salesman Problem},
  journal = {European Journal of Operational Research},
  year = 2009,
  volume = 199,
  number = 1,
  pages = {98--110}
}
@article{BalBirStuDor2010cor,
  author = {  Prasanna Balaprakash  and  Mauro Birattari  and  Thomas St{\"u}tzle  and  Marco Dorigo },
  title = {Estimation-based Metaheuristics for the Probabilistic Travelling Salesman Problem},
  journal = {Computers \& Operations Research},
  year = 2010,
  volume = 37,
  number = 11,
  pages = {1939--1951},
  doi = {10.1016/j.cor.2009.12.005}
}
@article{BalBirStuDor2015coa,
  author = {  Prasanna Balaprakash  and  Mauro Birattari  and  Thomas St{\"u}tzle  and  Marco Dorigo },
  title = {Estimation-based Metaheuristics for the Single Vehicle Routing Problem with Stochastic Demands and Customers},
  journal = {Computational Optimization and Applications},
  year = 2015,
  volume = 61,
  number = 2,
  pages = {463--487},
  doi = {10.1007/s10589-014-9719-z}
}
@article{BalBirStuYuaDor09,
  author = {  Prasanna Balaprakash  and  Mauro Birattari  and  Thomas St{\"u}tzle  and  Zhi Yuan  and  Marco Dorigo },
  title = {Estimation-based Ant Colony Optimization Algorithms
                  for the Probabilistic Travelling Salesman Problem},
  journal = {Swarm Intelligence},
  volume = 3,
  number = 3,
  year = 2009,
  pages = {223--242}
}
@article{BalCar1996,
  author = { Egon Balas  and M. C. Carrera},
  title = {A Dynamic Subgradient-based Branch and Bound
                  Procedure for Set Covering},
  journal = {Operations Research},
  year = 1996,
  volume = 44,
  number = 6,
  pages = {875--890}
}
@article{BalHo1980,
  author = { Egon Balas  and A. Ho},
  title = {Set Covering Algorithms Using Cutting Planes,
                  Heuristics, and Subgradient Optimization: A
                  Computational Study},
  journal = {Mathematical Programming Study},
  year = 1980,
  volume = 12,
  pages = {37--60}
}
@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,
  eprint = {http://joc.journal.informs.org/cgi/content/abstract/13/1/56},
  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}
}
@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}
}
@article{BarBatSenSil2015ijem,
  title = {Improving the Performance of Metaheuristics: An Approach
                  Combining Response Surface Methodology and Racing Algorithms},
  author = {Eduardo Batista de Moraes Barbosa and Edson Luiz
                  Franc{\c{c}}a Senne and Messias Borges Silva},
  journal = {International Journal of Engineering Mathematics},
  year = 2015,
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  doi = {10.1155/2015/167031},
  keywords = {F-race}
}
@article{BarDoeBer2020benchmarking,
  title = {Benchmarking in Optimization: Best Practice and Open Issues},
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                  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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}
@inproceedings{BarFlaKocKon2010spot,
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                  and Konen, Wolfgang},
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  year = 2010,
  address = {Karlsruhe},
  publisher = {KIT Scientific Publishing},
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  pages = {264--273}
}
@article{BarGolKel1995,
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  pages = {9--32},
  doi = {10.1007/BF02430363}
}
@article{BarJohNem1998or,
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@article{BarKwa2020,
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@inproceedings{BarLasPre2005cec,
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  publisher = {IEEE Press},
  month = sep,
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  key = {IEEE CEC},
  author = { Thomas Bartz-Beielstein  and  C. Lasarczyk  and  Mike Preuss },
  title = {Sequential Parameter Optimization},
  pages = {773--780}
}
@incollection{BarLasPre2010emaoa,
  editor = { Thomas Bartz-Beielstein  and  Marco Chiarandini  and  Lu{\'i}s Paquete  and  Mike Preuss },
  year = 2010,
  address = { Berlin, Germany },
  publisher = {Springer},
  booktitle = {Experimental Methods for the Analysis of
                  Optimization Algorithms},
  author = { Thomas Bartz-Beielstein  and  C. Lasarczyk  and  Mike Preuss },
  title = {The Sequential Parameter Optimization Toolbox},
  pages = {337--360},
  keywords = {SPOT},
  doi = {10.1007/978-3-642-02538-9_14}
}
@inproceedings{BarMar2004,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  month = sep,
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  key = {IEEE CEC},
  title = {Tuning search algorithms for real-world applications: A
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  author = { Thomas Bartz-Beielstein  and Markon, Sandor},
  pages = {1111--1118}
}
@inproceedings{BarPea2012aaai,
  year = 2012,
  publisher = {{AAAI} Press},
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  editor = {Jorg Hoffmann and Bart Selman},
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  pages = {698,704}
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@article{BarPea2016,
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}
@inproceedings{BarPre2005em,
  address = {Reykjavik, Iceland},
  editor = { Lu{\'i}s Paquete  and  Marco Chiarandini  and Dario Basso},
  year = {2006},
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  author = { Thomas Bartz-Beielstein  and  Mike Preuss },
  title = {Considerations of budget allocation for sequential parameter
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@incollection{BarPre2014experimental,
  address = {Berlin\slash Heidelberg},
  publisher = {Springer},
  year = 2014,
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  editor = {Borenstein, Yossi and  A. Moraglio },
  author = { Thomas Bartz-Beielstein  and  Mike Preuss },
  title = {Experimental Analysis of Optimization Algorithms: Tuning and
                  Beyond},
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}
@inproceedings{BarSch03,
  author = { Benjam{\'i}n Bar{\'a}n  and  Matilde Schaerer },
  title = {A multiobjective ant colony system for vehicle
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                  Conference on Applied Informatics},
  pages = {97--102},
  year = 2003,
  address = {Insbruck, Austria}
}
@article{BarZae2017model,
  author = { Thomas Bartz-Beielstein  and  Martin Zaefferer },
  title = {Model-based methods for continuous and discrete global
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  year = 2017,
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  author = {Basseur, Matthieu and   Go{\"e}ffon, Adrien  and  Arnaud Liefooghe  and  Verel, S{\'e}bastien },
  title = {On Set-based Local Search for Multiobjective Combinatorial
                  Optimization},
  pages = {471--478},
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  acmid = 2463430
}
@book{BatBruMas08:book,
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  year = 2008,
  series = {Operations Research/Computer Science Interfaces},
  volume = 45,
  doi = {10.1007/978-0-387-09624-7}
}
@inproceedings{BatCam2009reactive,
  address = {Hamburg, Germany},
  publisher = {University of Hamburg},
  editor = {M. Caserta and  Stefan Vo{\ss} },
  year = 2010,
  booktitle = {Proceedings of MIC 2009, the 8th Metaheuristics International Conference},
  title = {Reactive search optimization: Learning while optimizing. An
                  experiment in interactive multi-objective optimization},
  author = { Roberto Battiti  and  Paolo Campigotto }
}
@article{BatPas2010tec,
  author = { Roberto Battiti  and  Andrea Passerini },
  title = {Brain-Computer Evolutionary Multiobjective Optimization: A
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                  DTLZ8 in \cite{DebThiLau2005dtlz}}
}
@article{BatPro1996:jea,
  author = { Roberto Battiti  and M. Protasi},
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  year = 1997,
  volume = 2
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@inproceedings{BatSchUrl2014patat,
  publisher = {PATAT},
  year = 2014,
  editor = { Ender {\"O}zcan  and Edmund K. Burke and Barry McCollum},
  booktitle = {PATAT 2014: Proceedings of the 10th International Conference of the Practice and Theory of Automated Timetabling},
  author = {Michele Battistutta and Andrea Schaerf and  Tommaso Urli },
  title = {Feature-based tuning of single-stage simulated annealing for examination timetabling},
  pages = {53--61},
  keywords = {F-race}
}
@article{BatSchUrl2017,
  author = {Michele Battistutta and Andrea Schaerf and  Tommaso Urli },
  title = {Feature-based Tuning of Single-stage Simulated Annealing for
                  Examination Timetabling},
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  year = 2017,
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  number = 2,
  pages = {239--254}
}
@article{BatTec1994:cma,
  title = {Simulated annealing and Tabu search in the long run: A
                  comparison on {QAP} tasks},
  author = { Roberto Battiti  and  Tecchiolli, Giampietro },
  journal = {Computer and Mathematics with Applications},
  volume = 28,
  number = 6,
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  year = 1994,
  publisher = {Elsevier},
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}
@article{BatTec1994:orsa,
  author = { Roberto Battiti  and  Tecchiolli, Giampietro },
  title = {The Reactive Tabu Search},
  journal = {ORSA Journal on Computing},
  year = 1994,
  volume = 6,
  number = 2,
  pages = {126--140}
}
@article{BatTec1996aor,
  title = {The continuous reactive tabu search: blending combinatorial optimization and stochastic search for global optimization},
  author = { Roberto Battiti  and  Tecchiolli, Giampietro },
  journal = {Annals of Operations Research},
  volume = 63,
  number = 2,
  pages = {151--188},
  year = 1996
}
@unpublished{Bau1986a,
  author = {E. B. Baum},
  title = {Iterated Descent: A Better Algorithm for Local
                  Search in Combinatorial Optimization Problems},
  note = {Manuscript},
  year = 1986
}
@inproceedings{Bau1986b,
  author = {E. B. Baum},
  title = {Towards Practical ``Neural'' Computation for Combinatorial
                  Optimization Problems},
  booktitle = {Neural Networks for Computing, AIP Conference Proceedings},
  year = 1986,
  pages = {53--64}
}
@article{BauPer2007:ejor,
  author = { J. Bautista  and  J. Pereira },
  title = {Ant algorithms for a time and space constrained
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  journal = {European Journal of Operational Research},
  year = 2007,
  volume = 177,
  number = 3,
  pages = {2016--2032},
  doi = {10.1016/j.ejor.2005.12.017}
}
@article{Baumol1962,
  author = {Baumol, William J.},
  title = {Management models and industrial applications of linear
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  year = 1962
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  author = {John Baxter},
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@inproceedings{BayDerSab2005cie,
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                  approach to assembly line balancing problems},
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                  Industrial Engineering (CIE35)},
  pages = {263--268},
  year = 2005,
  address = {Istanbul, Turkey}
}
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  title = {A Genetic Algorithm for the Multidimensional Knapsack Problem},
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  volume = 4,
  number = 1,
  pages = {63--86}
}
@inproceedings{BeaGra2002,
  title = {Improved Distributed Hypercube Sampling},
  author = {Beachkofski, Brian and Grandhi, Ramana},
  booktitle = {Proceedings of the 43rd AIAA/ASME/ASCE/AHS/ASC Structures,
                  Structural Dynamics, and Materials Conference},
  year = 2002,
  organization = {AIAA paper 2002-1274},
  publisher = {American Institute of Aeronautics and Astronautics},
  alias = {Beachkofski2002}
}
@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}}
}
@techreport{Beasley1998,
  title = {Heuristic algorithms for the unconstrained binary quadratic
                  programming problem},
  author = { John E. Beasley },
  address = {London, England},
  year = 1998,
  institution = {The Management School, Imperial College}
}
@inproceedings{BecGotStu05,
  address = { Heidelberg },
  publisher = {Springer},
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  volume = 3871,
  editor = { Talbi, El-Ghazali  and Pierre Liardet and Pierre
                  Collet and Evelyne Lutton and Marc Schoenauer},
  year = 2005,
  booktitle = {Artificial Evolution},
  author = {S. Becker and J. Gottlieb and  Thomas St{\"u}tzle },
  title = {Applications of Racing Algorithms: An Industrial
                  Perspective},
  pages = {271--283}
}
@book{BedBai1982,
  title = {Integrated Production Control Systems: Management, Analysis,
                  Design},
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  volume = 2,
  year = 1982,
  publisher = {John Wiley \& Sons},
  address = { New York, NY }
}
@inproceedings{BehAffWag2017,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2017,
  editor = {Peter A. N. Bosman},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference Companion, GECCO Companion 2017},
  title = {Instance-based algorithm selection on quadratic assignment problem landscapes},
  author = {Beham, Andreas and Affenzeller, Michael and Wagner, Stefan},
  pages = {1471--1478}
}
@article{BehFat2011,
  author = {J. Behnamian and S.M.T. {Fatemi Ghomi}},
  title = {Hybrid Flowshop Scheduling with Machine and Resource-dependent Processing Times},
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  year = 2011,
  volume = 35,
  number = 3,
  pages = {1107--1123}
}
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  author = {Richard Bellman},
  title = {The theory of dynamic programming},
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}
@incollection{BelBraEsk2088moointevo,
  editor = { J{\"u}rgen Branke  and  Kalyanmoy Deb  and  Kaisa Miettinen  and  Roman S{\l}owi{\'n}ski },
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 5252,
  year = 2008,
  booktitle = {Multiobjective Optimization: Interactive and Evolutionary
                  Approaches},
  author = {Belton, Valerie and  J{\"u}rgen Branke  and Eskelinen,
                  Petri and  Salvatore Greco  and Molina, Juli\'{a}n and
                  Ruiz, Francisco and  Roman S{\l}owi{\'n}ski },
  title = {Interactive Multiobjective Optimization from a
                  Learning Perspective},
  pages = {405--433},
  doi = {10.1007/978-3-540-88908-3_15}
}
@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}
}
@inproceedings{BelDreSavSch2016:ppsn,
  isbn = {978-3-319-45822-9},
  doi = {10.1007/978-3-319-45823-6},
  year = 2016,
  volume = 9921,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { Julia Handl  and  Emma Hart  and  Lewis, P. R.  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Gabriela Ochoa  and  Ben Paechter },
  booktitle = {Parallel Problem Solving from Nature -- PPSN XIV},
  author = {Nacim Belkhir and  Johann Dr{\'e}o  and Pierre Sav{\'{e}}ant and  Marc Schoenauer },
  title = {Feature Based Algorithm Configuration: A Case Study with Differential Evolution},
  pages = {156--166}
}
@incollection{BelDreSavSch2017:gecco,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2017,
  editor = {Peter A. N. Bosman},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2017},
  author = {Nacim Belkhir and  Johann Dr{\'e}o  and Pierre Sav{\'{e}}ant and  Marc Schoenauer },
  title = {Per Instance Algorithm Configuration of {CMA-ES} with Limited Budget},
  pages = {681--688}
}
@inproceedings{Ben90,
  author = {J. L. Bentley},
  title = {Experiments on Traveling Salesman Heuristics},
  booktitle = {Proceedings of the First Annual ACM-SIAM Symposium
                  on Discrete Algorithms},
  year = 1990,
  editor = {David S. Johnson},
  publisher = {Society for Industrial and Applied Mathematics, Philadelphia, PA, USA},
  pages = {91--99}
}
@article{Ben92,
  author = {J. L. Bentley},
  title = {Fast Algorithms for Geometric Traveling Salesman
                  Problems},
  journal = {ORSA Journal on Computing},
  year = 1992,
  volume = 4,
  number = 4,
  pages = {387--411}
}
@incollection{BenCasLus2020interactive,
  publisher = {{AAAI} Press},
  isbn = {978-1-57735-823-7},
  year = 2020,
  booktitle = {Proceedings of  the {AAAI} Conference on Artificial Intelligence},
  key = {AAAI2020},
  doi = {10.1609/aaai.v34i03.5612},
  pages = {2335--2342},
  author = {Nawal Benabbou and Cassandre Leroy and  Thibaut Lust },
  title = {An Interactive Regret-Based Genetic Algorithm for Solving
                  Multi-Objective Combinatorial Optimization Problems},
  keywords = {interactive, multi-objective, decision-makers}
}
@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
}
@incollection{BenRit2015:icaps,
  year = 2015,
  address = { Menlo Park, CA },
  publisher = {{AAAI} Press},
  booktitle = {Proceedings of the Twenty-Fifth International Conference on
                  Automated Planning and Scheduling, {ICAPS} 2015},
  editor = { Ronen I. Brafman  and Carmel Domshlak and Patrik Haslum and  Shlomo Zilberstein },
  author = {Alexander Javier Benavides and  Marcus Ritt},
  title = {Iterated Local Search Heuristics for Minimizing Total Completion Time
                 in Permutation and Non-permutation Flow Shops},
  pages = {34--41},
  keyword = {irace}
}
@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}
}
@incollection{BenRolBlu2010,
  doi = {10.1007/978-3-642-13800-3},
  address = { Heidelberg },
  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},
  author = {Stefano Benedettini and  Andrea Roli  and  Christian Blum },
  title = {A Randomized Iterated Greedy Algorithm for the Founder Sequence Reconstruction
               Problem},
  pages = {37--51}
}
@incollection{BenRolDig2008ants,
  volume = 5217,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  editor = { Marco Dorigo  and others },
  fulleditor = { Marco Dorigo  and  Mauro Birattari  and  Christian Blum  and M. Clerc and  Thomas St{\"u}tzle  and A. F. T. Winfield},
  year = 2008,
  booktitle = {Ant Colony Optimization and Swarm Intelligence, 6th
                  International Conference, ANTS 2008},
  author = {Stefano Benedettini and  Andrea Roli  and Luca {Di Gaspero}},
  title = {Two-level {ACO} for Haplotype Inference under Pure Parsimony},
  pages = {179--190},
  alias = {ants2008-benedigaroli}
}
@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}
}
@book{Ber95,
  author = {D. Bertsekas},
  title = {Dynamic Programming and Optimal Control},
  publisher = {Athena Scientific, Belmont, MA},
  year = 1995
}
@book{Ber98,
  author = {D. Bertsekas},
  title = {Network Optimization: Continuous and Discrete Models},
  publisher = {Athena Scientific, Belmont, MA},
  year = 1998
}
@incollection{BerBarBen2011nips,
  publisher = {Curran Associates, Red Hook, NY},
  year = 2011,
  editor = {J. Shawe-Taylor and R. S. Zemel and P. L. Bartlett and
                  F. Pereira and K. Q. Weinberger},
  booktitle = {Advances in Neural Information Processing Systems (NIPS 24)},
  title = {Algorithms for Hyper-Parameter Optimization},
  author = { James S. Bergstra  and Bardenet, R\'{e}mi and  Bengio, Yoshua  and Bal\'{a}zs K\'{e}gl},
  pages = {2546--2554},
  url = {http://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf}
}
@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.},
  eprint = {http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf}
}
@inproceedings{BerDorLanSerGam1996:icec,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  year = 1996,
  editor = { Thomas B{\"a}ck  and  T. Fukuda  and  Zbigniew Michalewicz },
  booktitle = {Proceedings of the 1996 IEEE International Conference on
                  Evolutionary Computation (ICEC'96)},
  author = { Hughes Bersini  and  Marco Dorigo  and  S. Langerman  and  G. Seront  and  L. M. Gambardella },
  title = {Results of the First International Contest on
                  Evolutionary Optimisation},
  pages = {611--615}
}
@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},
  year = 2016,
  url = {http://arxiv.org/abs/1602.04450},
  journal = {Arxiv preprint arXiv:1602.04450}
}
@inproceedings{BerSchKra2016safe,
  title = {Safe controller optimization for quadrotors with {Gaussian
}                  processes},
  author = {Berkenkamp, Felix and Schoellig, Angela P. and Krause,
                  Andreas},
  booktitle = {2016 IEEE International Conference on Robotics and Automation
                  (ICRA)},
  pages = {491--496},
  year = 2016,
  organization = {IEEE},
  doi = {10.1109/ICRA.2016.7487170}
}
@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}
}
@inproceedings{BerYasCox2013,
  url = {http://jmlr.org/proceedings/papers/v28/},
  year = 2013,
  volume = 28,
  booktitle = {Proceedings of  the 30th International Conference on Machine Learning, {ICML} 2013},
  editor = {Dasgupta, Sanjoy and McAllester, David},
  title = {Making a science of model search: Hyperparameter optimization
                  in hundreds of dimensions for vision architectures},
  author = { James S. Bergstra  and Yasmin, Daniel and Cox, David},
  pages = {115-123}
}
@inproceedings{BesStuDor00:ppsn,
  anote = {IC.29},
  address = { Heidelberg },
  publisher = {Springer},
  editor = { Marc Schoenauer  and others},
  aeditor = { Marc Schoenauer  and  Kalyanmoy Deb  and  G{\"u}nther Rudolph  and  Xin Yao  and E. Lutton and  Juan-Juli{\'a}n Merelo  and  Hans-Paul Schwefel },
  year = 2000,
  volume = 1917,
  series = {Lecture Notes in Computer Science},
  booktitle = {Parallel Problem Solving from Nature -- PPSN VI},
  author = { Matthijs L. {den Besten}  and  Thomas St{\"u}tzle  and  Marco Dorigo },
  title = {Ant Colony Optimization for the Total Weighted
                  Tardiness Problem},
  pages = {611--620}
}
@incollection{BesStuDor2001:evoworkshops,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 2037,
  editor = {E. J. W. Boers and others},
  aeditor = {E. J. W. Boers and J. Gottlieb and P. L. Lanzi and R. E. Smith
                 and S. Cagnoni and E. Hart and G. R. Raidl and H. Tijink},
  year = 2001,
  booktitle = {Applications of Evolutionary Computing,
                  Proceedings of  EvoWorkshops 2001},
  author = { Matthijs L. {den Besten}  and  Thomas St{\"u}tzle  and  Marco Dorigo },
  title = {Design of Iterated Local Search Algorithms: An
                  Example Application to the Single Machine Total
                  Weighted Tardiness Problem},
  pages = {441--452}
}
@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}
}
@inproceedings{BeuRud06:hypervolume,
  author = { Nicola Beume  and  G{\"u}nther Rudolph },
  title = {Faster {S}-Metric Calculation by Considering
                  Dominated Hypervolume as {Klee's} Measure Problem},
  booktitle = {Proceedings of the Second IASTED Conference on
                  Computational Intelligence},
  editor = {B. Kovalerchuk},
  pages = {231--236},
  publisher = {ACTA Press, Anaheim},
  year = 2006
}
@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}
}
@incollection{BezLopStu2012:ants,
  volume = 7461,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  editor = { Marco Dorigo  and others },
  year = 2012,
  booktitle = {Swarm Intelligence, 8th International Conference, ANTS 2012},
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Generation of Multi-Objective {ACO}
                  Algorithms for the Biobjective Knapsack},
  pages = {37--48},
  doi = {10.1007/978-3-642-32650-9_4},
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2012-008/}
}
@misc{BezLopStu2012:ants-supp,
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Generation of {MOACO} Algorithms for the Biobjective Bidimensional Knapsack Problem: Supplementary material},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2012-008/}},
  year = 2012
}
@misc{BezLopStu2013:evocop-supp,
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {An Analysis of Local Search for the Bi-objective Bidimensional Knapsack: Supplementary material},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2012-016/}},
  year = 2013
}
@misc{BezLopStu2013:lion-supp,
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Deconstructing Multi-Objective Evolutionary Algorithms: An Iterative Analysis on the Permutation Flowshop: Supplementary material},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2013-010/}},
  year = 2013
}
@incollection{BezLopStu2013evocop,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  year = 2013,
  volume = 7832,
  booktitle = {Proceedings of EvoCOP 2013 -- 13th European Conference on Evolutionary Computation in Combinatorial Optimization },
  editor = { Martin Middendorf  and  Christian Blum },
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {An Analysis of Local Search for the Bi-objective
                  Bidimensional Knapsack Problem},
  pages = {85--96},
  doi = {10.1007/978-3-642-37198-1_8}
}
@techreport{BezLopStu2014:automoeaTR,
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Com\-ponent-Wise Design of Multi-Objective
                  Evolutionary Algorithms},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2014,
  number = {TR/IRIDIA/2014-012},
  month = aug
}
@incollection{BezLopStu2014:lion,
  address = { Heidelberg },
  publisher = {Springer},
  volume = 8426,
  editor = { Panos M. Pardalos  and  Mauricio G. C. Resende  and Chrysafis Vogiatzis and Jose
                  L. Walteros},
  series = {Lecture Notes in Computer Science},
  year = 2014,
  booktitle = {Learning and Intelligent Optimization, 8th International Conference, LION 8},
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Deconstructing Multi-Objective Evolutionary Algorithms: An
                  Iterative Analysis on the Permutation Flowshop},
  pages = {57--172},
  doi = {10.1007/978-3-319-09584-4_16},
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2013-010/}
}
@incollection{BezLopStu2014:ppsn,
  year = 2014,
  volume = 8672,
  address = { Heidelberg },
  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 = {PPSN 2014},
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Design of Evolutionary Algorithms for
                  Multi-Objective Combinatorial Optimization},
  doi = {10.1007/978-3-319-10762-2_50},
  pages = {508--517}
}
@misc{BezLopStu2014:ppsn-supp,
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Design of Evolutionary Algorithms for Multi-Objective Combinatorial Optimization},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2014-007/}},
  year = 2014
}
@misc{BezLopStu2015:supp,
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Component-Wise Design of Multi-Objective Evolutionary Algorithms},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2014-010/}},
  year = 2015
}
@misc{BezLopStu2015emoDEsupp,
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {To {DE} or Not to {DE}? {Multi}-objective Differential
                  Evolution Revisited from a Component-Wise Perspective: {Supplementary} material},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2015-001/}},
  year = 2015
}
@incollection{BezLopStu2015emode,
  editor = { Ant{\'o}nio Gaspar{-}Cunha  and Carlos Henggeler Antunes and  Carlos A. {Coello Coello} },
  volume = 9018,
  year = 2015,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2015 Part {I}},
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {To {DE} or Not to {DE}? {Multi}-objective Differential
                  Evolution Revisited from a Component-Wise Perspective},
  pages = {48--63},
  doi = {10.1007/978-3-319-15934-8_4}
}
@inproceedings{BezLopStu2015moead,
  editor = { Ant{\'o}nio Gaspar{-}Cunha  and Carlos Henggeler Antunes and  Carlos A. {Coello Coello} },
  volume = 9018,
  year = 2015,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2015 Part {I}},
  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}
}
@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},
  doi = {10.1109/TEVC.2015.2474158},
  year = 2016,
  volume = 20,
  number = 3,
  pages = {403--417},
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2014-010/}
}
@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
}
@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.}
}
@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,
  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{BezLopStu2017emo-supp,
  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: Supplementary material},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2016-006/}},
  year = 2016
}
@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 = {http://iridia.ulb.ac.be/supp/IridiaSupp2016-004/},
  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.}
}
@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{http://iridia.ulb.ac.be/supp/IridiaSupp2016-004/}},
  year = 2019,
  alias = {BezLopStu2017tec-supp}
}
@incollection{BezLopStu2019gecco,
  aurl = {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:
                  Supplementary material},
  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 }
}
@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
}
@inproceedings{BiaGamDor02:ppsn,
  anote = {IC.34},
  address = { Heidelberg },
  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 = {ECAI 2020},
  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},
  doi = {10.3233/FAIA200122},
  pages = {427--434}
}
@inproceedings{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 Eggensperger,
                  Katharina and  Frank Hutter  and  Chris Fawcett  and  Holger H. Hoos },
  title = {Efficient Parameter Importance Analysis via Ablation with
                  Surrogates},
  url = {https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14750}
}
@inproceedings{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},
  title = {Cave: Configuration assessment, visualization and evaluation},
  author = { Biedenkapp, Andr{\'e}  and Marben, Joshua and  Marius Thomas Lindauer  and  Frank Hutter },
  pages = {115--130}
}
@incollection{BilPar1995:aisb,
  year = 1995,
  volume = 993,
  address = { Berlin, Germany },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  booktitle = {Evolutionary Computing, AISB Workshop},
  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}
}
@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}
}
@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}
}
@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}
}
@incollection{BirChiSaeStu2011,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = {T. Berthold and A. M. Gleixner and S. Heinz and T. Koch},
  year = 2011,
  booktitle = {Integration of AI and OR Techniques in Contraint Programming
                  for Combinatorial Optimization Problems},
  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 },
  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.}
}
@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}
}
@incollection{BirStuPaqVar02:gecco,
  publisher = {Morgan Kaufmann Publishers, San Francisco, CA},
  editor = {W. B. Langdon 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},
  eprint = {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}
}
@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}
}
@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}
}
@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 Izzo, Dario 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}
}
@article{BisIzzYam2010:pagmo-arxiv,
  title = {A Global Optimisation Toolbox for Massively Parallel
                  Engineering Optimisation},
  author = {Biscani, Francesco and Izzo, Dario 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{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},
  eprint = {http://jmlr.org/papers/v17/15-066.html}
}
@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}
}
@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
}
@incollection{BleBlu04:disjoint,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { G{\"u}nther R. Raidl  and others},
  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 P. Machado and E. Marchiori and
                  R. Rothlauf and G. D. Smith and G. Squillero},
  volume = 3005,
  year = 2004,
  booktitle = {Applications of Evolutionary Computing, Proceedings of EvoWorkshops 2004},
  author = { Mar{\'i}a J. Blesa  and  Christian Blum },
  title = {Ant Colony Optimization for the Maximum
                  Edge-Disjoint Paths Problem},
  pages = {160--169}
}
@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}
}
@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 Machado,
                  P. 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{BloPerJouKesHoo2017:emo,
  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,
  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}
}
@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}
}
@incollection{BluBauPer06:ants,
  address = { Heidelberg },
  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}}
}
@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}
}
@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}
}
@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
}
@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 },
  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}
}
@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}
}
@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}
}
@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 }
}
@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}
}
@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}
}
@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}
}
@incollection{BluYab06:hm,
  address = { Heidelberg },
  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}
}
@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}
}
@phdthesis{Boese1996,
  author = {K. D. Boese},
  title = {Models for Iterative Global Optimization},
  school = {University of California, Computer Science Department,
Los Angeles, CA},
  year = 1996
}
@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}
}
@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}
}
@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
}
@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 }
}
@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}
}
@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}
}
@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}
}
@inproceedings{BosGuyVap1992,
  year = 1992,
  publisher = {ACM Press},
  booktitle = {COLT'92},
  editor = {David Haussler},
  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}
}
@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}
}
@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{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  Milosz Kadzinski  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{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}
}
@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}
}
@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, FR }
}
@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}
}
@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}
}
@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}
}
@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}
}
@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
}
@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}
}
@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 },
  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}
}
@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}
}
@incollection{BreSch2011ea,
  address = { Heidelberg },
  publisher = {Springer},
  editor = {Hao, Jin-Kao and Legrand, Pierrick and Collet, Pierre and
                  Monmarch{\'e}, Nicolas and Lutton, Evelyne and Schoenauer,
                  Marc},
  shorteditor = {Hao, Jin-Kao and others},
  volume = 7401,
  series = {Lecture Notes in Computer Science},
  year = 2012,
  booktitle = {Artificial Evolution: 10th International Conference, Evolution Artificielle, EA, 2011},
  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}
}
@inproceedings{BreSch2011gecco,
  year = 2011,
  address = { New York, NY },
  publisher = {ACM Press},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference Companion, 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}
}
@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}
}
@incollection{BriFri2009emo,
  address = { Heidelberg },
  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}
}
@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}
}
@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},
  eprint = {http://docs.theinf.uni-jena.de/paper/2011GECCO.pdf}
}
@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},
  eprint = {http://docs.theinf.uni-jena.de/paper/2012GECCO_Hyp.pdf}
}
@inproceedings{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}
}
@incollection{BrinFriNeuWag2011,
  publisher = {IJCAI/AAAI Press, Menlo Park, CA},
  editor = {Toby Walsh},
  year = 2011,
  booktitle = {Proceedings of the Twenty-Second 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,
  editor = { Ant{\'o}nio Gaspar{-}Cunha  and Carlos Henggeler Antunes and  Carlos A. {Coello Coello} },
  volume = 9018,
  year = 2015,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2015 Part {I}},
  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}
}
@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}
}
@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}
}
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                  Hierarchical Reinforcement Learning},
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  url = {https://arxiv.org/abs/1012.2599}
}
@incollection{BroLopNau2012ppsn,
  volume = 7491,
  year = 2012,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { Carlos A. {Coello Coello}  and others},
  booktitle = {Parallel Problem Solving from Nature, PPSN XII},
  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.}
}
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  pages = {611--627}
}
@inproceedings{BruRit2018cec,
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  publisher = {IEEE Press},
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  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}
}
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  publisher = {Springer},
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  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}
}
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  pages = {24--35}
}
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  volume = 89,
  pages = {319--328},
  alias = {ASrank99}
}
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}
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  title = {The Late Acceptance Hill-Climbing Heuristic},
  institution = {University of Stirling},
  number = {CSM-192},
  year = 2012
}
@article{BurByk2017,
  title = {The Late Acceptance Hill-Climbing Heuristic},
  author = { Edmund K. Burke  and  Yuri Bykov },
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  number = 1,
  pages = {70--78},
  year = 2017
}
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}
@article{BurFraMos2004:joh,
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  year = 2004,
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}
@article{BurGenHyd2013,
  author = { Edmund K. Burke  and  Michel Gendreau  and  Matthew R. Hyde  and  Graham Kendall  and  Gabriela Ochoa  and  Ender {\"O}zcan  and  Rong Qu },
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}
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  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}
}
@article{BurHydKen2010tec,
  author = { Edmund K. Burke  and  Matthew R. Hyde  and  Graham Kendall  and John R. Woodward},
  journal = {IEEE Transactions on Evolutionary Computation},
  title = {A Genetic Programming Hyper-Heuristic Approach for Evolving 2-D Strip Packing Heuristics},
  year = 2010,
  volume = 14,
  number = 6,
  pages = {942--958},
  doi = {10.1109/TEVC.2010.2041061}
}
@article{BurHydKen2012tec,
  author = { Edmund K. Burke  and  Matthew R. Hyde  and  Graham Kendall },
  title = {Grammatical Evolution of Local Search Heuristics},
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  volume = 16,
  number = 7,
  year = 2012,
  pages = {406--417},
  doi = {10.1109/TEVC.2011.2160401}
}
@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}
}
@article{BurKarRen1997,
  title = {{QAPLIB}--a Quadratic Assignment Problem Library},
  author = { Burkard, Rainer E.  and  Stefan E. Karisch  and  Franz Rendl },
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  volume = 10,
  number = 4,
  pages = {391--403},
  year = 1997,
  publisher = {Springer}
}
@article{BurRen1984,
  author = { Burkard, Rainer E.  and  Franz Rendl },
  title = {A Thermodynamically Motivated Simulation Procedure for
                  Combinatorial Optimization Problems},
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  number = 2,
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}
@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}
}
@article{BusRobTot2014,
  author = {Erika Buson and Roberto Roberti and  Paolo Toth },
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  number = 5,
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@incollection{Buz2019signif,
  aurl = {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 Companion, 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},
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  year = 2017
}
@misc{CPLEX,
  author = {IBM},
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}
@article{CabLuqMol2002promoin,
  author = {Caballero, R. and  Mariano Luque  and Molina, J. and Ruiz, F.},
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  pages = {635--656},
  keywords = {preferences, multi interactive methods framework}
}
@article{CacStu2017:endm,
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}
@article{CahMelTal2004,
  author = {Cahon, Sebastien and Melab, Nordine and  Talbi, El-Ghazali },
  title = {{ParadisEO}: A Framework for the Reusable Design of
                  Parallel and Distributed Metaheuristics},
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}
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  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
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  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}
}
@article{CamDorStu2019si,
  author = {Camacho-Villal\'{o}n, Christian Leonardo and  Marco Dorigo  and  Thomas St{\"u}tzle },
  title = {The intelligent water drops algorithm: why it cannot be considered a novel algorithm},
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  volume = 13,
  pages = {173--192}
}
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  doi = {10.1007/978-3-642-13800-3},
  address = { Heidelberg },
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  title = {Adapting to a realistic decision maker: experiments
                  towards a reactive multi-objective optimizer},
  author = { Paolo Campigotto  and  Andrea Passerini },
  pages = {338--341}
}
@article{CamSchXia2013sentim,
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  year = 2013,
  title = {New avenues in opinion mining and sentiment analysis},
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  number = 2,
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}
@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
}
@article{CamWan2020sample,
  author = {Felipe Campelo and  Wanner, Elizabeth F. },
  title = {Sample size calculations for the experimental comparison of
                  multiple algorithms on multiple problem instances},
  journal = {Journal of Heuristics},
  year = 2020,
  doi = {10.1007/s10732-020-09454-w}
}
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  year = 2017
}
@article{Cap2017,
  title = {Variable Neighborhood Search for Extremal Vertices : The {AutoGraphiX-III} System},
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  address = {Seville, Spain}
}
@incollection{CarPinOli2017recipe,
  address = { Heidelberg },
  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 L. Pappa},
  title = {{RECIPE:} {A} Grammar-Based Framework for Automatically
                  Evolving Classification Pipelines},
  pages = {246--261},
  doi = {10.1007/978-3-319-55696-3_16}
}
@inproceedings{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.}
}
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  year = 1999
}
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}
@article{CebIruMen2014eda,
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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
                  solve the permutation flowshop scheduling problem. A hybrid
                  approach consisting of the new estimation of distribution
                  algorithm and a variable neighborhood search is
                  proposed. Conducted experiments demonstrate that the proposed
                  algorithm is able to outperform the state-of-the-art
                  approaches. Moreover, from the 220 benchmark instances
                  tested, the proposed hybrid approach obtains new best known
                  results in 152 cases. An in-depth study of the results
                  suggests that the successful performance of the introduced
                  approach is due to the ability of the generalized Mallows
                  estimation of distribution algorithm to discover promising
                  regions in the search space.},
  doi = {10.1109/TEVC.2013.2260548},
  journal = {IEEE Transactions on Evolutionary Computation},
  keywords = {Estimation of distribution algorithms,Generalized Mallows
                  model,Permutation flowshop scheduling
                  problem,Permutations-based optimization problems},
  number = 2,
  pages = {286--300},
  volume = 18,
  year = 2014
}
@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}
}
@article{Cer85,
  author = {Vladim{\'i}r \v{C}ern\'y},
  title = {A Thermodynamical Approach to the Traveling Salesman Problem: An Efficient Simulation Algorithm},
  journal = {Journal of Optimization Theory and Applications},
  year = 1985,
  volume = 45,
  number = 1,
  pages = {41--51}
}
@article{CesDiGSch2012,
  title = {Design, Engineering, and Experimental Analysis of a Simulated Annealing Approach to the Post-Enrolment Course Timetabling Problem},
  author = { Sara Ceschia  and Luca {Di Gaspero} and Andrea Schaerf},
  journal = {Computers \& Operations Research},
  volume = 39,
  number = 7,
  pages = {1615--1624},
  year = 2012,
  publisher = {Elsevier}
}
@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}
}
@article{CesSch2012aim,
  author = { Sara Ceschia  and Andrea Schaerf},
  title = {Modeling and solving the dynamic patient admission scheduling problem under uncertainty},
  journal = {Artificial Intelligence in Medicine},
  volume = 56,
  number = 3,
  pages = {199--205},
  year = 2012,
  doi = {10.1016/j.artmed.2012.09.001},
  keywords = {F-race}
}
@article{CesSchStu2013:cie,
  author = { Sara Ceschia  and Andrea Schaerf and  Thomas St{\"u}tzle },
  title = {Local Search Techniques for a Routing-packing Problem},
  journal = {Computers and Industrial Engineering},
  year = 2013,
  volume = 66,
  number = 4,
  pages = {1138--1149}
}
@article{ChaWag2015many,
  author = {Shelvin Chand and  Markus Wagner },
  title = {Evolutionary many-objective optimization: A quick-start
                  guide},
  journal = {Surveys in Operations Research and Management Science},
  volume = 20,
  number = 2,
  pages = {35--42},
  year = 2015,
  doi = {10.1016/j.sorms.2015.08.001}
}
@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}
}
@article{Chase93,
  author = { Donald V. Chase  and  Lindell E. Ormsbee },
  title = {Computer-generated pumping schedules for satisfying
                  operation objectives},
  journal = {J. Am. Water Works Assoc.},
  year = 1993,
  volume = 85,
  number = 7,
  pages = {54--61}
}
@article{ChauDeb2010asoft,
  title = {An interactive evolutionary multi-objective optimization and
                  decision making procedure},
  author = {Chaudhuri, Shamik and  Kalyanmoy Deb },
  journal = {Applied Soft Computing},
  volume = 10,
  number = 2,
  pages = {496--511},
  year = 2010
}
@article{CheChiSto2012,
  title = {Business Intelligence and Analytics: From Big Data to Big
                  Impact},
  author = {Chen, Hsinchun and Chiang, Roger H. L. and Storey, Veda C.},
  journal = {MIS Quarterly},
  volume = 36,
  number = 4,
  pages = {1165--1188},
  year = 2012
}
@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 },
  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}
}
@article{CheHaoGlo2016,
  title = {A hybrid metaheuristic approach for the capacitated arc
                  routing problem},
  author = {Yuning Chen and  Jin-Kao Hao  and  Fred Glover },
  journal = {European Journal of Operational Research},
  volume = 553,
  number = 1,
  pages = {25--39},
  year = 2016,
  doi = {10.1016/j.ejor.2016.02.015},
  keywords = {irace}
}
@article{CheHsi2014,
  author = {Chen, Ruey-Maw and Hsieh, Fu-Ren},
  title = {An exchange local search heuristic based scheme for
                  permutation flow shop problems},
  journal = {Applied Mathematics \& Information Sciences},
  volume = 8,
  number = 1,
  pages = {209--215},
  year = 2014
}
@inproceedings{CheKanTay1991ijcai,
  publisher = {Morgan Kaufmann Publishers},
  editor = {Mylopoulos, John and Reiter, Raymond},
  year = 1995,
  booktitle = {Proceedings of the Twelfth 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}
}
@article{CheLi1999gen,
  author = { F. Y.  Cheng and X. S.  Li },
  title = {Generalized center method for multiobjective engineering
                  optimization},
  journal = {Engineering Optimization},
  volume = 31,
  number = 5,
  pages = {641--661},
  year = 1999,
  doi = {10.1080/03052159908941390}
}
@article{CheSia2000ejor,
  title = {Tabu search applied to global optimization},
  author = {Rachid Chelouah and Patrick Siarry},
  journal = {European Journal of Operational Research},
  volume = 123,
  number = 2,
  pages = {256--270},
  year = 2000
}
@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}
}
@article{ChengMao07:acs_tsptw,
  author = {Chin-Bin Cheng and Chun-Pin Mao},
  title = {A modified ant colony system for solving the
                  travelling salesman problem with time windows},
  journal = {Mathematical and Computer Modelling},
  year = 2007,
  volume = 46,
  pages = {1225--1235},
  doi = {10.1016/j.mcm.2006.11.035}
}
@article{ChiBirSocRos2006jos,
  author = { Marco Chiarandini  and  Mauro Birattari  and  Krzysztof Socha  and  O. Rossi-Doria },
  title = {An Effective Hybrid Algorithm for University Course
                  Timetabling},
  journal = {Journal of Scheduling},
  year = 2006,
  volume = 9,
  pages = {403--432},
  number = 5,
  month = oct,
  doi = {10.1007/s10951-006-8495-8},
  keywords = {2003 international timetabling competition, F-race}
}
@article{ChiCorDamBau11,
  author = {Chica, Manuel and  Oscar Cord{\'o}n  and Damas,
                  Sergio and Bautista, Joaqu{\'i}n},
  year = 2011,
  issn = {1865-9284},
  journal = {Memetic Computing},
  volume = 3,
  number = 1,
  title = {A New Diversity Induction Mechanism for a
                  Multi-objective Ant Colony Algorithm to Solve a
                  Real-world time and Space Assembly Line Balancing
                  Problem},
  pages = {15--24}
}
@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},
  title = {Mixed Models for the Analysis of Optimization Algorithms},
  author = { Marco Chiarandini  and Yuri Goegebeur},
  annote = {Preliminary version available as \emph{Tech.\
                  Rep.} MF-2009-07-001 at the The Danish Mathematical Society},
  pages = {225--264},
  doi = {10.1007/978-3-642-02538-9}
}
@article{ChiUlySha2016,
  author = {D. S. Chivilikhin and V. I. Ulyantsev and A. A. Shalyto},
  title = {Modified ant colony algorithm for constructing finite state machines from execution scenarios and temporal formulas},
  journal = {Automation and Remote Control},
  year = 2016,
  volume = 77,
  number = 3,
  pages = {473--484},
  doi = {10.1134/S0005117916030097},
  keywords = {irace}
}
@phdthesis{ChiarandiniPhD,
  author = { Marco Chiarandini },
  title = {Stochastic Local Search Methods for Highly
                  Constrained Combinatorial Optimisation Problems},
  school = {FB Informatik, TU Darmstadt, Germany},
  year = 2005
}
@article{Chicano2011ecj,
  author = { Chicano, Francisco  and  Darrell Whitley  and  Alba, Enrique },
  title = {A Methodology to Find the Elementary Landscape Decomposition
                  of Combinatorial Optimization Problems},
  journal = {Evolutionary Computation},
  year = 2011,
  volume = 19,
  pages = {597--637},
  number = 4
}
@article{Chicano2012aml,
  author = { Chicano, Francisco  and  Gabriel J. Luque  and  Alba, Enrique },
  title = {Autocorrelation Measures for the Quadratic Assignment
                  Problem},
  journal = {Applied Mathematics Letters},
  year = 2012,
  volume = 25,
  pages = {698--705},
  doi = {10.1016/j.aml.2011.09.053}
}
@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
}
@article{ChrMinTot81tsptw,
  author = { Christofides, Nicos  and A. Mingozzi and  Paolo Toth },
  title = {State-space relaxation procedures for the
                  computation of bounds to routing problems},
  volume = 11,
  number = 2,
  journal = {Networks},
  year = 1981,
  pages = {145--164},
  doi = {10.1002/net.3230110207},
  anote = {makespan optimization}
}
@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,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2021,
  editor = { Chicano, Francisco  and  Krzysztof Krawiec },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference Companion, GECCO Companion 2021},
  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}
}
@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}
}
@phdthesis{Chugh2017phd,
  author = { Tinkle Chugh },
  title = {Handling expensive multiobjective optimization problems with
                  evolutionary algorithms},
  school = {University of Jyväskylä},
  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 solu- tions 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}
}
@inproceedings{CirJohMcGZha2001,
  author = {Jill Cirasella and David S. Johnson and  Lyle A. McGeoch  and Weixiong Zhang},
  title = {The Asymmetric Traveling Salesman Problem: Algorithms,
                  Instance Generators, and Tests},
  booktitle = {Algorithm Engineering and Experimentation, Third
                  International Workshop, {ALENEX} 2001, Washington, DC, USA,
                  January 5-6, 2001, Revised Papers},
  pages = {32--59},
  series = {Lecture Notes in Computer Science},
  volume = 2153,
  publisher = {Springer},
  address = { Berlin, Germany },
  year = 2001,
  doi = {10.1007/3-540-44808-X_3},
  editor = {Adam L. Buchsbaum and Jack Snoeyink}
}
@inproceedings{ClaKar1992electronic,
  author = {Jon Claerbout and Martin Karrenbach},
  year = 1992,
  title = {Electronic documents give reproducible research a new
                  meaning},
  booktitle = {SEG Technical Program Expanded Abstracts 1992},
  publisher = {Society of Exploration Geophysicists},
  pages = {601--604},
  doi = {10.1190/1.1822162},
  annote = {Proposed a reproducibility taxonomy, defined reproducibility
                  and taxonomy}
}
@article{Clark95,
  author = { R. M. Clark  and  L. A. Rossman  and  L. J. Wymer },
  title = {Modeling distribution system water quality:
                  regulatory implications},
  journal = {Journal of Water Resources Planning and Management, {ASCE}},
  year = 1995,
  volume = 121,
  number = 6,
  pages = {423--428},
  note = {}
}
@misc{CleKen2011spso,
  author = {M. Clerc and  J. Kennedy },
  title = {Standard {PSO} 2011},
  howpublished = {Particle Swarm Central},
  year = 2011,
  url = {http://www.particleswarm.info/},
  alias = {pso:central}
}
@article{CocDraBes2020threats,
  author = {Cockburn, Andy and Dragicevic, Pierre and Besan\c{c}on, Lonni
                  and Gutwin, Carl},
  title = {Threats of a Replication Crisis in Empirical Computer
                  Science},
  year = 2020,
  volume = 63,
  number = 8,
  doi = {10.1145/3360311},
  abstract = {Research replication only works if there is confidence built
                  into the results.},
  journal = {Communications of the ACM},
  month = jul,
  pages = {70--79},
  numpages = 10
}
@article{CodManMarRes96:informs,
  author = {B. Codenotti and G. Manzini and L. Margara and G. Resta},
  title = {Perturbation: An Efficient Technique for the
                  Solution of Very Large Instances of the
                  {Euclidean} {TSP}},
  journal = {INFORMS Journal on Computing},
  year = 1996,
  volume = 8,
  number = 2,
  pages = {125--133}
}
@incollection{Coe2000cec,
  month = jul,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  year = 2000,
  booktitle = {Proceedings of  the 2000 Congress on Evolutionary Computation (CEC'00)},
  key = {IEEE CEC},
  title = {Handling preferences in evolutionary multiobjective
                  optimization: A survey},
  author = { Carlos A. {Coello Coello} },
  pages = {30--37}
}
@article{Coe2002constraint,
  author = { Carlos A. {Coello Coello} },
  title = {Theoretical and numerical constraint-handling techniques used
                  with evolutionary algorithms: a survey of the state of the
                  art},
  journal = {Computer Methods in Applied Mechanics and Engineering},
  year = 2002,
  volume = 191,
  number = {11-12},
  pages = {1245--1287},
  doi = {10.1016/S0045-7825(01)00323-1}
}
@incollection{Coe2015multi,
  title = {Multi-objective Evolutionary Algorithms in Real-World
                  Applications: Some Recent Results and Current Challenges},
  author = { Carlos A. {Coello Coello} },
  booktitle = {Advances in Evolutionary and Deterministic Methods for
                  Design, Optimization and Control in Engineering and Sciences},
  pages = {3--18},
  year = 2015,
  doi = {10.1007/978-3-319-11541-2_1},
  publisher = {Springer}
}
@book{CoeLamVVe2007:book,
  author = { Carlos A. {Coello Coello}  and  Gary B. Lamont  and  David A. {Van Veldhuizen} },
  title = {Evolutionary Algorithms for Solving Multi-Objective Problems},
  year = 2007,
  publisher = {Springer},
  address = { New York, NY }
}
@incollection{CoeSie2004igd,
  year = 2004,
  address = { Heidelberg },
  publisher = {Springer},
  volume = 2972,
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  author = { Werner de Schaetzen  and  Dragan A. Savic  and  Godfrey A. Walters },
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  year = 1988,
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  publisher = {{AAAI} Press\slash {MIT} Press, Menlo Park, CA },
  editor = {Howard E. Shrobe and Tom M. Mitchell and Reid G. Smith},
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@book{DeanVoss99:DAE,
  author = { Angela Dean  and  Daniel Voss },
  title = {Design and Analysis of Experiments},
  publisher = {Springer},
  address = { London, UK },
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  year = 1999
}
@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}
}
@incollection{Deb2008introduction,
  editor = { J{\"u}rgen Branke  and  Kalyanmoy Deb  and  Kaisa Miettinen  and  Roman S{\l}owi{\'n}ski },
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 5252,
  year = 2008,
  booktitle = {Multiobjective Optimization: Interactive and Evolutionary
                  Approaches},
  title = {Introduction to evolutionary multiobjective optimization},
  author = { Kalyanmoy Deb },
  abstract = {In its current state, evolutionary multiobjective
                  optimization (EMO) is an established field of research and
                  application with more than 150 PhD theses, more than ten
                  dedicated texts and edited books, commercial softwares and
                  numerous freely downloadable codes, a biannual conference
                  series running successfully since 2001, special sessions and
                  workshops held at all major evolutionary computing
                  conferences, and full-time researchers from universities and
                  industries from all around the globe. In this chapter, we
                  provide a brief introduction to EMO principles, illustrate
                  some EMO algorithms with simulated results, and outline the
                  current research and application potential of EMO. For
                  solving multiobjective optimization problems, EMO procedures
                  attempt to find a set of well-distributed Pareto-optimal
                  points, so that an idea of the extent and shape of the
                  Pareto-optimal front can be obtained. Although this task was
                  the early motivation of EMO research, EMO principles are now
                  being found to be useful in various other problem solving
                  tasks, enabling one to treat problems naturally as they
                  are. One of the major current research thrusts is to combine
                  EMO procedures with other multiple criterion decision making
                  (MCDM)  tools so as to develop hybrid and interactive
                  multiobjective optimization algorithms for finding a set of
                  trade-off optimal solutions and then choose a preferred
                  solution for implementation. This chapter provides the
                  background of EMO principles and their potential to launch
                  such collaborative studies with MCDM researchers in the
                  coming years.},
  doi = {10.1007/978-3-540-88908-3_3},
  pages = {59--96}
}
@incollection{Deb2014,
  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 = {Multi-objective optimization},
  author = { Kalyanmoy Deb },
  pages = {403--449}
}
@book{Deb:MOEA,
  author = { Kalyanmoy Deb },
  title = {Multi-Objective Optimization Using Evolutionary
                  Algorithms},
  year = 2001,
  publisher = {Wiley},
  address = {Chichester, UK}
}
@incollection{DebAg1999polymut,
  doi = {10.1007/978-3-7091-6384-9},
  editor = {Andrej Dobnikar and Nigel C. Steele and David
                  W. Pearson and Rudolf F. Albrecht},
  publisher = {Springer Verlag},
  year = 1999,
  booktitle = {Artificial Neural Nets and Genetic Algorithms (ICANNGA-99)},
  key = {ICANNGA},
  author = { Kalyanmoy Deb  and S. Agrawal},
  title = {A Niched-Penalty Approach for Constraint Handling in Genetic
                  Algorithms},
  pages = {235--243},
  keywords = {polynomial mutation}
}
@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,
  eprint = {http://www.complex-systems.com/abstracts/v09_i02_a02.html},
  keywords = {SBX}
}
@incollection{DebAgrPra2000ppsn,
  anote = {IC.29},
  address = { Heidelberg },
  publisher = {Springer},
  editor = { Marc Schoenauer  and others},
  aeditor = { Marc Schoenauer  and  Kalyanmoy Deb  and  G{\"u}nther Rudolph  and  Xin Yao  and E. Lutton and  Juan-Juli{\'a}n Merelo  and  Hans-Paul Schwefel },
  year = 2000,
  volume = 1917,
  series = {Lecture Notes in Computer Science},
  booktitle = {Parallel Problem Solving from Nature -- PPSN VI},
  title = {A fast elitist non-dominated sorting genetic algorithm
             for multi-objective optimization: {NSGA-II}},
  author = { Kalyanmoy Deb  and S. Agarwal and A. Pratap and T. Meyarivan},
  pages = {849--858}
}
@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}
}
@techreport{DebJain02,
  author = { Kalyanmoy Deb  and  Sachin Jain },
  title = {Multi-Speed Gearbox Design Using Multi-Objective
                  Evolutionary Algorithms},
  institution = {KanGAL},
  year = 2002,
  number = 2002001,
  month = feb
}
@article{DebJain2014:nsga3-part1,
  author = { Kalyanmoy Deb  and  Sachin 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}
}
@incollection{DebMyb2016gecco,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2016,
  editor = { Tobias Friedrich  and  Frank Neumann  and  Andrew M. Sutton },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2016},
  title = {Breaking the billion-variable barrier in real-world
                  optimization using a customized evolutionary algorithm},
  author = { Kalyanmoy Deb  and Myburgh, Christie},
  pages = {653--660}
}
@incollection{DebSin2009emo,
  address = { Heidelberg },
  publisher = {Springer},
  year = 2009,
  series = {Lecture Notes in Computer Science},
  volume = 5467,
  editor = {Matthias Ehrgott and  Carlos M. Fonseca  and  Xavier Gandibleux  and Jin-Kao Hao and Marc Sevaux},
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2009},
  title = {Solving Bilevel Multi-Objective Optimization Problems Using
                  Evolutionary Algorithms},
  author = { Kalyanmoy Deb  and Sinha, Ankur},
  pages = {110--124},
  alias = {Deb2009}
}
@incollection{DebSun2006gecco,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2006,
  editor = {M. Cattolico and others},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2006},
  author = { Kalyanmoy Deb  and Sundar, J.},
  title = {Reference point based multi-objective optimization using
                  evolutionary algorithms},
  pages = {635--642},
  doi = {10.1145/1143997.1144112}
}
@incollection{DebTewDixDut2007finding,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  year = 2007,
  booktitle = {Proceedings of the 2007 Congress on Evolutionary
                  Computation (CEC 2007)},
  key = {IEEE CEC},
  title = {Finding trade-off solutions close to {KKT} points using
                  evolutionary multi-objective optimization},
  author = { Kalyanmoy Deb  and Tewari, Rahul and Dixit, Mayur and Dutta, Joydeep},
  pages = {2109--2116}
}
@techreport{DebThiLau2001dtlz,
  author = { Kalyanmoy Deb  and  Lothar Thiele  and  Marco Laumanns  and  Eckart Zitzler },
  institution = {Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Z{\"u}rich, Switzerland},
  number = 112,
  title = {Scalable Test Problems for Evolutionary
                  Multi-Objective Optimization},
  year = 2001,
  keywords = {DTLZ benchmark},
  note = {Do not cite this TR! It is incorrect and it is superseeded by~\cite{DebThiLau2005dtlz}}
}
@incollection{DebThiLau2005dtlz,
  address = { London, UK },
  year = 2005,
  month = jan,
  editor = {Abraham, Ajith and Jain, Lakhmi and Goldberg, Robert},
  series = {Advanced Information and Knowledge Processing},
  publisher = {Springer},
  booktitle = {Evolutionary Multiobjective Optimization},
  author = { Kalyanmoy Deb  and  Lothar Thiele  and  Marco Laumanns  and  Eckart Zitzler },
  title = {Scalable Test Problems for Evolutionary
                  Multiobjective Optimization},
  keywords = {DTLZ benchmark},
  pages = {105--145}
}
@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}
}
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  author = {Annelies De Corte and  Kenneth S{\"o}rensen },
  title = {Optimisation of gravity-fed water distribution network
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  journal = {European Journal of Operational Research},
  volume = 228,
  number = 1,
  pages = {1--10},
  doi = {10.1016/j.ejor.2012.11.046},
  year = 2013
}
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  author = {Annelies De Corte and  Kenneth S{\"o}rensen },
  title = {An Iterated Local Search Algorithm for Water Distribution
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  journal = {Networks},
  year = 2016,
  volume = 67,
  number = 3,
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  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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  title = {Imagenet: A large-scale hierarchical image database},
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  author = { Marcelo {De Souza}  and  Marcus Ritt},
  title = {An Automatically Designed Recombination Heuristic for the
                  Test-Assignment Problem},
  pages = {1--8},
  doi = {10.1109/CEC.2018.8477801}
}
@incollection{DesRit2018evo,
  address = { Heidelberg },
  publisher = {Springer},
  volume = 10782,
  series = {Lecture Notes in Computer Science},
  year = 2018,
  booktitle = {Proceedings of EvoCOP 2018 -- 18th European Conference on Evolutionary Computation in Combinatorial Optimization },
  editor = { Arnaud Liefooghe  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  author = { Marcelo {De Souza}  and  Marcus Ritt},
  title = {Automatic Grammar-Based Design of Heuristic Algorithms for
                  Unconstrained Binary Quadratic Programming},
  pages = {67--84},
  doi = {10.1007/978-3-319-77449-7_5}
}
@misc{DesRit2018hhbqp,
  author = { Marcelo {De Souza}  and  Marcus Ritt},
  title = {Hybrid Heuristic for Unconstrained Binary Quadratic
                  Programming -- Source Code of {HHBQP}},
  howpublished = {\url{https://github.com/souzamarcelo/hhbqp}},
  year = 2018
}
@misc{DesRitLop2020acviz,
  author = { Marcelo {De Souza}  and  Marcus Ritt and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and   P{\'e}rez C{\'a}ceres, Leslie},
  title = {{\softwarepackage{ACVIZ}}: A Tool for the Visual Analysis of
                  the Configuration of Algorithms with {\rpackage{irace}} --
                  Source Code},
  howpublished = {\url{https://github.com/souzamarcelo/acviz}},
  year = 2020
}
@misc{DesRitLopPer2020zenodo,
  author = { Marcelo {De Souza}  and  Marcus Ritt and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and   P{\'e}rez C{\'a}ceres, Leslie},
  title = {{\softwarepackage{ACVIZ}}: Algorithm Configuration
                  Visualizations for {\rpackage{irace}}: Supplementary material},
  month = sep,
  year = 2020,
  publisher = {Zenodo},
  howpublished = {\url{http://doi.org/10.5281/zenodo.4714582}}
}
@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}}
}
@phdthesis{Dewez2004PhD,
  author = {Sophie Dewez},
  title = {On the toll setting problem},
  school = {Facult\'{e} de Sciences, Universit\'{e} Libre de Bruxelles},
  year = 2014,
  annote = {Supervised by Dr. Martine Labb\'{e}}
}
@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}
}
@article{DiaMouFigCli2002:ejor,
  author = { L. C. Dias  and  Vincent Mousseau  and  Jos{\'e} Rui Figueira  and  J. N. Cl{\'i}maco },
  title = {An aggregation/disaggregation approach to obtain robust conclusions with {ELECTRE TRI}},
  year = 2002,
  journal = {European Journal of Operational Research},
  volume = 138,
  number = 2,
  month = apr,
  pages = {332--348 }
}
@article{DicDor1998:jair,
  author = { Gianni A. {Di Caro}  and  Marco Dorigo },
  title = {Ant{Net}: Distributed Stigmergetic Control for Communications
                  Networks},
  journal = {Journal of Artificial Intelligence Research},
  volume = 9,
  pages = {317--365},
  year = 1998,
  alias = {DicDor98:jair}
}
@article{DicDucGam2005,
  author = { Gianni A. {Di Caro}  and  F. Ducatelle  and  L. M. Gambardella },
  title = {{AntHocNet}: An adaptive nature-inspired algorithm for routing in mobile ad hoc networks},
  journal = {European Transactions on Telecommunications},
  year = 2005,
  volume = 16,
  number = 5,
  pages = {443--455}
}
@incollection{DieValAreRodSua2014ants,
  volume = 8667,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  editor = { Marco Dorigo  and others },
  year = 2014,
  booktitle = {Swarm Intelligence, 9th International Conference, ANTS 2014},
  author = {Diego D{\'i}az and Pablo Valledor and Paula Areces and Jorge Rodil and Montserrat Su{\'a}rez},
  title = {An {ACO} Algorithm to Solve an Extended Cutting Stock Problem for Scrap Minimization in a Bar Mill},
  pages = {13--24}
}
@inproceedings{DigChiSch2006,
  publisher = {IOS Press},
  year = 2006,
  booktitle = {Proceedings of the 17th European Conference on Artificial Intelligence,
                  {ECAI} 2006, Riva del Garda, Italy, August29 - September 1, 2006},
  editor = {Brewka, Gerhard and Coradeschi, Silvia and Perini, Anna and Traverso, Paolo},
  author = {Luca {Di Gaspero} and  Marco Chiarandini  and Andrea Schaerf},
  title = {A Study on the Short-Term Prohibition Mechanisms in Tabu Search},
  pages = {83--87}
}
@incollection{DigRenUrl2013cp,
  year = 2013,
  volume = 8124,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  booktitle = {Principles and Practice of Constraint Programming},
  editor = {Christian Schulte},
  title = {Constraint-Based Approaches for Balancing Bike Sharing
                  Systems},
  author = {Luca {Di Gaspero} and  Andrea Rendl  and  Tommaso Urli },
  pages = {758--773},
  doi = {10.1007/978-3-642-40627-0_56},
  keywords = {F-race}
}
@incollection{DigRenUrl2013hyme,
  address = { Heidelberg },
  publisher = {Springer},
  volume = 7919,
  series = {Lecture Notes in Computer Science},
  editor = { Mar{\'i}a J. Blesa  and  Christian Blum  and Paola Festa and  Andrea Roli  and  M. Sampels },
  isbn = {978-3-642-38515-5},
  year = 2013,
  booktitle = {Hybrid Metaheuristics},
  title = {A Hybrid {ACO+CP} for Balancing Bicycle Sharing Systems},
  author = {Luca {Di Gaspero} and  Andrea Rendl  and  Tommaso Urli },
  pages = {198--212},
  keywords = {F-race},
  doi = {10.1007/978-3-642-38516-2_16}
}
@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},
  eprint = {http://www.diegm.uniud.it/satt/papers/DiSc03.pdf},
  volume = 33,
  year = 2003
}
@article{DinSonGup2015,
  author = {Ding, J.-Y. and Song, S. and Gupta, J. N. D. and Zhang, R. and Chiong, R. and Wu, C.},
  title = {An Improved Iterated Greedy Algorithm with a Tabu-based Reconstruction Strategy for the No-wait Flowshop Scheduling Problem},
  journal = {Applied Soft Computing},
  year = 2015,
  volume = 30,
  pages = {604--613}
}
@article{DoeDoeEbe2015,
  author = { Benjamin Doerr  and  Carola Doerr  and Franziska Ebel},
  title = {From black-box complexity to designing new genetic algorithms},
  journal = {Theoretical Computer Science},
  volume = 567,
  pages = {87-104},
  year = 2015,
  doi = {10.1016/j.tcs.2014.11.028}
}
@article{DoeDoeYan2020,
  author = { Benjamin Doerr  and  Carola Doerr  and Yang, Jing},
  title = {Optimal parameter choices via precise black-box analysis},
  journal = {Theoretical Computer Science},
  volume = 801,
  pages = {1--34},
  year = 2020,
  doi = {10.1016/j.tcs.2019.06.014}
}
@article{DoeFueGro06,
  author = { Karl F. Doerner  and  Guenther Fuellerer  and  Manfred Gronalt  and  Richard F. Hartl  and  Manuel Iori },
  title = {Metaheuristics for the Vehicle Routing Problem with Loading Constraints},
  journal = {Networks},
  year = 2006,
  volume = 49,
  number = 4,
  pages = {294--307}
}
@article{DoeGieWitYan2019,
  title = {The ({1+\(\lambda\)}) evolutionary
                  algorithm with self-adjusting mutation rate},
  author = { Benjamin Doerr  and Gie{\ss}en, Christian and  Carsten Witt  and Yang, Jing},
  journal = {Algorithmica},
  volume = 81,
  number = 2,
  pages = {593--631},
  year = 2019,
  publisher = {Springer}
}
@article{DoeGutHar08,
  author = { Karl F. Doerner  and  Gutjahr, Walter J.  and  Richard F. Hartl  and  Christine Strauss  and  Christian Stummer },
  title = {Nature-Inspired Metaheuristics in Multiobjective
                  Activity Crashing},
  journal = {Omega},
  year = 2008,
  volume = 36,
  number = 6,
  pages = {1019--1037}
}
@article{DoeGutHarStrStu04:aor,
  author = { Karl F. Doerner  and  Gutjahr, Walter J.  and  Richard F. Hartl  and  Christine Strauss  and  Christian Stummer },
  title = {{Pareto} Ant Colony Optimization: A Metaheuristic
                  Approach to Multiobjective Portfolio Selection},
  journal = {Annals of Operations Research},
  year = 2004,
  volume = 131,
  pages = {79--99},
  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},
  journal = {European Journal of Operational Research},
  year = 2006,
  volume = 171,
  pages = {830--841}
}
@article{DoeHarRei03,
  author = { Karl F. Doerner  and  Richard F. Hartl  and  Marc Reimann },
  title = {Are {COMPETants} more competent for problem solving?
                  {The} case of a multiple objective transportation
                  problem},
  journal = {Central European Journal for Operations Research and Economics},
  pages = {115--141},
  volume = 11,
  number = 2,
  year = 2003
}
@article{DoeJohWin2012,
  title = {Multiplicative drift analysis},
  author = { Benjamin Doerr  and Johannsen, Daniel and Winzen, Carola},
  journal = {Algorithmica},
  volume = 64,
  number = 4,
  pages = {673--697},
  year = 2012
}
@article{DoeKotLenWin2013,
  title = {Black-box complexities of combinatorial problems},
  author = { Benjamin Doerr  and K{\"o}tzing, Timo  and Lengler, Johannes and Winzen, Carola},
  journal = {Theoretical Computer Science},
  volume = 471,
  pages = {84--106},
  year = 2013
}
@article{DoeMerStu2009:swarm,
  author = { Karl F. Doerner  and  D. Merkle  and  Thomas St{\"u}tzle },
  title = {Special issue on Ant Colony Optimization},
  journal = {Swarm Intelligence},
  year = 2009,
  volume = 3,
  number = 1
}
@article{DoeNeuSudWit2011:tcs,
  author = { Benjamin Doerr  and  Frank Neumann  and  Dirk Sudholt  and  Carsten Witt },
  title = {Runtime analysis of the 1-{ANT} ant colony optimizer},
  journal = {Theoretical Computer Science},
  year = 2011,
  volume = 412,
  number = 1,
  pages = {1629--1644}
}
@article{Dog2015asoco,
  author = { Do\v{g}an Ayd{\i}n },
  title = {Composite artificial bee colony algorithms: From
                  component-based analysis to high-performing algorithms},
  journal = {Applied Soft Computing},
  volume = 32,
  pages = {266--285},
  year = 2015,
  doi = {10.1016/j.asoc.2015.03.051},
  keywords = {irace}
}
@article{DoiPekReg2004rank,
  author = {Jean-Paul Doignon and Aleksandar Peke{\v{c}} and Michel
                  Regenwetter},
  title = {The repeated insertion model for rankings: Missing link
                  between two subset choice models},
  doi = {10.1007/bf02295838},
  year = 2004,
  month = mar,
  volume = 69,
  number = 1,
  pages = {33--54},
  journal = {Psychometrika},
  abstract = {Several probabilistic models for subset choice have been
                  proposed in the literature, for example, to explain approval
                  voting data. We show that Marley et al.'s latent scale model
                  is subsumed by Falmagne and Regenwetter's size-independent
                  model, in the sense that every choice probability
                  distribution generated by the former can also be explained by
                  the latter. Our proof relies on the construction of a
                  probabilistic ranking model which we label the ``repeated
                  insertion model''. This model is a special case of Marden's
                  orthogonal contrast model class and, in turn, includes the
                  classical Mallows $\varphi$-model as a special case. We
                  explore its basic properties as well as its relationship to
                  Fligner and Verducci's multistage ranking model.}
}
@article{DolMor2002benchmarking,
  author = {Dolan, Elizabeth D. and Mor{\'e}, Jorge J.},
  journal = {Mathematical Programming},
  number = 2,
  pages = {201--213},
  title = {Benchmarking optimization software with performance profiles},
  volume = 91,
  year = 2002,
  keywords = {performance profiles; convergence}
}
@incollection{DomHul2000,
  year = 2000,
  address = { New York, NY },
  publisher = {ACM Press},
  booktitle = {The 6th {ACM} {SIGKDD} International Conference on Knowledge
                  Discovery and Data Mining, {KDD} 2000},
  annote = {\url{http://dl.acm.org/citation.cfm?id=347090}},
  editor = {Raghu Ramakrishnan and Salvatore J. Stolfo and Roberto
                  J. Bayardo and Ismail Parsa},
  key = {SIGKDD},
  author = {Domingos, Pedro and Hulten, Geoff},
  title = {Mining high-speed data streams},
  pages = {71--80}
}
@article{DonCheHua2013,
  author = {Xingye Dong and Ping and Houkuan Huang and Maciek Nowak},
  title = {A Multi-restart Iterated Local Search Algorithm for the Permutation Flow Shop Problem Minimizing Total Flow Time},
  journal = {Computers \& Operations Research},
  year = 2013,
  volume = 40,
  number = 2,
  pages = {627--632}
}
@article{DonHuaChe2009:cor,
  author = {X. Dong and H. Huang and P. Chen},
  title = {An Iterated Local Search Algorithm for the Permutation Flowshop Problem with Total Flowtime Criterion},
  journal = {Computers \& Operations Research},
  year = 2009,
  volume = 36,
  number = 5,
  pages = {1664--1669}
}
@article{DonMonCasRizGam08,
  author = {A. V. Donati and  Roberto Montemanni  and N. Casagrande  and A. E. Rizzoli  and  L. M. Gambardella },
  title = {Time dependent vehicle routing problem with a multi ant
                  colony system},
  journal = {European Journal of Operational Research},
  volume = 185,
  number = 3,
  year = 2008,
  pages = {1174--1191}
}
@article{Dor2007:scholarpedia,
  author = { Marco Dorigo },
  title = {Ant {Colony} {Optimization}},
  year = 2007,
  journal = {Scholarpedia},
  volume = 2,
  number = 3,
  pages = 1461,
  doi = {10.4249/scholarpedia.1461},
  alias = {Dor2007:sch-aco}
}
@article{Dor2016sipolicy,
  author = { Marco Dorigo },
  title = {Swarm intelligence: A few things you need to know if you want
                  to publish in this journal},
  journal = {Swarm Intelligence},
  year = 2016,
  month = nov,
  url = {https://static.springer.com/sgw/documents/1593723/application/pdf/Additional_submission_instructions.pdf}
}
@article{DorBirLiLop2017si,
  author = { Marco Dorigo  and  Mauro Birattari  and  Li, Xiaodong  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Kazuhiro Ohkura  and  Carlo Pinciroli  and  Thomas St{\"u}tzle },
  title = {{ANTS} 2016 Special Issue: Editorial},
  journal = {Swarm Intelligence},
  year = 2017,
  month = nov,
  doi = {10.1007/s11721-017-0146-5}
}
@article{DorBirStu06:ci,
  author = { Marco Dorigo  and  Mauro Birattari  and  Thomas St{\"u}tzle },
  title = {Ant Colony Optimization: Artificial Ants as a Computational Intelligence Technique},
  journal = {IEEE Computational Intelligence Magazine},
  year = 2006,
  volume = 1,
  number = 4,
  pages = {28--39}
}
@article{DorBlu2005:tcs,
  author = { Marco Dorigo  and  Christian Blum },
  title = {Ant colony optimization theory: A survey},
  journal = {Theoretical Computer Science},
  volume = 344,
  number = {2-3},
  year = 2005,
  pages = {243--278}
}
@incollection{DorDic99:nio,
  alias = {CorDorGlo99},
  address = {London, UK},
  year = 1999,
  publisher = {McGraw Hill},
  editor = { David Corne  and  Marco Dorigo  and  Fred Glover },
  booktitle = {New Ideas in Optimization},
  author = { Marco Dorigo  and  Gianni A. {Di Caro} },
  title = {The {Ant} {Colony} {Optimization} Meta-Heuristic},
  pages = {11--32},
  anote = {Also available as Technical Report IRIDIA/99-1,
                  Universit{\'e} Libre de Bruxelles, Belgium}
}
@article{DorDicGam99:al,
  author = { Marco Dorigo  and  Gianni A. {Di Caro}  and  L. M. Gambardella },
  title = {Ant Algorithms for Discrete Optimization},
  journal = {Artificial Life},
  volume = 5,
  number = 2,
  pages = {137--172},
  year = 1999
}
@techreport{DorGam1996:iridia,
  author = { Marco Dorigo  and  L. M. Gambardella },
  title = {Ant {Colony} {System}},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 1996,
  number = {IRIDIA/96-05}
}
@article{DorGam1997:biosys,
  author = { Marco Dorigo  and  L. M. Gambardella },
  title = {Ant Colonies for the Traveling Salesman Problem},
  journal = {BioSystems},
  year = 1997,
  volume = 43,
  number = 2,
  pages = {73--81},
  alias = {DorGam1997:bio},
  doi = {10.1016/S0303-2647(97)01708-5}
}
@article{DorGam1997:tec,
  key = {DorGam97:tec},
  alias = {DorGam97:tec},
  author = { Marco Dorigo  and  L. M. Gambardella },
  title = {{Ant} {Colony} {System}: A Cooperative Learning
                  Approach to the Traveling Salesman Problem},
  journal = {IEEE Transactions on Evolutionary Computation},
  year = 1997,
  volume = 1,
  number = 1,
  pages = {53--66},
  keyword = {Ant Colony System}
}
@article{DorGamMidStu2002:tec,
  doi = {10.1109/TEVC.2002.802446},
  author = { Marco Dorigo  and  L. M. Gambardella  and  Martin Middendorf  and  Thomas St{\"u}tzle },
  title = {Guest Editorial: Special Section on Ant Colony
                  Optimization},
  year = 2002,
  journal = {IEEE Transactions on Evolutionary Computation},
  volume = 6,
  number = 4,
  pages = {317--320},
  keyword = {ant colony optimization, swarm intelligence}
}
@techreport{DorManCol1991:tr16Revised,
  author = { Marco Dorigo  and  Vittorio Maniezzo  and  Alberto Colorni },
  title = {The {Ant} {System}: {An} autocatalytic optimizing process},
  institution = {Dipartimento di Elettronica, Politecnico di Milano, Italy},
  year = 1991,
  number = {91-016 Revised}
}
@article{DorManCol1996:tsmcb,
  author = { Marco Dorigo  and  Vittorio Maniezzo  and  Alberto Colorni },
  title = {{Ant} {System}: Optimization by a Colony of
                  Cooperating Agents},
  journal = {IEEE Transactions on Systems, Man, and Cybernetics -- Part B},
  year = 1996,
  volume = 26,
  number = 1,
  pages = {29--41},
  alias = {DorManCol96:tsmcb}
}
@techreport{DorManCol91:tr16,
  author = { Marco Dorigo  and  Vittorio Maniezzo  and  Alberto Colorni },
  title = {Positive Feedback as a Search Strategy},
  institution = {Dipartimento di Elettronica, Politecnico di Milano, Italy},
  year = 1991,
  number = {91-016}
}
@incollection{DorMonOliStu2011eorms,
  doi = {10.1002/9780470400531},
  year = 2011,
  publisher = {John Wiley \& Sons},
  editor = {J. J. Cochran},
  booktitle = {Wiley Encyclopedia of Operations Research and
                  Management Science},
  author = { Marco Dorigo  and  Marco A. {Montes de Oca}  and  Sabrina Oliveira  and  Thomas St{\"u}tzle },
  title = {Ant Colony Optimization},
  pages = {114--125},
  volume = 1
}
@incollection{DorStu02:mh,
  alias = {GloKoc02:handbook-mh},
  publisher = {Kluwer Academic Publishers, Norwell, MA},
  year = 2002,
  editor = { Fred Glover  and Gary A. Kochenberger},
  booktitle = {Handbook of Metaheuristics},
  author = { Marco Dorigo  and  Thomas St{\"u}tzle },
  title = {The Ant Colony Optimization Metaheuristic: Algorithms, Applications and Advances},
  pages = {251--285}
}
@book{DorStu2004:book,
  author = { Marco Dorigo  and  Thomas St{\"u}tzle },
  title = {Ant Colony Optimization},
  publisher = {MIT Press},
  address = {Cambridge, MA},
  year = 2004,
  pagination = 305,
  anote = {305 p},
  alias = {DorStu04:AcoBook},
  alias = {DorStu04:book}
}
@article{DorStuDic2000:fgcs,
  author = { Marco Dorigo  and  Thomas St{\"u}tzle  and  Gianni A. {Di Caro} },
  title = {Special Issue on ``{Ant} {Algorithms}''},
  year = 2000,
  journal = {Future Generation Computer Systems},
  volume = 16,
  number = 8,
  keyword = {swarm intelligence, ant colony optimization}
}
@phdthesis{DorigoPhD,
  author = { Marco Dorigo },
  title = {Optimization, Learning and Natural Algorithms},
  school = {Dipartimento di Elettronica, Politecnico di Milano, Italy},
  year = 1992,
  atype = {{Ph.D.} thesis},
  note = {In Italian},
  alias = {Dor92:thesis}
}
@article{DouZop2010:ejor,
  author = { Michael Doumpos  and  Constantin Zopounidis },
  title = {Preference disaggregation and statistical learning for multicriteria decision support: A review},
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  volume = 209,
  number = 3,
  pages = {203--214},
  year = 2011
}
@article{DovTusFil2010ec,
  author = {Erik Dovgan and Tea Tu{\v s}ar and Bogdan Filipi{\v c}},
  title = {Parameter tuning in an evolutionary algorithm for commodity
                  transportation optimization},
  journal = {Evolutionary Computation},
  alias = {ParamTunComm},
  year = 2010,
  pages = {1--8}
}
@incollection{Dre2009gecco,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2009,
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference Companion, GECCO Companion 2009},
  editor = {Franz Rothlauf},
  author = { Johann Dr{\'e}o },
  title = {Using performance fronts for parameter setting of
                  stochastic metaheuristics},
  pages = {2197--2200},
  doi = {10.1145/1570256.1570301}
}
@incollection{DreDoeSem2019,
  aurl = {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 Companion, GECCO Companion 2019},
  author = { Johann Dr{\'e}o  and  Carola Doerr  and Semet, Yann},
  title = {Coupling the design of benchmark with algorithm in landscape-aware solver design},
  pages = {1419--1420}
}
@incollection{DreSia02:ants,
  address = { Heidelberg },
  publisher = {Springer},
  editor = { Marco Dorigo  and others },
  fulleditor = { Marco Dorigo  and  Gianni A. {Di Caro}  and  M. Sampels },
  volume = 2463,
  series = {Lecture Notes in Computer Science},
  year = 2002,
  booktitle = {Ant Algorithms, Third International Workshop, ANTS
                  2002},
  author = { Johann Dr{\'e}o  and P. Siarry},
  title = {A New Ant Colony Algorithm Using the Heterarchical Concept Aimed at Optimization of Multiminima Continuous Functions},
  pages = {216--221}
}
@article{DreSia2004,
  author = { Johann Dr{\'e}o  and P. Siarry},
  title = {Continuous interacting ant colony algorithm based on dense
                  heterarchy},
  journal = {Future Generation Computer Systems},
  volume = 20,
  number = 5,
  year = 2004,
  pages = {841--856},
  alias = {DreSia04}
}
@inproceedings{DroJanWeg2002,
  publisher = {Morgan Kaufmann Publishers},
  editor = { De Jong, Kenneth A.  and Poli, Riccardo and Rowe, Jonathan E.},
  year = 2002,
  booktitle = {Proceedings of the Seventh Workshop on Foundations
                  of Genetic Algorithms (FOGA)},
  title = {A new framework for the valuation of algorithms for black-box-optimization},
  author = {Droste, Stefan and Jansen, Thomas and  Ingo Wegener },
  pages = {253--270}
}
@article{DroJanWeg2006,
  title = {Upper and lower bounds for randomized search heuristics in black-box optimization},
  author = {Droste, Stefan and Jansen, Thomas and  Ingo Wegener },
  journal = {Theory of Computing Systems},
  volume = 39,
  number = 4,
  pages = {525--544},
  year = 2006,
  publisher = {Springer}
}
@inproceedings{Dru2009replicability,
  author = {Chris Drummond},
  title = {Replicability is not Reproducibility: Nor is it Good Science},
  booktitle = {Proceedings of the Evaluation Methods for Machine Learning
                  Workshop at the 26th ICML},
  address = {Montreal, Canada},
  url = {http://www.site.uottawa.ca/~cdrummon/pubs/ICMLws09.pdf},
  year = 2009
}
@incollection{DruThi2010,
  volume = 6238,
  year = 2010,
  address = { Heidelberg },
  publisher = {Springer},
  editor = {Schaefer, Robert and Cotta, Carlos and Kolodziej,
                  Joanna and  G{\"u}nther Rudolph },
  series = {Lecture Notes in Computer Science},
  booktitle = {Parallel Problem Solving from Nature, PPSN XI},
  author = { M{\u{a}}d{\u{a}}lina M. Drugan  and  Dirk Thierens },
  title = {Path-Guided Mutation for Stochastic {Pareto} Local Search Algorithms},
  pages = {485--495}
}
@article{DruThi2012,
  author = { M{\u{a}}d{\u{a}}lina M. Drugan  and  Dirk Thierens },
  title = {Stochastic {Pareto} local search: {Pareto} neighbourhood
              exploration and perturbation strategies},
  journal = {Journal of Heuristics},
  volume = 18,
  number = 5,
  year = 2012,
  pages = {727--766}
}
@article{DuLeu90,
  author = {J. Du and  Joseph Y.-T. Leung },
  title = {Minimizing Total Tardiness on One Machine is
                  {NP}-Hard},
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  year = 1990,
  volume = 15,
  number = 3,
  pages = {483--495}
}
@techreport{Dub2009:sls-ds,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste },
  title = {Weight Setting Strategies for Two-Phase Local Search:
                  A Study on Biobjective Permutation Flowshop Scheduling},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2009,
  number = {TR/IRIDIA/2009-024}
}
@inproceedings{DubHooStu2015gecco,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2015,
  editor = {Sara Silva and  Anna I. Esparcia{-}Alc{\'{a}}zar },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2015},
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Holger H. Hoos  and  Thomas St{\"u}tzle },
  title = {On the Empirical Scaling Behaviour of State-of-the-art Local
                  Search Algorithms for the Euclidean {TSP}},
  pages = {377--384},
  doi = {10.1145/2739480.2754747}
}
@incollection{DubLopStu09:hm-bfsp,
  address = { Heidelberg },
  publisher = {Springer},
  volume = 5818,
  series = {Lecture Notes in Computer Science},
  editor = { Mar{\'i}a J. Blesa  and  Christian Blum  and Luca {Di Gaspero} and  Andrea Roli  and  M. Sampels  and Andrea Schaerf},
  year = 2009,
  booktitle = {Hybrid Metaheuristics},
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Effective Hybrid Stochastic Local Search Algorithms for
                  Biobjective Permutation Flowshop Scheduling},
  pages = {100--114},
  doi = {10.1007/978-3-642-04918-7_8},
  alias = {DuboisHM09}
}
@misc{DubLopStu10:journal-anytime-supp,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {{Supplementary material: Improving the Anytime Behavior of Two-Phase Local Search}},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2010-012}},
  year = 2010
}
@misc{DubLopStu10:journal-bfsp-supp,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {{Supplementary material: A Hybrid TP+PLS Algorithm for Bi-objective Flow-shop Scheduling Problems}},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2010-001}},
  year = 2010
}
@incollection{DubLopStu10:lion-bfsp,
  address = { Heidelberg },
  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},
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Adaptive ``Anytime'' Two-Phase Local Search},
  pages = {52--67},
  doi = {10.1007/978-3-642-13800-3_5}
}
@misc{DubLopStu2011:gecco-supp,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Supplementary material: Automatic Configuration of State-of-the-art Multi-objective Optimizers Using the {TPLS+PLS} Framework},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2011-005}},
  year = 2011
}
@article{DubLopStu2011amai,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Improving the Anytime Behavior of Two-Phase Local
                  Search},
  journal = {Annals of Mathematics and Artificial Intelligence},
  year = 2011,
  volume = 61,
  number = 2,
  pages = {125--154},
  doi = {10.1007/s10472-011-9235-0},
  alias = {DubLopStu2010amai}
}
@article{DubLopStu2011cor,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {A Hybrid {TP$+$PLS} Algorithm for Bi-objective
                  Flow-Shop Scheduling Problems},
  journal = {Computers \& Operations Research},
  year = 2011,
  volume = 38,
  number = 8,
  pages = {1219--1236},
  doi = {10.1016/j.cor.2010.10.008},
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2010-001/}
}
@incollection{DubLopStu2011gecco,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2011,
  editor = {Natalio Krasnogor and Pier Luca Lanzi},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2011},
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Configuration of State-of-the-art Multi-Objective
                  Optimizers Using the {TP+PLS} Framework},
  pages = {2019--2026},
  doi = {10.1145/2001576.2001847}
}
@misc{DubLopStu2012:evocop-supp,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {{Supplementary Material: {Pareto} Local Search Variants for Anytime Bi-Objective Optimization}},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2012-004}},
  year = 2012
}
@incollection{DubLopStu2012evocop,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 7245,
  year = 2012,
  editor = { Jin-Kao Hao  and  Martin Middendorf },
  booktitle = {Proceedings of EvoCOP 2012 -- 12th European Conference on Evolutionary Computation in Combinatorial Optimization },
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {{Pareto} Local Search Algorithms for Anytime
                  Bi-objective Optimization},
  pages = {206--217},
  doi = {10.1007/978-3-642-29124-1_18},
  alias = {DubLopStu12:evocop}
}
@incollection{DubLopStu2013hm,
  url = {http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-30670-9},
  year = 2013,
  volume = 434,
  series = {Studies in Computational Intelligence},
  editor = { Talbi, El-Ghazali },
  publisher = {Springer Verlag},
  booktitle = {Hybrid Metaheuristics},
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Combining Two Search Paradigms for Multi-objective
                  Optimization: {Two}-{Phase} and {Pareto} Local Search},
  pages = {97--117},
  doi = {10.1007/978-3-642-30671-6_3},
  alias = {DubLopStu2012hm}
}
@article{DubLopStu2015ejor,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Anytime {Pareto} Local Search},
  journal = {European Journal of Operational Research},
  year = 2015,
  volume = 243,
  number = 2,
  pages = {369--385},
  doi = {10.1016/j.ejor.2014.10.062},
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2013-003/},
  alias = {DubLopStu2013cor}
}
@misc{DubLopStu2015ejor-supp,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Supplementary material: Anytime {Pareto} Local Search},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2013-003}},
  year = 2013,
  alias = {DubLopStu13cor-supp}
}
@misc{DubPagStu2017:flowshop-makespan-supp,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Federico Pagnozzi  and  Thomas St{\"u}tzle },
  title = {Supplementary material: {An} iterated greedy algorithm with optimization of partial
  solutions for the permutation flowshop problem},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2013-006}},
  year = 2017
}
@article{DubPagStu2017cor,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Federico Pagnozzi  and  Thomas St{\"u}tzle },
  title = {An Iterated Greedy Algorithm with Optimization of Partial
  Solutions for the Permutation Flowshop Problem},
  journal = {Computers \& Operations Research},
  year = 2017,
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2013-006},
  volume = 81,
  pages = {160--166},
  doi = {10.1016/j.cor.2016.12.021}
}
@incollection{DubStu2017cec,
  year = 2017,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  booktitle = {Proceedings of  the 2017 Congress on Evolutionary Computation (CEC 2017)},
  key = {IEEE CEC},
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Thomas St{\"u}tzle },
  title = {Tuning of a Stigmergy-based Traffic Light Controller as a Dynamic Optimization Problem},
  pages = {1--8}
}
@mastersthesis{Dubois2009,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste },
  title = {A study of {Pareto} and Two-Phase Local Search
                  Algorithms for Biobjective Permutation Flowshop
                  Scheduling},
  school = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2009
}
@mastersthesis{Dubois2010,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste },
  title = {Effective Stochastic Local Search Algorithms For
                  Bi-Objective Permutation Flowshop Scheduling},
  school = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  type = {Rapport d'avancement de recherches pr\'esent\'e pour
                  la Formation Doctorale en sciences de l'Ing\'enieur},
  year = 2010
}
@phdthesis{DuboisPhD,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste },
  title = {Anytime Local Search for Multi-Objective Combinatorial
                  Optimization: Design, Analysis and Automatic Configuration},
  school = {IRIDIA, {\'E}cole polytechnique, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2014,
  annote = {Supervised by Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez }
}
@article{Dud2008carbodies,
  author = {Fabian Duddeck},
  title = {Multidisciplinary optimization of car bodies},
  journal = {Structural and Multidisciplinary Optimization},
  year = 2008,
  volume = 35,
  number = 4,
  pages = {375--389},
  doi = {10.1007/s00158-007-0130-6},
  annote = {Evolutionary optimization of car bodies at General Motors}
}
@article{DueSch90,
  author = { Gunter Dueck  and T. Scheuer},
  title = {Threshold Accepting: A General Purpose Optimization
                  Algorithm Appearing Superior to Simulated Annealing},
  journal = {Journal of Computational Physics},
  year = 1990,
  volume = 90,
  number = 1,
  pages = {161--175}
}
@article{Dueck1993,
  title = {New Optimization Heuristics: the Great Deluge Algorithm and the Record-To-Record Travel},
  author = { Gunter Dueck },
  journal = {Journal of Computational Physics},
  volume = 104,
  number = 1,
  pages = {86--92},
  year = 1993,
  publisher = {Elsevier}
}
@misc{Duecketal1999:patent,
  author = { Gunter Dueck  and Martin Maehler and Johannes Schneider and Gerhard Schrimpf and Hermann Stamm-Wilbrandt},
  title = {Optimization with Ruin Recreate},
  howpublished = {US Patent 6,418,398 B1},
  month = jul,
  year = 2002,
  note = {Filed on October 1, 1999 and granted on July 9, 2002;
                  Assignee is IBM Corporation, Armonk, NY (US)}
}
@article{DuiBerCar2017constrained,
  title = {Constrained {Bayesian} Optimization with Particle Swarms for
                  Safe Adaptive Controller Tuning},
  journal = {{IFAC}-{PapersOnLine}},
  volume = 50,
  number = 1,
  pages = {11800--11807},
  year = 2017,
  annote = {20th IFAC World Congress},
  doi = {10.1016/j.ifacol.2017.08.1991},
  author = {Rikky R. P. R. Duivenvoorden and Felix Berkenkamp and Nicolas
                  Carion and Andreas Krause and Angela P. Schoellig},
  keywords = {Adaptive Control, Constrained Bayesian Optimization, Safety,
                  Gaussian Process, Particle Swarm Optimization, Policy Search,
                  Reinforcement learning},
  abstract = {Tuning controller parameters is a recurring and
                  time-consuming problem in control. This is especially true in
                  the field of adaptive control, where good performance is
                  typically only achieved after significant tuning. Recently,
                  it has been shown that constrained Bayesian optimization is a
                  promising approach to automate the tuning process without
                  risking system failures during the optimization
                  process. However, this approach is computationally too
                  expensive for tuning more than a couple of parameters. In
                  this paper, we provide a heuristic in order to efficiently
                  perform constrained Bayesian optimization in high-dimensional
                  parameter spaces by using an adaptive discretization based on
                  particle swarms. We apply the method to the tuning problem of
                  an L1 adaptive controller on a quadrotor vehicle and show
                  that we can reliably and automatically tune parameters in
                  experiments.}
}
@article{DuiVos1999,
  author = {Cees Duin and  Stefan Vo{\ss} },
  title = {The Pilot Method: A Strategy for Heuristic Repetition with Application to the {Steiner} Problem in Graphs},
  journal = {Networks},
  year = 1999,
  volume = 34,
  number = 3,
  pages = {181--191}
}
@incollection{DumStu2003,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  year = 2003,
  editor = { G{\"u}nther R. Raidl  and Gottlieb, Jens},
  volume = 2611,
  booktitle = {Proceedings of EvoCOP 2003 -- 3rd  European Conference on Evolutionary Computation in Combinatorial Optimization },
  author = {Irina Dumitrescu and  Thomas St{\"u}tzle },
  title = {Combinations of Local Search and Exact Algorithms},
  pages = {211--223}
}
@incollection{DumStu2009,
  address = { New York, NY },
  series = {Annals of Information Systems},
  volume = 10,
  year = 2009,
  publisher = {Springer},
  booktitle = {Matheuristics---Hybridizing Metaheuristics and Mathematical
                  Programming},
  editor = { Vittorio Maniezzo  and  Thomas St{\"u}tzle  and  Stefan Vo{\ss} },
  author = {Irina Dumitrescu and  Thomas St{\"u}tzle },
  title = {Usage of Exact Algorithms to Enhance Stochastic Local Search Algorithms},
  pages = {103--134}
}
@article{Dumas95tsptw,
  author = { Y. Dumas  and  J. Desrosiers  and  E. Gelinas  and  M. M. Solomon },
  title = {An Optimal Algorithm for the Traveling Salesman
                  Problem with Time Windows},
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  year = 1995,
  volume = 43,
  number = 2,
  pages = {367--371}
}
@article{Dun1964tech,
  title = {Multiple Comparisons Using Rank Sums},
  author = { Olive Jean Dunn },
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  year = 1964,
  publisher = {Taylor \& Francis Group}
}
@article{Dunn1961jasa,
  title = {Multiple Comparisons Among Means},
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}
@incollection{DurNebLunAlb2009,
  address = { Heidelberg },
  publisher = {Springer},
  year = 2009,
  series = {Lecture Notes in Computer Science},
  volume = 5467,
  editor = {Matthias Ehrgott and  Carlos M. Fonseca  and  Xavier Gandibleux  and Jin-Kao Hao and Marc Sevaux},
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2009},
  title = {On the Effect of the Steady-State Selection Scheme in
                  Multi-Objective Genetic Algorithms},
  author = {Durillo, Juan J. and Nebro, Antonio J. and Luna, Francisco
                  and Alba, Enrique},
  pages = {183-197}
}
@incollection{DwoKumNao2001rank,
  isbn = {1-58113-348-0},
  year = 2001,
  address = { New York, NY },
  publisher = {ACM Press},
  booktitle = {Proceedings of the Tenth International World Wide Web
                  Conference, {WWW} 10},
  editor = {Vincent Y. Shen and Nobuo Saito and Michael R. Lyu and Mary
                  Ellen Zurko},
  title = {Rank aggregation methods for the Web},
  author = {Dwork, Cynthia and Kumar, Ravi and Naor, Moni and Sivakumar,
                  D.},
  doi = {10.1145/371920.372165},
  keywords = {Kemeny ranking,multi-word queries,rank aggregation,ranking
                  functions,spam},
  pages = {613--622}
}
@manual{EPANET2Manual,
  title = {{EPANET} 2 Users Manual},
  author = { L. A. Rossman },
  organization = {U.S. Environmental Protection Agency},
  address = {Cincinnati, USA},
  year = 2000
}
@manual{EPANET94,
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  organization = {Risk Reduction Engineering Laboratory, Office of
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  address = {Cincinnati, USA},
  year = 1994
}
@inproceedings{EPANET_Toolkit,
  author = { L. A. Rossman },
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                  of Water Distribution Systems},
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                  and Management Conference},
  year = 1999,
  address = {Reston, USA},
  publisher = {ASCE},
  anote = {CD-ROM}
}
@inproceedings{EbeKen1995:pso,
  author = { Eberhart, Russell C.  and  J. Kennedy },
  booktitle = {Proceedings of the Sixth International Symposium on
                  Micro Machine and Human Science},
  title = {A New Optimizer Using Particle Swarm Theory},
  year = 1995,
  pages = {39--43}
}
@inproceedings{EggHutHooLey2015,
  year = 2015,
  publisher = {{AAAI} Press},
  booktitle = {Proceedings of  the {AAAI} Conference on Artificial Intelligence},
  editor = {Blai Bonet and Sven Koenig},
  author = {Eggensperger, Katharina and  Frank Hutter  and  Holger H. Hoos  and  Kevin Leyton-Brown },
  title = {Efficient Benchmarking of Hyperparameter Optimizers via
                  Surrogates},
  pages = {1114--1120}
}
@article{Eglese1990,
  title = {Simulated Annealing: a Tool for Operational Research},
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@incollection{Ehm2016,
  author = {Werner Ehm},
  title = {Reproducibility from the perspective of meta-analysis},
  editor = {Harald Atmanspacher and Sabine Maasen},
  booktitle = {Reproducibility -- Principles, problems, practices and prospects},
  publisher = {Wiley},
  year = 2016,
  pages = {141--168}
}
@article{Ehr2006discussion,
  title = {A discussion of scalarization techniques for multiple
                  objective integer programming},
  author = { Matthias Ehrgott },
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  number = 1,
  pages = {343--360},
  year = 2006,
  publisher = {Springer}
}
@article{EhrGan04,
  author = { Matthias Ehrgott  and  Xavier Gandibleux },
  title = {Approximative Solution Methods for Combinatorial
                  Multicriteria Optimization},
  journal = {TOP},
  year = 2004,
  volume = 12,
  number = 1,
  pages = {1--88}
}
@incollection{EhrGan08hybrid,
  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},
  author = { Matthias Ehrgott  and  Xavier Gandibleux },
  title = {Hybrid Metaheuristics for Multi-objective
                  Combinatorial Optimization},
  doi = {10.1007/978-3-540-78295-7_8},
  abstract = {Many real-world optimization problems can be
                  modelled as combinatorial optimization
                  problems. Often, these problems are characterized by
                  their large size and the presence of multiple,
                  conflicting objectives. Despite progress in solving
                  multi-objective combinatorial optimization problems
                  exactly, the large size often means that heuristics
                  are required for their solution in acceptable time.
                  Since the middle of the nineties the trend is
                  towards heuristics that ``pick and choose'' elements
                  from several of the established metaheuristic
                  schemes. Such hybrid approximation techniques may
                  even combine exact and heuristic approaches. In this
                  chapter we give an overview over approximation
                  methods in multi-objective combinatorial
                  optimization. We briefly summarize ``classical''
                  metaheuristics and focus on recent approaches, where
                  metaheuristics are hybridized and/or combined with
                  exact methods.  },
  pages = {221--259}
}
@book{Ehrgott00:multicriteria,
  author = { Matthias Ehrgott },
  title = {Multicriteria Optimization},
  publisher = {Springer},
  address = { Berlin, Germany },
  year = 2000,
  volume = 491,
  series = {Lecture Notes in Economics and Mathematical Systems}
}
@book{Ehrgott2005multicrit,
  author = { Matthias Ehrgott },
  title = {Multicriteria Optimization},
  publisher = {Springer},
  address = { Berlin, Germany },
  year = 2005,
  edition = {2nd},
  doi = {10.1007/3-540-27659-9}
}
@article{EibHinMich1999tec,
  author = { Agoston E. Eiben  and Robert Hinterding and  Zbigniew Michalewicz },
  title = {Parameter Control in Evolutionary Algorithms},
  journal = {IEEE Transactions on Evolutionary Computation},
  volume = 3,
  number = 2,
  pages = {124--141},
  year = 1999
}
@incollection{EibHorKow2006rl,
  title = {Reinforcement learning for online control of evolutionary
                  algorithms},
  author = { Agoston E. Eiben  and Horvath, Mark and Kowalczyk, Wojtek and Schut, Martijn C.},
  booktitle = {International Workshop on Engineering Self-Organising
                  Applications},
  pages = {151--160},
  year = 2006,
  publisher = {Springer}
}
@inproceedings{EibJel2002critical,
  year = 2002,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  booktitle = {Proceedings of  the 2002 Congress on Evolutionary Computation (CEC'02)},
  key = {IEEE CEC},
  author = { Agoston E. Eiben  and M. Jelasity},
  title = {A critical note on experimental research methodology in {EC}},
  annote = {Discusses reproducibility, generalizability and separation
                  between training (for tuning and experimentation) and testing
                  instances (for comparisons).},
  doi = {10.1109/cec.2002.1006991},
  pages = {582--587}
}
@incollection{EibMichSChSmi07,
  address = { Berlin, Germany },
  publisher = {Springer},
  year = 2007,
  booktitle = {Parameter Setting in Evolutionary Algorithms},
  editor = {F. Lobo and C. F. Lima and  Zbigniew Michalewicz },
  author = { Agoston E. Eiben  and  Zbigniew Michalewicz  and  Marc Schoenauer  and James E. Smith},
  title = {Parameter Control in Evolutionary Algorithms},
  pages = {19--46}
}
@article{EibRud1999,
  title = {Theory of evolutionary algorithms: A bird's eye view},
  author = { Agoston E. Eiben  and  G{\"u}nther Rudolph },
  journal = {Theoretical Computer Science},
  volume = 229,
  number = {1-2},
  pages = {3--9},
  year = 1999
}
@book{EibSmi2003,
  title = {Introduction to Evolutionary Computing},
  author = { Agoston E. Eiben  and  Smith, James E. },
  publisher = {Springer},
  year = 2003,
  isbn = 3540401849,
  alias = {Eiben2003}
}
@book{EibSmi2007,
  author = { Agoston E. Eiben  and  Smith, James E. },
  title = {Introduction to Evolutionary Computing},
  publisher = {Springer},
  year = 2007,
  series = {Natural Computing Series},
  edition = {2nd},
  alias = {EibSmi07}
}
@article{EibSmi2011swec,
  author = { Agoston E. Eiben  and  Smit, Selmar K. },
  title = {Parameter Tuning for Configuring and Analyzing
                  Evolutionary Algorithms},
  journal = {Swarm and Evolutionary Computation},
  year = 2011,
  volume = 1,
  number = 1,
  pages = {19--31},
  doi = {10.1016/j.swevo.2011.02.001}
}
@article{EkeKwa2018ems,
  title = {Including robustness considerations in the search phase of
                  Many-Objective Robust Decision Making},
  author = {Eker, Sibel and  Kwakkel, Jan H. },
  journal = {Environmental Modelling \& Software},
  volume = 105,
  pages = {201--216},
  year = 2018,
  keywords = {scenario-based}
}
@incollection{Ela2011:gecco,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2011,
  editor = {Natalio Krasnogor and Pier Luca Lanzi},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2011},
  author = {Mohammed El-Abd},
  title = {Opposition-based Artificial Bee Colony Algorithm},
  pages = {109--116}
}
@article{Elm1991rnn,
  title = {Distributed representations, simple recurrent networks, and
                  grammatical structure},
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  number = {2-3},
  pages = {195--225},
  year = 1991,
  publisher = {Springer}
}
@article{EmePer1991,
  author = {V. A. Emelichev and V. A. Perepelitsa},
  title = {Complexity of Vector Optimization Problems on Graphs},
  journal = {Optimization},
  year = 1991,
  volume = 22,
  number = 6,
  doi = {10.1080/02331939108843732},
  pages = {906--918}
}
@article{EmePer1992,
  author = {V. A. Emelichev and V. A. Perepelitsa},
  title = {On the Cardinality of the Set of Alternatives in Discrete Many-criterion Problems},
  journal = {Discrete Mathematics and Applications},
  year = 1992,
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  number = 5,
  pages = {461--471}
}
@inproceedings{EmmDeuKli2011ehvi,
  year = 2011,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  booktitle = {Proceedings of  the 2011 Congress on Evolutionary Computation (CEC 2011)},
  key = {IEEE CEC},
  author = { Emmerich, Michael T. M.  and   Andr{\'{e}} H. Deutz  and J. W. Klinkenberg},
  title = {Hypervolume-based expected improvement: Monotonicity
                  properties and exact computation},
  pages = {2147--2154},
  doi = {10.1109/CEC.2011.5949880},
  annote = {Proposed Expected Hypervolume Improvement (EHVI)}
}
@incollection{EmmFon2011emo,
  address = { Heidelberg },
  publisher = {Springer},
  year = 2011,
  series = {Lecture Notes in Computer Science},
  volume = 6576,
  editor = { Takahashi, R. H. C.  and others},
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2011},
  title = {Computing Hypervolume Contributions in Low
                  Dimensions: Asymptotically Optimal Algorithm and
                  Complexity Results},
  doi = {10.1007/978-3-642-19893-9_9},
  abstract = {Given a finite set $Y \subset \mathbb{R}^d$ of n mutually
                  non-dominated vectors in $d \geq 2 dimensions$, the
                  hypervolume contribution of a point $y \in Y$ is the
                  difference between the hypervolume indicator of $Y$
                  and the hypervolume indicator of $Y \setminus \{y\}$. In
                  multi-objective metaheuristics, hypervolume
                  contributions are computed in several selection and
                  bounded-size archiving procedures. This paper
                  presents new results on the (time) complexity of
                  computing all hypervolume contributions. It is
                  proved that for d = 2,3 the problem has time
                  complexity $\Theta(n logn)$, and, for $d > 3$,
                  the time complexity is bounded below by $\Omega(n
                  logn)$. Moreover, complexity bounds are derived for
                  computing a single hypervolume contribution. A
                  dimension sweep algorithm with time complexity
                  $\mathcal{O} (n logn)$ and space
                  complexity $\mathcal{O}(n)$ is
                  proposed for computing all hypervolume contributions
                  in three dimensions. It improves the complexity of
                  the best known algorithm for d = 3 by a factor of
                  $\sqrt{n}$ . Theoretical results
                  are complemented by performance tests on randomly
                  generated test-problems.},
  author = { Emmerich, Michael T. M.  and  Carlos M. Fonseca },
  pages = {121--135}
}
@article{EmmGiaNau2006,
  author = { Emmerich, Michael T. M.  and K. C. Giannakoglou and  Boris Naujoks },
  journal = {IEEE Transactions on Evolutionary Computation},
  title = {Single- and multiobjective evolutionary optimization assisted
                  by {Gaussian} random field metamodels},
  year = 2006,
  volume = 10,
  number = 4,
  pages = {421--439},
  doi = {10.1109/TEVC.2005.859463}
}
@article{EngWie2007twod,
  title = {{2D} decision-making for multicriteria design optimization},
  author = {Engau, Alexander and  Margaret M. Wiecek },
  journal = {Structural and Multidisciplinary Optimization},
  volume = 34,
  pages = {301--315},
  year = 2007,
  doi = {10.1007/s00158-006-0078-y},
  publisher = {Springer}
}
@article{EngWie2008interactive,
  title = {Interactive coordination of objective decompositions in
                  multiobjective programming},
  author = {Engau, Alexander and  Margaret M. Wiecek },
  journal = {Management Science},
  volume = 54,
  number = 7,
  pages = {1350--1363},
  year = 2008,
  publisher = {{INFORMS}}
}
@incollection{EppDeSStu2011:adt,
  address = { Heidelberg },
  publisher = {Springer},
  editor = { Ronen I. Brafman  and  F. Roberts  and  Alexis Tsouki{\`a}s },
  volume = 6992,
  series = {Lecture Notes in Artificial Intelligence},
  year = 2011,
  booktitle = {Algorithmic Decision Theory, Third International Conference,
                  {ADT} 2011},
  author = { Stefan Eppe  and  Yves {De Smet}  and  Thomas St{\"u}tzle },
  title = {A bi-objective optimization model to eliciting decision maker's preferences for the {PROMETHEE II} method},
  pages = {56--66}
}
@incollection{EppLopStuDeS2011:cec,
  year = 2011,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  booktitle = {Proceedings of  the 2011 Congress on Evolutionary Computation (CEC 2011)},
  key = {IEEE CEC},
  author = { Stefan Eppe  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle  and  Yves {De Smet} },
  title = {An Experimental Study of Preference Model Integration into
                  Multi-Objective Optimization Heuristics},
  pages = {2751--2758},
  doi = {10.1109/CEC.2011.5949963}
}
@inproceedings{EriPeaGar2019scalable,
  aurl = {http://papers.nips.cc/book/advances-in-neural-information-processing-systems-32-2019},
  year = 2019,
  editor = {Hanna M. Wallach and Hugo Larochelle and Alina Beygelzimer
                  and Florence d'Alch{\'{e}}{-}Buc and Emily B. Fox and Roman
                  Garnett},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS 32)},
  title = {Scalable Global Optimization via Local {Bayesian}
                  Optimization},
  author = {Eriksson, David and Pearce, Michael and Gardner, Jacob and
                  Turner, Ryan D. and Poloczek, Matthias},
  pages = {5496--5507},
  eprint = {http://papers.nips.cc/paper/8788-scalable-global-optimization-via-local-bayesian-optimization.pdf},
  epub = {https://arxiv.org/abs/1910.01739}
}
@inproceedings{Ertin01,
  author = { Emre Ertin  and  Anthony N. Dean  and  Mathew L. Moore  and  Kevin L. Priddy },
  title = {Dynamic Optimization for Optimal Control of Water
                  Distribution Systems},
  booktitle = {Applications and Science of Computational
                  Intelligence {IV}, Proceedings of {SPIE}},
  year = 2001,
  month = mar,
  pages = {142--149},
  editor = { Kevin L. Priddy  and  Paul E. Keller  and  Peter J. Angeline },
  volume = 4390
}
@inproceedings{Esat94,
  author = { V. Esat  and  M. Hall },
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  booktitle = {Hydroinformatics'94},
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  year = 1994,
  editor = { A. Verwey  and  A. Minns  and  V. Babovic  and  C. Maksimovi{\'c} },
  address = {Balkema, Rotterdam, The Netherlands},
  note = {}
}
@inproceedings{EshSch1992,
  isbn = {1-55860-263-1},
  year = 1993,
  publisher = {Morgan Kaufmann Publishers},
  booktitle = {Foundations of Genetic Algorithms (FOGA)},
  editor = { Darrell Whitley },
  author = { Larry J. Eshelman  and  J. David Schaffer },
  title = {Real-Coded Genetic Algorithms and Interval-Schemata},
  pages = {187--202},
  alias = {DBLP:conf/foga/EshelmanS92}
}
@inproceedings{Eshelman89crossoverbiases,
  publisher = {Morgan Kaufmann Publishers, San Mateo, CA},
  editor = { J. David Schaffer },
  year = 1989,
  booktitle = {Proc. of the Third Int. Conf. on Genetic Algorithms},
  author = { Larry J. Eshelman  and  A. Caruana  and  J. David Schaffer },
  title = {Biases in the Crossover Landscape},
  pages = {86--91}
}
@article{EssMatDau2008,
  author = {Imen Essafi and Yazid Mati and  St{\'e}phane Dauz{\`e}re-P{\`e}r{\'e}s },
  title = {A Genetic Local Search Algorithm for Minimizing Total Weighted
  Tardiness in the Job-shop Scheduling Problem},
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  year = 2008,
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  number = 8,
  pages = {2599--2616}
}
@incollection{EycSno02,
  address = { Heidelberg },
  publisher = {Springer},
  editor = { Marco Dorigo  and others },
  fulleditor = { Marco Dorigo  and  Gianni A. {Di Caro}  and  M. Sampels },
  volume = 2463,
  series = {Lecture Notes in Computer Science},
  year = 2002,
  booktitle = {Ant Algorithms, Third International Workshop, ANTS
                  2002},
  author = {C. J. Eyckelhof and M. Snoek},
  title = {Ant Systems for a Dynamic {TSP}: {Ants} Caught in a
                  Traffic Jam},
  pages = {88--99}
}
@incollection{FalLinHut2015spysmac,
  address = { Cham, Switzerland },
  publisher = {Springer},
  editor = {Heule, Marijn and Weaver, Sean},
  volume = 9340,
  series = {Lecture Notes in Computer Science},
  year = 2015,
  booktitle = {Theory and Applications of Satisfiability Testing -- {SAT}
                  2015},
  title = {{SpySMAC}: Automated configuration and performance analysis
                  of {SAT} solvers},
  author = {Falkner, Stefan and  Marius Thomas Lindauer  and  Frank Hutter },
  doi = {10.1007/978-3-319-24318-4_16},
  pages = {215--222}
}
@article{FanBif2013,
  title = {Mining big data: current status, and forecast to the future},
  author = {Fan, Wei and Bifet, Albert},
  journal = {ACM sIGKDD Explorations Newsletter},
  volume = 14,
  number = 2,
  pages = {1--5},
  year = 2013,
  publisher = {ACM}
}
@article{Fanelli2012,
  author = {Fanelli, Daniele},
  title = {Negative results are disappearing from most disciplines and
                  countries},
  journal = {Scientometrics},
  year = 2012,
  volume = 90,
  number = 3,
  pages = {891--904},
  doi = {10.1007/s11192-011-0494-7},
  abstract = {Concerns that the growing competition for funding and
                  citations might distort science are frequently discussed, but
                  have not been verified directly. Of the hypothesized
                  problems, perhaps the most worrying is a worsening of
                  positive-outcome bias. A system that disfavours negative
                  results not only distorts the scientific literature directly,
                  but might also discourage high-risk projects and pressure
                  scientists to fabricate and falsify their data. This study
                  analysed over 4,600 papers published in all disciplines
                  between 1990 and 2007, measuring the frequency of papers
                  that, having declared to have ``tested'' a hypothesis,
                  reported a positive support for it. The overall frequency of
                  positive supports has grown by over 22{\%} between 1990 and
                  2007, with significant differences between disciplines and
                  countries. The increase was stronger in the social and some
                  biomedical disciplines. The United States had published, over
                  the years, significantly fewer positive results than Asian
                  countries (and particularly Japan) but more than European
                  countries (and in particular the United
                  Kingdom). Methodological artefacts cannot explain away these
                  patterns, which support the hypotheses that research is
                  becoming less pioneering and/or that the objectivity with
                  which results are produced and published is decreasing.}
}
@inproceedings{FarAma2002nafips,
  publisher = {IEEE Service Center},
  month = jun,
  address = {Piscataway, New Jersey},
  year = 2002,
  booktitle = {Proceedings of the NAFIPS-FLINT International
                  Conference'2002},
  key = {NAFIPS},
  author = {M. Farina and P. Amato},
  title = {On the Optimal Solution Definition for Many-criteria
                  Optimization Problems},
  pages = {233--238}
}
@article{FarBinResFal2005tps,
  author = { Faria, Jr, H.  and S. Binato and  Mauricio G. C. Resende  and  D. J. Falc{\~a}o },
  title = {Power transmission network design by a greedy randomized
                  adaptive path relinking approach},
  journal = {IEEE Transactions on Power Systems},
  year = 2005,
  volume = 20,
  number = 1,
  pages = {43--49}
}
@article{FarWalSav2006hydroinf,
  author = {R. Farmani and  Godfrey A. Walters  and  Dragan A. Savic },
  title = {Evolutionary multi-objective optimization of the
                  design and operation of water distribution network:
                  total cost vs. reliability vs. water quality},
  journal = { Journal of Hydroinformatics },
  year = 2006,
  volume = 8,
  number = 3,
  pages = {165--179}
}
@article{FavMorPel06tsptw,
  author = { D. Favaretto  and  E. Moretti  and  Paola Pellegrini },
  title = {Ant colony system approach for variants of the
                  traveling salesman problem with time windows},
  journal = {Journal of Information and Optimization Sciences},
  year = 2006,
  volume = 27,
  number = 1,
  pages = {35--54}
}
@article{FavMorPel07,
  author = { D. Favaretto  and  E. Moretti  and  Paola Pellegrini },
  title = {Ant Colony System for a {VRP} with Multiple Time Windows and Multiple Visits},
  journal = {Journal of Interdisciplinary Mathematics},
  year = 2007,
  volume = 10,
  number = 2,
  pages = {263--284}
}
@incollection{FavMorPel09:sls,
  volume = 5752,
  series = {Lecture Notes in Computer Science},
  year = 2009,
  address = { Heidelberg },
  publisher = {Springer},
  booktitle = {Engineering Stochastic Local Search
                  Algorithms. Designing, Implementing and Analyzing
                  Effective Heuristics. SLS~2009},
  editor = { Thomas St{\"u}tzle  and  Mauro Birattari  and  Holger H. Hoos },
  author = { D. Favaretto  and  E. Moretti  and  Paola Pellegrini },
  title = {On the explorative behavior of {\MaxMinAntSystem}},
  pages = {115--119}
}
@inproceedings{FawHelHooKar2011icaps,
  year = 2011,
  booktitle = {Proceedings of ICAPS-PAL11},
  editor = {Karpas, Erez and Jim{\'e}nez Celorrio, Sergio and Kambhampati, Subbarao},
  author = { Chris Fawcett  and Malte Helmert and  Holger H. Hoos  and Erez Karpas
                  and Gabriele R\"{o}ger and Jendrik Seipp},
  title = {{FD-Autotune}: Domain-Specific Configuration using
                  Fast-Downward}
}
@inproceedings{FawHoos2013mic,
  year = 2013,
  booktitle = {Proceedings of MIC 2013, the 10th Metaheuristics
                  International Conference},
  key = {MIC},
  author = { Chris Fawcett  and  Holger H. Hoos },
  title = {Analysing Differences between Algorithm
                  Configurations through Ablation},
  pages = {123--132},
  eprint = {http://www.cs.ubc.ca/~hoos/Publ/FawHoo13.pdf}
}
@article{FawHoos2015ablation,
  title = {Analysing Differences Between Algorithm Configurations
                  through Ablation},
  author = { Chris Fawcett  and  Holger H. Hoos },
  journal = {Journal of Heuristics},
  pages = {431--458},
  volume = 22,
  number = 4,
  year = 2016
}
@article{FeoRes1989,
  author = { T. A. Feo  and  Mauricio G. C. Resende },
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                  Difficult Set Covering Problem},
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  year = 1989,
  volume = 8,
  number = 2,
  pages = {67--71}
}
@article{FeoRes1995,
  author = { T. A. Feo  and  Mauricio G. C. Resende },
  title = {Greedy Randomized Adaptive Search Procedures},
  journal = {Journal of Global Optimization},
  year = 1995,
  volume = 6,
  number = 2,
  pages = {109--113},
  alias = {FR95}
}
@article{FeoResSmi1994,
  author = { T. A. Feo  and  Mauricio G. C. Resende  and S. H. Smith},
  title = {A Greedy Randomized Adaptive Search Procedure for Maximum Independent Set},
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  year = 1994,
  volume = 42,
  pages = {860--878},
  month = oct
}
@incollection{FerAlvDiaIglEna2014ants,
  volume = 8667,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  editor = { Marco Dorigo  and others },
  year = 2014,
  booktitle = {Swarm Intelligence, 9th International Conference, ANTS 2014},
  author = { Fern\'{a}ndez, Silvino  and  \'{A}lvarez, Segundo  and  Diego D{\'i}az  and Miguel Iglesias and Borja Ena},
  title = {Scheduling a Galvanizing Line by Ant Colony Optimization},
  doi = {10.1007/978-3-319-09952-1_13},
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}
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  address = { New York, NY },
  publisher = {ACM Press},
  year = 2015,
  editor = { Jim{\'e}nez Laredo, Juan Luis  and Sara Silva and  Anna I. Esparcia{-}Alc{\'{a}}zar },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference Companion, GECCO Companion 2015},
  author = { Fern\'{a}ndez, Silvino  and  \'{A}lvarez, Segundo  and Malatsetxebarria, Eneko and Valledor, Pablo and  Diego D{\'i}az },
  title = {Performance Comparison of Ant Colony Algorithms for the
                  Scheduling of Steel Production Lines},
  doi = {10.1145/2739482.2764658},
  keywords = {irace}
}
@incollection{FerFonGas2007:gecco,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2007,
  editor = {Dirk Thierens and others},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2007},
  author = { Jos{\'e} C. Ferreira  and  Carlos M. Fonseca  and  Ant{\'o}nio Gaspar{-}Cunha },
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  pages = {789--796}
}
@article{FerFra2014,
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  author = { Victor Fernandez-Viagas  and  Jose M. Frami{\~n}{\'a}n },
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  year = 2014,
  pages = {60--67},
  volume = 45
}
@article{FerFra2017,
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}
@article{FerFra2018,
  author = { Victor Fernandez-Viagas  and  Jose M. Frami{\~n}{\'a}n },
  title = {Iterated-greedy-based algorithms with beam search initialization for the permutation flowshop to minimise total tardiness},
  journal = {Expert Systems with Applications},
  year = 2018,
  volume = 94,
  pages = {58--69}
}
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  doi = {10.1155/2016/3871046},
  journal = {Mathematical Problems in Engineering},
  pages = {1--19},
  title = {Intelligent Testing of Traffic Light Programs: Validation in
                  Smart Mobility Scenarios},
  volume = 2016,
  year = 2016
}
@article{FerGuiRamJua2016,
  author = {Alberto Ferrer and Daniel Guimarans and  Helena {Ramalhinho Louren{\c c}o}  and Angel A. Juan},
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               Costs},
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}
@article{FerLopAlb2019asoc,
  author = { Javier Ferrer  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Alba, Enrique },
  title = {Reliable Simulation-Optimization of Traffic Lights in a
                  Real-World City},
  journal = {Applied Soft Computing},
  year = 2019,
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  doi = {10.1016/j.asoc.2019.03.016},
  supplement = {https://github.com/MLopez-Ibanez/irace-sumo}
}
@article{FerNavBer2009:ejor,
  author = { Eduardo Fernandez  and  Jorge Navarro  and  Sergio Bernal },
  title = {Multicriteria Sorting Using a Valued Indifference Relation Under a Preference Disaggregation Paradigm},
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  year = 2009
}
@article{FerRuiFra2016,
  author = { Victor Fernandez-Viagas  and  Rub{\'e}n Ruiz  and  Jose M. Frami{\~n}{\'a}n },
  title = {A New Vision of Approximate Methods for the Permutation Flowshop to Minimise Makespan: State-of-the-art and Computational Evaluation},
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  year = 2017
}
@article{FerUrr2010do,
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                  problem with time windows},
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  keywords = {TSPTW}
}
@incollection{FerValDia2016gecco,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2016,
  editor = { Tobias Friedrich  and  Frank Neumann  and  Andrew M. Sutton },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference Companion, GECCO Companion 2016},
  author = { Fern\'{a}ndez, Silvino  and Valledor, Pablo and  Diego D{\'i}az  and Malatsetxebarria, Eneko and Iglesias, Miguel},
  title = {Criticality of Response Time in the usage of Metaheuristics
                  in Industry},
  pages = {937--940}
}
@article{FerValFra2018,
  author = { Victor Fernandez-Viagas  and  Jorge M. S. Valente  and  Jose M. Frami{\~n}{\'a}n },
  title = {Iterated-greedy-based algorithms with Beam Search Initialization for the Permutation Flowshop to Minimise Total Tardiness},
  journal = {Expert Systems with Applications},
  volume = 94,
  pages = {58--69},
  year = 2018
}
@inproceedings{FeuKleEggSprBluHut2015autosklearn,
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  year = 2015,
  booktitle = {Advances in Neural Information Processing Systems (NIPS
                  28)},
  editor = {Corinna Cortes and Neil D. Lawrence and Daniel D. Lee and
                  Masashi Sugiyama and Roman Garnett},
  title = {Efficient and robust automated machine learning},
  author = {Feurer, Matthias and Klein, Aaron and Eggensperger, Katharina
                  and Springenberg, Jost and Blum, Manuel and  Frank Hutter },
  pages = {2962--2970},
  epub = {http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf}
}
@article{FiaDaCSchSeb2010:amai,
  author = { {\'A}lvaro Fialho  and Luis {Da Costa} and  Marc Schoenauer  and  Mich{\`e}le Sebag },
  title = {Analyzing Bandit-based Adaptive Operator Selection
                  Mechanisms},
  journal = {Annals of Mathematics and Artificial Intelligence},
  year = 2010,
  volume = 60,
  number = {1--2},
  pages = {25--64}
}
@incollection{FiaRosScho2010comp,
  volume = 6238,
  year = 2010,
  address = { Heidelberg },
  publisher = {Springer},
  editor = {Schaefer, Robert and Cotta, Carlos and Kolodziej,
                  Joanna and  G{\"u}nther Rudolph },
  series = {Lecture Notes in Computer Science},
  booktitle = {Parallel Problem Solving from Nature, PPSN XI},
  title = {Comparison-based adaptive strategy selection with bandits in
                  differential evolution},
  author = { {\'A}lvaro Fialho  and Ros, Raymond and  Marc Schoenauer  and  Mich{\`e}le Sebag },
  pages = {194--203}
}
@incollection{FiaSchoSeb2010fauc,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2010,
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference Companion, GECCO Companion 2010},
  editor = {Martin Pelikan and  J{\"u}rgen Branke },
  title = {Fitness-{AUC} bandit adaptive strategy selection vs.\ the
                  probability matching one within differential evolution: an
                  empirical comparison on the {BBOB-2010} noiseless testbed},
  author = { {\'A}lvaro Fialho  and  Marc Schoenauer  and  Mich{\`e}le Sebag },
  pages = {1535--1542}
}
@incollection{FiaSchoSeb2010toward,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2010,
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2010},
  editor = {Martin Pelikan and  J{\"u}rgen Branke },
  title = {Toward comparison-based adaptive operator selection},
  author = { {\'A}lvaro Fialho  and  Marc Schoenauer  and  Mich{\`e}le Sebag },
  pages = {767--774},
  annote = {Proposed F-AUC and F-SR}
}
@phdthesis{Fialho2010PhD,
  title = {Adaptive operator selection for optimization},
  author = { {\'A}lvaro Fialho },
  year = 2010,
  school = {Universit{\'e} Paris Sud-Paris XI}
}
@article{Fie2000:siamo,
  author = { Mark J. Fielding },
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}
@incollection{FieEve2013visualising,
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  year = 2013,
  volume = 7811,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2013},
  editor = { Robin C. Purshouse  and  Peter J. Fleming  and  Carlos M. Fonseca  and  Salvatore Greco  and Jane Shaw},
  title = {Visualising high-dimensional {Pareto} relationships in
                  two-dimensional scatterplots},
  author = { Jonathan E. Fieldsend  and  Everson, Richard M. },
  pages = {558--572},
  doi = {10.1007/978-3-642-37140-0_42}
}
@article{FieEveSing2003tec,
  title = {Using unconstrained elite archives for multiobjective
                  optimization},
  author = { Jonathan E. Fieldsend  and  Everson, Richard M.  and Singh, Sameer},
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  year = 2003
}
@article{FigFonHalKla2017easy,
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                  Easy-Towards a Categorization of Tractable Multiobjective
                  Combinatorial Optimization Problems},
  author = { Jos{\'e} Rui Figueira  and  Carlos M. Fonseca  and Halffmann, Pascal and  Kathrin Klamroth  and  Lu{\'i}s Paquete  and Ruzika, Stefan and Schulze,
                  Britta and Stiglmayr, Michael and Willems, David},
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  year = 2017,
  publisher = {Wiley Online Library},
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}
@incollection{FinVos2002,
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  publisher = {Kluwer Academic Publishers, Boston, MA},
  editor = { Stefan Vo{\ss}  and  David L. Woodruff },
  booktitle = {Optimization Software Class Libraries},
  author = {Andreas Fink and  Stefan Vo{\ss} },
  title = {{HotFrame}: A Heuristic Optimization Framework},
  pages = {81--154}
}
@incollection{FisDhaJou2015lion,
  address = { Heidelberg },
  publisher = {Springer},
  volume = 8994,
  editor = {Clarisse Dhaenens and  Laetitia Jourdan  and  Marie-El{\'e}onore Marmion },
  series = {Lecture Notes in Computer Science},
  year = 2015,
  booktitle = {Learning and Intelligent Optimization, 9th International
                  Conference, LION 9},
  author = {Benjamin Fisset and Clarisse Dhaenens and  Laetitia Jourdan },
  title = {{MO-Mine$_\text{clust}$}: A Framework for Multi-Objective
                  Clustering},
  pages = {293--305},
  keywords = {irace}
}
@article{FisGloLod2005,
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  author = { Matteo Fischetti  and  Fred Glover  and  Andrea Lodi },
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  year = 2005,
  publisher = {Springer}
}
@article{FisLod2003:mp,
  author = { Matteo Fischetti  and  Andrea Lodi },
  title = {Local Branching},
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  year = 2003,
  volume = 98,
  pages = {23--47}
}
@article{FisMon2014joh,
  title = {Proximity search for 0-1 mixed-integer convex programming},
  author = { Matteo Fischetti  and  Monaci, Michele },
  journal = {Journal of Heuristics},
  volume = 20,
  number = 6,
  pages = {709--731},
  year = 2014,
  publisher = {Springer}
}
@article{FisMon2014or,
  author = { Matteo Fischetti  and  Monaci, Michele },
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  year = 2014,
  doi = {10.1287/opre.2013.1231},
  annote = {\url{http://mat.tepper.cmu.edu/blog/?p=1695}},
  abstract = { High sensitivity to initial conditions is generally viewed
                  as a drawback of tree search methods because it leads to
                  erratic behavior to be mitigated somehow. In this paper we
                  investigate the opposite viewpoint and consider this behavior
                  as an opportunity to exploit. Our working hypothesis is that
                  erraticism is in fact just a consequence of the exponential
                  nature of tree search that acts as a chaotic amplifier, so it
                  is largely unavoidable. We propose a bet-and-run approach to
                  actually turn erraticism to one's advantage. The idea is to
                  make a number of short sample runs with randomized initial
                  conditions, to bet on the "most promising" run selected
                  according to certain simple criteria, and to bring it to
                  completion. Computational results on a large testbed of mixed
                  integer linear programs from the literature are presented,
                  showing the potential of this approach even when embedded in
                  a proof-of-concept implementation. }
}
@article{FisMonSal2012:or,
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}
@article{FisSal2009,
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  author = { Matteo Fischetti  and Salvagnin, Domenico},
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  pages = {201--222},
  year = 2009,
  publisher = {Springer}
}
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  editor = { Carlos A. {Coello Coello}  and A. H. Aguirre and  Eckart Zitzler },
  year = 2005,
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  title = {Many-objective optimization: An engineering design
                  perspective},
  author = { Peter J. Fleming  and  Robin C. Purshouse  and Lygoe, Robert J.},
  pages = {14--32}
}
@incollection{Fleischer2003emo,
  address = { Heidelberg },
  publisher = {Springer},
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  series = {Lecture Notes in Computer Science},
  shorteditor = { Carlos M. Fonseca  and  others},
  editor = { Carlos M. Fonseca  and  Peter J. Fleming  and  Eckart Zitzler  and  Kalyanmoy Deb  and  Lothar Thiele },
  year = 2003,
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2003},
  author = {M. Fleischer},
  title = {The Measure of {Pareto} Optima. Applications to
                  Multi-objective Metaheuristics},
  pages = {519--533}
}
@book{Fletcher1987,
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  annote = {BFGS}
}
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@article{FliVer1986,
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  keywords = {Mallows model, ranking, probabilistic models},
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}
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@article{FloKel2010plos,
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                  of {Darwinian} Selection},
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  year = 2010,
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  alias = {Floreano:2010eg}
}
@incollection{FloMon1994automatic,
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                  Simulation of adaptive behavior: From Animals to Animats 3},
  editor = {Cliff, D. and Husbands, P. and Meyer, J.-A. and Wilson, S.},
  title = {Automatic creation of an autonomous agent: Genetic evolution
                  of a neural network driven robot},
  author = {Floreano, Dario and Mondada, Francesco},
  annote = {LIS-CONF-1994-003},
  pages = {421--430}
}
@article{FloUrz2000nn,
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  alias = {Floreano:2000uy}
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  booktitle = {Handbook of Metaheuristics},
  author = { Filippo Focacci  and Fran{\c{c}}ois Laburthe and  Andrea Lodi },
  title = {Local Search and Constraint Programming},
  pages = {369--403}
}
@article{FocLodMil02tsptw,
  author = { Filippo Focacci  and  Andrea Lodi  and  Michela Milano },
  title = {A Hybrid Exact Algorithm for the {TSPTW}},
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  volume = 14,
  pages = {403--417}
}
@book{FogOweWal1966,
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}
@book{Fogel95:EvolutionaryComputation,
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  publisher = {IEEE Press}
}
@inproceedings{FonFle1993:moga,
  isbn = {1-55860-299-2},
  year = 1993,
  publisher = {Morgan Kaufmann Publishers},
  booktitle = {ICGA},
  editor = {Stephanie Forrest},
  author = { Carlos M. Fonseca  and  Peter J. Fleming },
  title = {Genetic Algorithms for Multiobjective Optimization:
                  Formulation, Discussion and Generalization},
  pages = {416--423},
  eprint = {http://eden.dei.uc.pt/~cmfonsec/fonseca-ga93-reprint.pdf},
  annote = {Proposes MOGA}
}
@incollection{FonFle1996:ppsn,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 1141,
  editor = {H.-M. Voigt and others},
  aeditor = {H.-M. Voigt and W. Ebeling and  Rechenberg, Ingo  and  Hans-Paul Schwefel },
  year = 1996,
  booktitle = {Parallel Problem Solving from Nature -- PPSN IV},
  author = { Carlos M. Fonseca  and  Peter J. Fleming },
  title = {On the Performance Assessment and Comparison of
                  Stochastic Multiobjective Optimizers},
  pages = {584--593},
  alias = {Fonseca96}
}
@article{FonFle1998:tsmca,
  author = { Carlos M. Fonseca  and  Peter J. Fleming },
  title = {Multiobjective Optimization and Multiple Constraint
                  Handling with Evolutionary Algorithms ({II}):
                  {Application} Example},
  journal = {IEEE Transactions on Systems, Man, and Cybernetics -- Part A},
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  volume = 28,
  number = 1,
  pages = {38--44},
  month = jan,
  doi = {10.1109/3468.650320}
}
@article{FonFle1998:tsmca1,
  author = { Carlos M. Fonseca  and  Peter J. Fleming },
  title = {Multiobjective Optimization and Multiple Constraint
                  Handling with Evolutionary Algorithms ({I}): {A}
                  Unified Formulation},
  journal = {IEEE Transactions on Systems, Man, and Cybernetics -- Part A},
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  number = 1,
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  month = jan,
  doi = {10.1109/3468.650319}
}
@incollection{FonFon2012,
  address = { Heidelberg },
  publisher = {Springer},
  editor = {Hao, Jin-Kao and Legrand, Pierrick and Collet, Pierre and
                  Monmarch{\'e}, Nicolas and Lutton, Evelyne and Schoenauer,
                  Marc},
  shorteditor = {Hao, Jin-Kao and others},
  volume = 7401,
  series = {Lecture Notes in Computer Science},
  year = 2012,
  booktitle = {Artificial Evolution: 10th International Conference, Evolution Artificielle, EA, 2011},
  author = { Viviane {Grunert da Fonseca}  and  Carlos M. Fonseca },
  title = {The Relationship between the Covered Fraction, Completeness
                  and Hypervolume Indicators},
  pages = {25--36}
}
@incollection{FonGruPaq2005:emo,
  address = { Heidelberg },
  publisher = {Springer},
  volume = 3410,
  series = {Lecture Notes in Computer Science},
  shorteditor = { Carlos A. {Coello Coello}  and others},
  editor = { Carlos A. {Coello Coello}  and A. H. Aguirre and  Eckart Zitzler },
  year = 2005,
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2005},
  author = { Carlos M. Fonseca  and  Viviane {Grunert da Fonseca}  and  Lu{\'i}s Paquete },
  title = {Exploring the Performance of Stochastic
                  Multiobjective Optimisers with the Second-Order
                  Attainment Function},
  pages = {250--264},
  alias = {lpaquete:15},
  doi = {10.1007/978-3-540-31880-4_18},
  abstract = {The attainment function has been proposed as a
                  measure of the statistical performance of stochastic
                  multiobjective optimisers which encompasses both the
                  quality of individual non-dominated solutions in
                  objective space and their spread along the trade-off
                  surface. It has also been related to results from
                  random closed-set theory, and cast as a mean-like,
                  first-order moment measure of the outcomes of
                  multiobjective optimisers. In this work, the use of
                  more informative, second-order moment measures for
                  the evaluation and comparison of multiobjective
                  optimiser performance is explored experimentally,
                  with emphasis on the interpretability of the
                  results.}
}
@incollection{FonGueLopPaq2011emo,
  address = { Heidelberg },
  publisher = {Springer},
  year = 2011,
  series = {Lecture Notes in Computer Science},
  volume = 6576,
  editor = { Takahashi, R. H. C.  and others},
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2011},
  author = { Carlos M. Fonseca  and  Andreia P. Guerreiro  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete },
  title = {On the Computation of the Empirical Attainment Function},
  doi = {10.1007/978-3-642-19893-9_8},
  pages = {106--120}
}
@incollection{FonPaqLop06:hypervolume,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  month = jul,
  year = 2006,
  booktitle = {Proceedings of  the 2006 Congress on Evolutionary Computation (CEC 2006)},
  key = {IEEE CEC},
  author = { Carlos M. Fonseca  and  Lu{\'i}s Paquete  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {An improved dimension-\hspace{0pt}sweep
                  algorithm for the hypervolume indicator},
  pages = {1157--1163},
  doi = {10.1109/CEC.2006.1688440},
  abstract = {This paper presents a recursive, dimension-sweep
                  algorithm for computing the hypervolume indicator of
                  the quality of a set of $n$ non-dominated points in
                  $d>2$ dimensions. It improves upon the existing HSO
                  (Hypervolume by Slicing Objectives) algorithm by
                  pruning the recursion tree to avoid repeated
                  dominance checks and the recalculation of partial
                  hypervolumes. Additionally, it incorporates a recent
                  result for the three-dimensional special case.  The
                  proposed algorithm achieves $O(n^{d-2} \log n)$ time
                  and linear space complexity in the worst-case, but
                  experimental results show that the pruning
                  techniques used may reduce the time complexity
                  exponent even further.}
}
@incollection{FonTag2020repro,
  author = {Fonseca Cacho, Jorge Ram{\'o}n and Taghva, Kazem},
  title = {The State of Reproducible Research in Computer Science},
  doi = {10.1007/978-3-030-43020-7_68},
  series = {Advances in Intelligent Systems and Computing},
  booktitle = {17th {International} {Conference} on {Information}
                  {Technology}-{New} {Generations} ({ITNG} 2020)},
  abstract = {Reproducible research is the cornerstone of cumulative
                  science and yet is one of the most serious crisis that we
                  face today in all fields. This paper aims to describe the
                  ongoing reproducible research crisis along with
                  counter-arguments of whether it really is a crisis, suggest
                  solutions to problems limiting reproducible research along
                  with the tools to implement such solutions by covering the
                  latest publications involving reproducible research.},
  language = {en},
  publisher = {Springer International Publishing},
  editor = {Latifi, Shahram},
  year = 2020,
  keywords = {Docker, Improving transparency, OCR, Open science,
                  Replicability, Reproducibility},
  pages = {519--524}
}
@article{ForKea2009surrogate,
  author = {Forrester, Alexander I. J. and Keane, Andy J.},
  title = {Recent advances in surrogate-based optimization},
  journal = {Progress in Aerospace Sciences},
  volume = 45,
  number = {1-3},
  pages = {50--79},
  doi = {10.1016/j.paerosci.2008.11.001},
  year = 2009,
  keywords = {Kriging; Gaussian Process; EGO; Design of Experiments}
}
@incollection{FosBickHardKok2007,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2007,
  editor = {Dirk Thierens and others},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2007},
  author = {Manuel F\"{o}rster and Bettina Bickel and Bernd
                  Hardung and Gabriella K\'{o}kai},
  title = {Self-adaptive ant colony optimisation applied to
                  function allocation in vehicle networks},
  pages = {1991--1998}
}
@inproceedings{FosHugObr2020,
  aurl = {https://dl.acm.org/citation.cfm?id=3377930},
  location = {Canc{\'u}n, Mexico},
  doi = {10.1145/3377930},
  isbn = {978-1-4503-7128-5},
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2020,
  editor = { Carlos A. {Coello Coello} },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2020},
  title = {Do sophisticated evolutionary algorithms perform better than simple ones?},
  author = {Foster, Michael and Hughes, Matthew and O'Brien, George and  Oliveto, Pietro S.  and Pyle, James and  Dirk Sudholt  and Williams, James},
  pages = {184--192}
}
@book{FouGayKer2002,
  author = {Robert Fourer and David M. Gay and Brian W. Kernighan},
  title = {{AMPL}: A Modeling Language for Mathematical Programming},
  publisher = {Duxbury},
  year = 2002,
  edition = {2nd}
}
@inproceedings{Fox1992uniting,
  author = { Bennett L. Fox },
  title = {Uniting probabilistic methods for optimization},
  booktitle = {Proceedings of the 24th conference on Winter simulation},
  pages = {500--505},
  year = 1992,
  organization = {ACM}
}
@article{Fox1993integrating,
  author = { Bennett L. Fox },
  title = {Integrating and accelerating tabu search, simulated
                  annealing, and genetic algorithms},
  journal = {Annals of Operations Research},
  volume = 41,
  number = 2,
  pages = {47--67},
  year = 1993,
  publisher = {Springer}
}
@incollection{Fox1995simulated,
  author = { Bennett L. Fox },
  title = {Simulated annealing: folklore, facts, and directions},
  booktitle = {Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing},
  pages = {17--48},
  year = 1995,
  publisher = {Springer}
}
@article{Fra2018tutorial,
  author = {Peter I. Frazier},
  title = {A Tutorial on {Bayesian} Optimization},
  journal = {Arxiv preprint arXiv:1807.02811},
  year = 2018,
  url = {http://arxiv.org/abs/1807.02811}
}
@phdthesis{Fra95:phd,
  author = { C. B. Fraser },
  title = {Subsequences and Supersequences of Strings},
  school = {University of Glasgow},
  year = 1995
}
@article{FraBraBru2014automode,
  author = {G. Francesca and  M. Brambilla and 
                  A. Brutschy and  Vito Trianni  and  Mauro Birattari },
  title = {{AutoMoDe}: A Novel Approach to the Automatic Design
                  of Control Software for Robot Swarms},
  journal = {Swarm Intelligence},
  year = 2014,
  volume = 8,
  number = 2,
  pages = {89--112},
  doi = {10.1007/s11721-014-0092-4}
}
@article{FraBraBru2015automode,
  author = {Francesca, Gianpiero and Brambilla, Manuele and Brutschy,
                  Arne and Garattoni, Lorenzo and Miletitch, Roman and
                  Podevijn, Gaetan and Reina, Andreagiovanni and Soleymani,
                  Touraj and Salvaro, Mattia and Pinciroli, Carlo and Mascia,
                  Franco and  Vito Trianni  and  Mauro Birattari },
  title = {{AutoMoDe-Chocolate}: Automatic Design of Control Software
                  for Robot Swarms},
  year = 2015,
  journal = {Swarm Intelligence},
  doi = {10.1007/s11721-015-0107-9},
  keywords = {Swarm robotics; Automatic design; AutoMoDe},
  language = {English}
}
@article{FraGupLei2004,
  title = {A Review and Classification of Heuristics for Permutation Flow-shop Scheduling with Makespan Objective},
  author = { Jose M. Frami{\~n}{\'a}n  and Jatinder N.D. Gupta and  Rainer Leisten },
  journal = {Journal of the Operational Research Society},
  year = 2004,
  number = 12,
  pages = {1243--1255},
  volume = 55
}
@inproceedings{FraGyoNad2020,
  url = {https://bnaic.liacs.leidenuniv.nl/wordpress/wp-content/uploads/bnaic2020proceedings.pdf},
  year = 2020,
  editor = {Cao, Lu and Kosters, Walter and Lijffijt, Jefrey},
  booktitle = {Proceedings of the 32nd Benelux Conference on Artificial Intelligence,
                  BNAIC 2020, Leiden, The Netherlands, 19-20 November 2020},
  author = { Alberto Franzin  and Gyory, Rapha\"el and Nad\'e, Jean-Charles and
                 Aubert, Guillaume and Klenkle, Georges and  Hughes Bersini },
  title = {Phil\'{e}as: Anomaly Detection for {IoT} Monitoring},
  pages = {56--70}
}
@book{FraLeiRui2014,
  title = {Manufacturing Scheduling Systems: An Integrated View on
                  Models, Methods, and Tools},
  author = { Jose M. Frami{\~n}{\'a}n  and  Rainer Leisten  and  Rub{\'e}n Ruiz },
  publisher = {Springer},
  address = { New York, NY },
  year = 2014
}
@article{FraPerStu2018ol,
  author = { Alberto Franzin  and   P{\'e}rez C{\'a}ceres, Leslie and  Thomas St{\"u}tzle },
  title = {Effect of Transformations of Numerical Parameters
                 in Automatic Algorithm Configuration},
  journal = {Optimization Letters},
  year = 2018,
  volume = 12,
  number = 8,
  pages = {1741--1753},
  doi = {10.1007/s11590-018-1240-3}
}
@article{FraSamDiC2016,
  author = { Alberto Franzin  and Sambo, Francesco and Di Camillo, Barbara},
  title = {\rpackage{bnstruct}: an {R} package for {Bayesian} Network structure learning
                  in the presence of missing data},
  journal = {Bioinformatics},
  year = 2016,
  volume = 33,
  number = 8,
  pages = {1250--1252}
}
@incollection{FraStu2016:gecco,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2016,
  editor = { Tobias Friedrich  and  Frank Neumann  and  Andrew M. Sutton },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference Companion, GECCO Companion 2016},
  author = { Alberto Franzin  and  Thomas St{\"u}tzle },
  title = {Exploration of Metaheuristics through Automatic Algorithm Configuration Techniques and Algorithmic Frameworks},
  pages = {1341--1347}
}
@inproceedings{FraStu2017:EA,
  address = { Heidelberg },
  publisher = {Springer},
  editor = {Lutton, Evelyne and Legrand, Pierrick and Parrend, Pierre and  Nicolas Monmarch{\'e}  and  Marc Schoenauer },
  volume = 10764,
  series = {Lecture Notes in Computer Science},
  year = 2017,
  booktitle = {EA 2017: Artificial Evolution},
  author = { Alberto Franzin  and  Thomas St{\"u}tzle },
  title = {Comparison of Acceptance Criteria in Randomized Local Searches},
  pages = {16--29}
}
@misc{FraStu2018-supp,
  author = { Alberto Franzin  and  Thomas St{\"u}tzle },
  title = {Revisiting Simulated Annealing: a Component-Based Analysis: {Supplementaty} Material},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2018-001}},
  year = 2018
}
@article{FraStu2019:cor,
  author = { Alberto Franzin  and  Thomas St{\"u}tzle },
  title = {Revisiting Simulated Annealing: A Component-Based Analysis},
  journal = {Computers \& Operations Research},
  volume = 104,
  pages = {191--206},
  year = 2019,
  doi = {10.1016/j.cor.2018.12.015}
}
@inproceedings{FraStu2020:lmca,
  year = 2020,
  editor = {Vlastelica, Marin and Song, Jialin and Ferber, Aaron and Amos, Brandon and
                  Martius, Georg and Dilkina, Bistra and Yue, Yisong},
  booktitle = {Learning Meets Combinatorial Algorithms Workshop at NeurIPS 2020,
                  {LMCA} 2020, Vancouver, Canada, December 12, 2020},
  author = { Alberto Franzin  and  Thomas St{\"u}tzle },
  title = {Towards transferring algorithm configurations across problems},
  pages = {1--6}
}
@incollection{FraStu2020:tailor,
  year = 2020,
  volume = 12641,
  address = { Cham, Switzerland },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  booktitle = {Trustworthy AI -- Integrating Learning, Optimization and
                  Reasoning. TAILOR 2020},
  editor = {Fredrik Heintz and Michela Milano and Barry O'Sullivan},
  author = { Alberto Franzin  and  Thomas St{\"u}tzle },
  title = {A causal framework for understanding optimisation algorithms},
  pages = {140--145}
}
@misc{FraStu2021-supp,
  author = { Alberto Franzin  and  Thomas St{\"u}tzle },
  title = {A Landscape-based Analysis of Fixed Temperature and Simulated Annealing: {Supplementaty} Material},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2021-002}},
  year = 2021
}
@article{FreFleGui2015aggregation,
  title = {Aggregation trees for visualization and dimension reduction
                  in many-objective optimization},
  author = {de Freitas, Alan R. R. and Fleming, Peter J. and
                  Guimar{\~a}es, Frederico G.},
  journal = {Information Sciences},
  volume = 298,
  pages = {288--314},
  year = 2015,
  publisher = {Elsevier}
}
@inproceedings{FreMer1996icec,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  year = 1996,
  editor = { Thomas B{\"a}ck  and  T. Fukuda  and  Zbigniew Michalewicz },
  booktitle = {Proceedings of the 1996 IEEE International Conference on
                  Evolutionary Computation (ICEC'96)},
  author = {B. Freisleben and P. Merz},
  title = {A Genetic Local Search Algorithm for Solving
                  Symmetric and Asymmetric Traveling Salesman
                  Problems},
  pages = {616--621}
}
@article{FriChaMar2010:ijor,
  author = { Hela Frikha  and  Habib Chabchoub  and  Jean-Marc Martel },
  title = {Inferring criteria's relative importance coefficients
                  in {PROMETHEE II}},
  journal = {International Journal of Operational Research},
  year = 2010,
  volume = 7,
  number = 2,
  pages = {257--275}
}
@incollection{FriGobQuiWag2018ppsn,
  volume = {11101},
  year = 2018,
  address = { Cham, Switzerland },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { Anne Auger  and  Carlos M. Fonseca  and Louren{\c c}o, N. and Machado,
                  P. and  Lu{\'i}s Paquete  and  Darrell Whitley },
  booktitle = {Parallel Problem Solving from Nature - PPSN XV},
  author = { Tobias Friedrich  and G{\"o}bel, Andreas and Quinzan, Francesco and  Markus Wagner },
  title = {Heavy-Tailed Mutation Operators in Single-Objective
                  Combinatorial Optimization},
  pages = {134--145},
  abstract = {A core feature of evolutionary algorithms is their mutation
                  operator. Recently, much attention has been devoted to the
                  study of mutation operators with dynamic and non-uniform
                  mutation rates. Following up on this line of work, we propose
                  a new mutation operator and analyze its performance on the
                  (1+1) Evolutionary Algorithm (EA). Our analyses show that
                  this mutation operator competes with pre-existing ones, when
                  used by the (1+1)-EA on classes of problems for which
                  results on the other mutation operators are available. We
                  present a ``jump'' function for which the performance of the
                  (1+1)-EA using any static uniform mutation and any restart
                  strategy can be worse than the performance of the (1+1)-EA
                  using our mutation operator with no restarts. We show that
                  the (1+1)-EA using our mutation operator finds a
                  (1/3)-approximation ratio on any non-negative submodular
                  function in polynomial time. This performance matches that of
                  combinatorial local search algorithms specifically designed
                  to solve this problem.}
}
@article{FriJoh05FFTW,
  author = {Matteo Frigo and Steven G. Johnson},
  title = {The Design and Implementation of {FFTW3}},
  journal = {Proceedings of the IEEE},
  year = 2005,
  volume = 93,
  number = 2,
  pages = {216--231},
  note = {Special issue on ``Program Generation, Optimization, and Platform Adaptation''}
}
@incollection{FriKotKre2015gecco,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2015,
  editor = {Sara Silva and  Anna I. Esparcia{-}Alc{\'{a}}zar },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2015},
  author = { Tobias Friedrich  and K{\"o}tzing, Timo  and Krejca, Martin S. and  Andrew M. Sutton },
  title = {Robustness of Ant Colony Optimization to Noise},
  pages = {17--24},
  numpages = 8,
  doi = {10.1145/2739480.2754723},
  keywords = {ant colony optimization, noisy fitness, run time analysis,
                  theory}
}
@incollection{FriKotWag2017:aaai,
  publisher = {{AAAI} Press},
  month = feb,
  year = 2017,
  editor = {Satinder P. Singh and Shaul Markovitch},
  booktitle = {Proceedings of  the {AAAI} Conference on Artificial Intelligence},
  author = { Tobias Friedrich  and K{\"o}tzing, Timo  and  Markus Wagner },
  title = {A Generic Bet-and-Run Strategy for Speeding Up Stochastic Local Search},
  pages = {801--807}
}
@incollection{FriQuiWag2018mutation,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2018,
  editor = { Aguirre, Hern\'{a}n E.  and Keiki Takadama},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2018},
  author = { Tobias Friedrich  and Quinzan, Francesco and  Markus Wagner },
  title = {Escaping Large Deceptive Basins of Attraction with
                  Heavy-tailed Mutation Operators},
  pages = {293--300},
  numpages = {8},
  doi = {10.1145/3205455.3205515},
  acmid = {3205515},
  keywords = {combinatorial optimization, heavy-tailed mutation,
                  single-objective optimization, experiments-motivated theory,
                  irace}
}
@article{Friedman1937use,
  title = {The use of ranks to avoid the assumption of normality
                  implicit in the analysis of variance},
  author = {Friedman, Milton},
  journal = {Journal of the American Statistical Association},
  volume = 32,
  number = 200,
  pages = {675--701},
  year = 1937
}
@inproceedings{Friendly1991stat,
  title = {Statistical graphics for multivariate data},
  author = {Friendly, Michael},
  year = 1991,
  booktitle = {SAS Conference Proceedings: SAS Users Group International 16
                  (SUGI 16)},
  annote = {February 17-20, 1991, New Orleans, Louisiana, 297 papers}
}
@article{FuEglLi2008,
  author = {Z Fu and R Eglese and L Y O Li},
  title = {A unified tabu search algorithm for vehicle routing problems with soft time windows},
  journal = {Journal of the Operational Research Society},
  volume = 59,
  number = 5,
  pages = {663--673},
  year = 2008
}
@book{FudTir83,
  author = {Fudenberg, D. and Tirole, J.},
  year = 1983,
  title = {Game Theory},
  publisher = {MIT Press, Cambridge, MA}
}
@article{FueDoeHar09ejor,
  author = { Guenther Fuellerer  and  Karl F. Doerner  and  Richard F. Hartl  and  Manuel Iori },
  title = {Metaheuristics for vehicle routing problems with
                  three-dimensional loading constraints},
  journal = {European Journal of Operational Research},
  year = 2009,
  volume = 201,
  number = 3,
  pages = {751--759},
  doi = {10.1016/j.ejor.2009.03.046}
}
@article{FueDoeHarIor09,
  author = { Guenther Fuellerer  and  Karl F. Doerner  and  Richard F. Hartl  and  Manuel Iori },
  title = {Ant colony optimization for the two-dimensional
                  loading vehicle routing problem},
  journal = {Computers \& Operations Research},
  year = 2009,
  volume = 36,
  number = 3,
  pages = {655--673}
}
@incollection{Fuk2004gecco,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 3103,
  editor = { Kalyanmoy Deb and others},
  year = 2004,
  booktitle = {Proceedings of the Genetic and Evolutionary
                  Computation Conference, GECCO 2004, Part II},
  title = {Evolving Local Search Heuristics for {SAT} Using
                  Genetic Programming},
  author = { Fukunaga, Alex S. },
  abstract = {Satisfiability testing ({SAT)} is a very active area
                  of research today, with numerous real-world
                  applications. We describe {CLASS2.0}, a genetic
                  programming system for semi-automatically designing
                  {SAT} local search heuristics. An empirical
                  comparison shows that that the heuristics generated
                  by our {GP} system outperform the state of the art
                  human-designed local search algorithms, as well as
                  previously proposed evolutionary approaches, with
                  respect to both runtime as well as search efficiency
                  (number of variable flips to solve a problem).},
  pages = {483--494}
}
@article{Fuk2008ec,
  title = {Automated Discovery of Local Search Heuristics for
                  Satisfiability Testing},
  author = { Fukunaga, Alex S. },
  number = 1,
  journal = {Evolutionary Computation},
  month = mar,
  year = 2008,
  pages = {31--61},
  volume = 16,
  doi = {10.1162/evco.2008.16.1.31},
  abstract = {The development of successful metaheuristic
                  algorithms such as local search for a difficult
                  problem such as satisfiability testing ({SAT)} is a
                  challenging task. We investigate an evolutionary
                  approach to automating the discovery of new local
                  search heuristics for {SAT.} We show that several
                  well-known {SAT} local search algorithms such as
                  Walksat and Novelty are composite heuristics that
                  are derived from novel combinations of a set of
                  building blocks. Based on this observation, we
                  developed {CLASS}, a genetic programming system that
                  uses a simple composition operator to automatically
                  discover {SAT} local search heuristics. New
                  heuristics discovered by {CLASS} are shown to be
                  competitive with the best Walksat variants,
                  including Novelty+. Evolutionary algorithms have
                  previously been applied to directly evolve a
                  solution for a particular {SAT} instance. We show
                  that the heuristics discovered by {CLASS} are also
                  competitive with these previous, direct evolutionary
                  approaches for {SAT.} We also analyze the local
                  search behavior of the learned heuristics using the
                  depth, mobility, and coverage metrics proposed by
                  Schuurmans and Southey.}
}
@book{FurLovLov2000stats,
  author = {Nancy E. Furlong and Eugene A. Lovelace and Kristin
                  L. Lovelace},
  title = {Research Methods and Statistics: An Integrated
                  Approach},
  publisher = {Harcourt College Publishers},
  year = 2000,
  alias = {furlong00:stats}
}
@article{Fursin2011milepost,
  author = {Grigori Fursin and Yuriy Kashnikov and Abdul Wahid Memon and Zbigniew Chamski and Olivier Temam and Mircea Namolaru and Elad Yom-Tov and Bilha Mendelson and Ayal Zaks and Eric Courtois and Francois Bodin and Phil Barnard and Elton Ashton and Edwin Bonilla and John Thomson and  Christopher K. I. Williams and Michael O'Boyle},
  title = {Milepost {GCC}: Machine Learning Enabled Self-tuning Compiler},
  journal = {International Journal of Parallel Programming},
  year = 2011,
  volume = 39,
  number = 3,
  pages = {296--327},
  publisher = {Springer, US},
  doi = {10.1007/s10766-010-0161-2}
}
@incollection{GaeCla04,
  isbn = {1-932415-66-1},
  year = 2005,
  publisher = {CSREA Press},
  booktitle = {Proceedings of the 2005 International Conference on Artificial Intelligence, ICAI 2005},
  editor = {Hamid R. Arabnia and Rose Joshua},
  author = {D. Gaertner and K. Clark},
  title = {On Optimal Parameters for Ant Colony Optimization
                  Algorithms},
  pages = {83--89}
}
@incollection{GagLeg2010emaa,
  editor = { Thomas Bartz-Beielstein  and  Marco Chiarandini  and  Lu{\'i}s Paquete  and  Mike Preuss },
  year = 2010,
  address = { Berlin, Germany },
  publisher = {Springer},
  booktitle = {Experimental Methods for the Analysis of
                  Optimization Algorithms},
  author = { Matteo Gagliolo  and Catherine Legrand},
  title = {Algorithm Survival Analysis},
  pages = {161--184},
  eprint = {http://como.vub.ac.be/~mgagliol/Gagliolo10Survival.pdf},
  doi = {10.1007/978-3-642-02538-9_7},
  abstract = {Algorithm selection is typically based on models of
                  algorithm performance,learned during a separate
                  offline training sequence, which can be
                  prohibitively expensive. In recent work, we adopted
                  an online approach, in which models of the runtime
                  distributions of the available algorithms are
                  iteratively updated and used to guide the allocation
                  of computational resources, while solving a sequence
                  of problem instances. The models are estimated using
                  survival analysis techniques, which allow us to
                  reduce computation time, censoring the runtimes of
                  the slower algorithms. Here, we review the
                  statistical aspects of our online selection method,
                  discussing the bias induced in the runtime
                  distributions (RTD) models by the competition of
                  different algorithms on the same problem instances.}
}
@article{GagPriGra02:jors,
  author = { Caroline Gagn{\'e}  and W. L. Price and M. Gravel},
  title = {Comparing an {ACO} algorithm with other heuristics
                  for the single machine scheduling problem with
                  sequence-dependent setup times},
  journal = {Journal of the Operational Research Society},
  year = 2002,
  volume = 53,
  pages = {895--906}
}
@article{GalHao1999heacol,
  title = {Hybrid evolutionary algorithms for graph coloring},
  author = {Galinier, Philippe and  Jin-Kao Hao },
  journal = {Journal of Combinatorial Optimization},
  volume = 3,
  number = 4,
  pages = {379--397},
  year = 1999,
  publisher = {Springer},
  doi = {10.1023/A:1009823419804}
}
@article{GamDor00:informs,
  author = { L. M. Gambardella  and  Marco Dorigo },
  title = {Ant {Colony} {System} Hybridized with a New Local
                  Search for the Sequential Ordering Problem},
  volume = 12,
  number = 3,
  pages = {237--255},
  journal = {INFORMS Journal on Computing},
  year = 2000,
  anote = {IJ.26}
}
@incollection{GamDor95:ml,
  publisher = {Morgan Kaufmann Publishers, Palo Alto, CA},
  editor = {A. Prieditis and S. Russell},
  booktitle = {Proceedings of the Twelfth International Conference
                  on Machine Learning (ML-95)},
  year = 1995,
  author = { L. M. Gambardella  and  Marco Dorigo },
  title = {Ant-{Q}: A Reinforcement Learning Approach to the
                  Traveling Salesman Problem},
  pages = {252--260}
}
@inproceedings{GamDor96:icec,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
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                  Routing Problems with Time Windows},
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  alias = {GamTaiAga99:newideas}
}
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  year = 2004,
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}
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}
@incollection{GanMorKat2003,
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  shorteditor = { Carlos M. Fonseca  and  others},
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}
@article{Gao2016,
  author = {Gao, Kaizhou and Zhang, Yicheng and Sadollah, Ali and Su,
                  Rong},
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}
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  address = {Piscataway, NJ},
  publisher = {IEEE Press},
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  key = {IEEE CEC},
  author = {Gao, Huiru and Nie, Haifeng and Li, Ke},
  title = {Visualisation of {Pareto} Front Approximation: A Short Survey
                  and Empirical Comparisons},
  pages = {1750--1757},
  doi = {10.1109/CEC.2019.8790298}
}
@article{GaoNieLi2019visarxiv,
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                  and Empirical Comparisons},
  author = {Gao, Huiru and Nie, Haifeng and Li, Ke},
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}
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                  optimization,Realistic traffic instances,SUMO microscopic
                  simulator of urban mobility,Traffic light scheduling},
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}
@incollection{GarDas2008,
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  publisher = {Springer},
  year = 2008,
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  booktitle = {Learning and Intelligent Optimization, Second International Conference, LION 2},
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}
@article{GarFer2015saferl,
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}
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  author = {Garc{\'i}a, Salvador and Fern{\'a}ndez, Alberto and Luengo,
                  Juli{\'a}n and  Francisco Herrera },
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}
@article{GarGloRodLozMar2014,
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}
@book{GarJoh1979,
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@article{GarOliAlb2013tec,
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@article{GarRodLoz2012soco,
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                  reconverging on a shared view of the computational
                  foundations of intelligence that promotes valuable
                  cross-disciplinary exchanges on questions, methods, and
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                  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,
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                  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
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                  that show the potential for interchange between computer
                  science, cognitive science, and neuroscience.},
  eprint = {https://science.sciencemag.org/content/349/6245/273.full.pdf},
  journal = {Science}
}
@incollection{GesHutKotMal2014lion,
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  author = {Daniel Geschwender and  Frank Hutter  and Kotthoff, Lars and Yuri Malitsky and  Holger H. Hoos  and  Kevin Leyton-Brown },
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}
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@incollection{Glo98,
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  address = { Heidelberg },
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  shorteditor = { Jin-Kao Hao  and others},
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}
@incollection{GloKoc1996:mic,
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@article{GloKocAli98,
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@book{GloLag97,
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  title = {Tabu Search},
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}
@incollection{GloLagMar2002:mh,
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  publisher = {Kluwer Academic Publishers, Norwell, MA},
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  author = { Fred Glover  and  Manuel Laguna  and Rafael Mart{\'i}},
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}
@article{GloLuHao2010diversif,
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  author = { Fred Glover  and L{\"u}, Zhipeng and  Jin-Kao Hao },
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  abstract = {To the best of our knowledge, this paper describes the first
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                  scheduling, analyzing a dynamic regional problem which is
                  currently under discussion at the Vienna hospital
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                  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.}
}
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  author = {Hajela, Prabhat and Lin, C-Y},
  journal = {Structural Optimization},
  volume = 4,
  number = 2,
  pages = {99--107},
  year = 1992,
  publisher = {Springer}
}
@article{HajSas1989,
  title = {Simulated annealing--to cool or not},
  author = { Bruce Hajek  and Galen Sasaki},
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  number = 5,
  pages = {443--447},
  year = 1989,
  publisher = {Elsevier}
}
@article{Hajek1988,
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  author = { Bruce Hajek },
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  year = 1988,
  publisher = {{INFORMS}}
}
@incollection{HalOliSud2019cutoff,
  aurl = {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 = {George T. Hall and  Oliveto, Pietro S.  and  Dirk Sudholt },
  title = {On the impact of the cutoff time on the performance of
                  algorithm configurators},
  pages = {907--915},
  doi = {10.1145/3321707.3321879},
  keywords = {theory, automatic configuration, capping}
}
@incollection{HalOliSud2020fast,
  volume = 12269,
  year = 2020,
  address = { Cham, Switzerland },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { Thomas B{\"a}ck  and  Mike Preuss  and Deutz, Andr{\'e} and Wang, Hao and  Carola Doerr  and  Emmerich, Michael T. M.  and  Heike Trautmann },
  booktitle = {Parallel Problem Solving from Nature - PPSN XVI},
  title = {Fast Perturbative Algorithm Configurators},
  author = {George T. Hall and  Oliveto, Pietro S.  and  Dirk Sudholt },
  doi = {10.1007/978-3-030-58112-1_2},
  pages = {19--32}
}
@incollection{HalOliSud2020gecco,
  aurl = {https://dl.acm.org/citation.cfm?id=3377930},
  location = {Canc{\'u}n, Mexico},
  isbn = {978-1-4503-7128-5},
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2020,
  editor = { Carlos A. {Coello Coello} },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2020},
  author = {George T. Hall and  Oliveto, Pietro S.  and  Dirk Sudholt },
  title = {Analysis of the performance of algorithm configurators for
                  search heuristics with global mutation operators},
  pages = {823--831},
  doi = {10.1145/3377930.3390218}
}
@article{HamRuh1994,
  author = {Hamacher, Horst W. and Ruhe, G\"{u}nter},
  title = {On spanning tree problems with multiple objectives},
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  year = 1994,
  volume = 52,
  number = 4,
  pages = {209--230}
}
@incollection{HamStu2017,
  address = { Heidelberg },
  publisher = {Springer},
  doi = {10.1007/978-3-319-55453-2},
  volume = 10197,
  series = {Lecture Notes in Computer Science},
  year = 2017,
  booktitle = {Proceedings of EvoCOP 2017 -- 17th European Conference on Evolutionary Computation in Combinatorial Optimization },
  editor = { Bin Hu  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  author = { Hayfa Hammami  and  Thomas St{\"u}tzle },
  title = {A Computational Study of Neighborhood Operators for Job-Shop
                  Scheduling Problems with Regular Objectives},
  pages = {1--17}
}
@incollection{Han1997mots,
  publisher = {Springer Verlag},
  editor = {J. Climaco},
  year = 1997,
  booktitle = {Proceedings of the 13th International Conference on
                  Multiple Criteria Decision Making (MCDM'97)},
  title = {Tabu search for multiobjective optimization: {MOTS}},
  author = { Michael Pilegaard Hansen },
  pages = {574--586},
  alias = {Han1997}
}
@techreport{HanAugFin2009bbob_setup,
  author = { Nikolaus Hansen  and  Anne Auger  and Finck, S. and Ros, R.},
  title = {Real-Parameter Black-Box Optimization Benchmarking 2009:
                  Experimental setup},
  institution = {INRIA, France},
  year = 2009,
  number = {RR-6828},
  supplement = {http://coco.gforge.inria.fr/bbob2012-downloads}
}
@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
}
@techreport{HanFinRosAug2009bbob,
  author = { Nikolaus Hansen  and Finck, S. and Ros, R. and  Anne Auger },
  title = {Real-Parameter Black-Box Optimization Benchmarking
                  2009: Noiseless Functions Definitions},
  institution = {INRIA, France},
  year = 2009,
  number = {RR-6829},
  note = {Updated February 2010},
  annote = {\url{http://coco.gforge.inria.fr/bbob2012-downloads}}
}
@techreport{HanJas1998,
  author = { Michael Pilegaard Hansen  and  Andrzej Jaszkiewicz },
  title = {Evaluating the quality of approximations to the
                  non-dominated set},
  institution = {Institute of Mathematical Modelling, Technical
                  University of Denmark},
  year = 1998,
  number = {IMM-REP-1998-7},
  address = {Lyngby, Denmark}
}
@article{HanJau90,
  author = { Pierre Hansen  and B. Jaumard},
  title = {Algorithms for the Maximum Satisfiability Problem},
  journal = {Computing},
  year = 1990,
  volume = 44,
  pages = {279--303}
}
@incollection{HanKno2008mpsn,
  publisher = {Springer},
  series = {Natural Computing Series},
  editor = { Joshua D. Knowles  and  David Corne  and  Kalyanmoy Deb  and Deva Raj Chair},
  year = 2008,
  booktitle = {Multiobjective Problem Solving from Nature},
  author = { Julia Handl  and  Joshua D. Knowles },
  title = {Modes of Problem Solving with Multiple Objectives:
                  Implications for Interpreting the {Pareto} Set and
                  for Decision Making},
  doi = {10.1007/978-3-540-72964-8_7},
  pages = {131--151}
}
@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
}
@incollection{HanMla02:mh,
  alias = {GloKoc02:handbook-mh},
  publisher = {Kluwer Academic Publishers, Norwell, MA},
  year = 2002,
  editor = { Fred Glover  and Gary A. Kochenberger},
  booktitle = {Handbook of Metaheuristics},
  author = { Pierre Hansen  and  Nenad Mladenovi{\'c} },
  title = {Variable Neighborhood Search},
  pages = {145--184}
}
@incollection{HanMlaBriPer2010:handbook,
  address = { New York, NY },
  publisher = {Springer},
  edition = {2nd},
  series = {International Series in Operations Research \& Management
                  Science},
  volume = 146,
  booktitle = {Handbook of Metaheuristics},
  year = 2010,
  editor = { Michel Gendreau  and  Jean-Yves Potvin },
  title = {Variable {Neighborhood} {Search}},
  author = { Pierre Hansen  and  Nenad Mladenovi{\'c}  and Jack Brimberg and Jos{\'e} A. Moreno P{\'e}rez},
  pages = {61--86}
}
@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
}
@phdthesis{Hansen1998PhD,
  author = { Michael Pilegaard Hansen },
  title = {Metaheuristics for multiple objective combinatorial
                  optimization},
  school = {Institute of Mathematical Modelling, Technical
                  University of Denmark},
  month = mar,
  year = 1998
}
@incollection{Hansen2006cma,
  title = {The {CMA} evolution strategy: a comparing review},
  author = { Nikolaus Hansen },
  booktitle = {Towards a new evolutionary computation},
  pages = {75--102},
  year = 2006,
  publisher = {Springer}
}
@incollection{Hansen2009bpopcma,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2009,
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference Companion, GECCO Companion 2009},
  editor = {Franz Rothlauf},
  author = { Nikolaus Hansen },
  title = {Benchmarking a {BI}-population {CMA-ES} on the {BBOB}-2009
                  function testbed},
  pages = {2389--2396},
  keywords = {bipop-cma-es}
}
@inproceedings{HaoCaiHua2006,
  publisher = {IEEE Press},
  year = 2006,
  booktitle = {Proceedings of the International Conference on
                  Machine Learning and Cybernetics},
  key = {ICMLC},
  author = {Zhifeng Hao and Ruichu Cai and Han Huang},
  title = {An Adaptive Parameter Control Strategy for {ACO}},
  pages = {203--206}
}
@incollection{HaoHuaQinCai2007,
  year = 2007,
  volume = 4490,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  editor = {Yong Shi and G. Dick van Albada and Jack Dongarra
                  and Peter M. A. Sloot},
  booktitle = {Computational Science -- ICCS 2007, 7th International Conference, Proceedings, Part IV},
  author = {Zhifeng Hao and Han Huang and Yong Qin and Ruichu
                  Cai},
  title = {An {ACO} Algorithm with Adaptive Volatility Rate of
                  Pheromone Trail},
  pages = {1167--1170}
}
@inproceedings{HaoPan1998,
  year = 1998,
  booktitle = {Fifth International Symposium on Artificial Intelligence and Mathematics,
                  {AIM} 1998, Fort Lauderdale, Florida, USA, January 4-6, 1998},
  editor = {Martin C. Golumbic and others},
  author = { Jin-Kao Hao  and Pannier, J{\^{e}}rome},
  title = {Simulated Annealing and Tabu Search for Constraint Solving},
  pages = {1--15}
}
@incollection{HarGin1995,
  publisher = {Morgan Kaufmann Publishers},
  editor = {Chris S. Mellish},
  year = 1995,
  booktitle = {Proceedings of the Fourteenth International Joint
                  Conference on Artificial Intelligence (IJCAI-95)},
  author = {William D. Harvey and Matthew L. Ginsberg},
  title = {Limited Discrepancy Search},
  pages = {607--615}
}
@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},
  journal = {Operations Research Letters},
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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},
  volume = 24,
  number = 4,
  pages = {609--635},
  year = 2016,
  doi = {10.1162/EVCO_a_00183 }
}
@article{Haraguchi2016:joh,
  author = {Kazuya Haraguchi},
  title = {Iterated Local Search with {Trellis}-Neighborhood for the Partial {Latin} Square Extension Problem},
  journal = {Journal of Heuristics},
  year = 2016,
  volume = 22,
  number = 5,
  pages = {727--757}
}
@article{HasRaj2004,
  author = { Sameer   Hasija  and  Chandrasekharan   Rajendran },
  title = {Scheduling in flowshops to minimize total tardiness of jobs},
  journal = {International Journal of Production Research},
  volume = 42,
  number = 11,
  pages = {2289--2301},
  year = 2004,
  doi = {10.1080/00207540310001657595}
}
@article{HasYagIba2008:do,
  author = {Hideki Hashimoto and  Mutsunori Yagiura  and  Toshihide Ibaraki },
  title = {An Iterated Local Search Algorithm for the Time-dependent Vehicle
               Routing Problem with Time Windows},
  journal = {Discrete Optimization},
  year = 2008,
  volume = 5,
  number = 2,
  pages = {434--456}
}
@incollection{HassGueSil2016deep,
  aurl = {http://www.aaai.org/Library/AAAI/aaai16contents.php},
  year = 2016,
  publisher = {{AAAI} Press},
  booktitle = {Proceedings of  the {AAAI} Conference on Artificial Intelligence},
  editor = {Dale Schuurmans and Michael P. Wellman},
  title = {Deep Reinforcement Learning with Double {Q}-Learning},
  author = {van Hasselt, Hado and Guez, Arthur and Silver, David}
}
@article{Haykin2004nn,
  title = {A comprehensive foundation},
  author = {Haykin, Simon},
  journal = {Neural Networks},
  volume = 2,
  pages = 41,
  year = 2004
}
@article{HazGunEre08customer,
  title = {Customer order scheduling problem: a comparative
                  metaheuristics study},
  number = 5,
  journal = {International Journal of Advanced Manufacturing Technology},
  author = {{\"O}nc{\"u} Hazir and Yavuz G{\"u}nalay and Erdal Erel},
  month = may,
  year = 2008,
  keywords = {{ACO,Customer} order {scheduling,Genetic}
                  {algorithms,Meta-heuristics,Simulated} {annealing,Tabu}
                  search},
  pages = {589--598},
  volume = 37,
  doi = {10.1007/s00170-007-0998-8},
  abstract = {The customer order scheduling problem {(COSP)} is defined as
                  to determine the sequence of tasks to satisfy the demand of
                  customers who order several types of products produced on a
                  single machine. A setup is required whenever a product type
                  is launched.  The objective of the scheduling problem is to
                  minimize the average customer order flow time. Since the
                  customer order scheduling problem is known to be strongly
                  {NP-hard,} we solve it using four major metaheuristics and
                  compare the performance of these heuristics, namely,
                  simulated annealing, genetic algorithms, tabu search, and ant
                  colony optimization. These are selected to represent various
                  characteristics of metaheuristics: nature-inspired
                  vs. artificially created, population-based vs. local search,
                  etc. A set of problems is generated to compare the solution
                  quality and computational efforts of these heuristics.
                  Results of the experimentation show that tabu search and ant
                  colony perform better for large problems whereas simulated
                  annealing performs best in small-size problems. Some
                  conclusions are also drawn on the interactions between
                  various problem parameters and the performance of the
                  heuristics.}
}
@article{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}
}
@inproceedings{HeiIge2009aicml,
  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},
  title = {Hoeffding and {Bernstein} races for selecting policies in
                  evolutionary direct policy search},
  author = { Heidrich-Meisner, Verena  and  Christian Igel },
  keywords = {automated algorithm configuration, CMA-ES, racing},
  pages = {401--408},
  doi = {10.1145/1553374.1553426}
}
@misc{Hel2018lkh,
  author = { Keld Helsgaun },
  title = {Source Code of the {Lin}-{Kernighan}-{Helsgaun} Traveling
                  Salesman Heuristic},
  howpublished = {\url{http://webhotel4.ruc.dk/~keld/research/LKH/}},
  year = 2018
}
@incollection{Hel2018ppsn,
  volume = {11101},
  year = 2018,
  address = { Cham, Switzerland },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { Anne Auger  and  Carlos M. Fonseca  and Louren{\c c}o, N. and Machado,
                  P. and  Lu{\'i}s Paquete  and  Darrell Whitley },
  booktitle = {Parallel Problem Solving from Nature - PPSN XV},
  author = { Keld Helsgaun },
  title = {Efficient Recombination in the {Lin}-{Kernighan}-{Helsgaun}
                  Traveling Salesman Heuristic},
  pages = {95--107},
  doi = {10.1007/978-3-319-99253-2_8}
}
@article{HelKar1970,
  author = {Held, Michael and Karp, Richard M.},
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  pages = {1138--1162},
  year = 1970
}
@article{HelRen1998mp,
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  year = 1998,
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}
@article{Helsgaun00,
  author = { Keld Helsgaun },
  title = {An Effective Implementation of the {Lin}-{Kernighan}
Traveling Salesman Heuristic},
  journal = {European Journal of Operational Research},
  year = 2000,
  volume = 126,
  pages = {106--130}
}
@article{Helsgaun09,
  author = { Keld Helsgaun },
  title = {General {\it k}-opt Submoves for the {Lin}-{Kernighan} {TSP}
               Heuristic},
  journal = {Mathematical Programming Computation},
  year = 2009,
  volume = 1,
  number = {2--3},
  pages = {119--163}
}
@book{Hen1999:mit,
  author = {van Hentenryck, Pascal },
  title = {The {OPL} optimization programming language},
  publisher = {MIT Press},
  year = 1999,
  address = {Cambridge, MA}
}
@incollection{HenJacJoh2003,
  doi = {10.1007/b101874},
  address = { Boston, MA},
  publisher = {Springer},
  year = 2003,
  editor = { Fred Glover  and Gary A. Kochenberger},
  booktitle = {Handbook of Metaheuristics},
  author = { Darrall Henderson  and  Sheldon H. Jacobson  and  Alan W. Johnson },
  title = {The Theory and Practice of Simulated Annealing},
  pages = {287--319}
}
@book{HenMich05:mit,
  author = {van Hentenryck, Pascal  and  Laurent D. Michel },
  title = {Constraint-based Local Search},
  publisher = {MIT Press},
  year = 2005,
  address = {Cambridge, MA}
}
@inproceedings{HenMich07synthesis,
  publisher = {{AAAI} Press\slash {MIT} Press, Menlo Park, CA },
  year = 2007,
  editor = {Robert C. Holte and Adele Howe},
  booktitle = {Proceedings of  the {AAAI} Conference on Artificial Intelligence},
  author = {van Hentenryck, Pascal  and  Laurent D. Michel },
  title = {Synthesis of constraint-based local search
                  algorithms from high-level models},
  pages = {273--278}
}
@article{Her2015toms,
  author = {Michael A. Heroux},
  title = {Editorial: {ACM} {TOMS} Replicated Computational Results
                  Initiative},
  doi = {10.1145/2743015},
  year = 2015,
  month = jun,
  publisher = {Association for Computing Machinery ({ACM})},
  volume = 41,
  number = 3,
  pages = {1--5},
  journal = {ACM Transactions on Mathematical Software}
}
@article{HerBlu2009:si,
  author = {H. Hern{\'a}ndez and  Christian Blum },
  title = {Ant colony optimization for multicasting in static
                  wireless ad-hoc networks},
  journal = {Swarm Intelligence},
  volume = 3,
  number = 2,
  pages = {125--148},
  year = 2009
}
@misc{HerLozMol2010test,
  author = { Francisco Herrera  and  Manuel Lozano  and  Daniel Molina },
  title = {Test suite for the special issue of {Soft}
                  {Computing} on scalability of evolutionary
                  algorithms and other metaheuristics for large scale
                  continuous optimization problems},
  year = 2010,
  howpublished = {\url{http://sci2s.ugr.es/eamhco/}},
  keywords = {SOCO benchmark}
}
@article{HerLozSan03:real-coded-crossover,
  author = { Francisco Herrera  and  Manuel Lozano  and  A. M. S{\'a}nchez },
  title = {A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study},
  journal = {International Journal of Intelligent Systems},
  year = 2003,
  volume = 18,
  number = 3,
  pages = {309--338},
  doi = {10.1002/int.10091}
}
@article{HerLozVer1998air,
  author = { Francisco Herrera  and  Manuel Lozano  and Verdegay, J. L.},
  title = {Tackling Real-Coded Genetic Algorithms: Operators
                  and Tools for Behavioural Analysis},
  journal = {Artificial Intelligence Review},
  year = 1998,
  volume = 12,
  pages = {265--319},
  keywords = {genetic algorithms, real coding, continuous search
                  spaces, mutation, recombination}
}
@article{Hermet2006ecj,
  author = { van Hemert, Jano I. },
  title = {Evolving Combinatorial Problem Instances That Are Difficult
                  to Solve},
  journal = {Evolutionary Computation},
  volume = 14,
  number = 4,
  pages = {433-462},
  year = 2006,
  doi = {10.1162/evco.2006.14.4.433},
  abstract = {This paper demonstrates how evolutionary computation can be
                  used to acquire difficult to solve combinatorial problem
                  instances. As a result of this technique, the corresponding
                  algorithms used to solve these instances are
                  stress-tested. The technique is applied in three important
                  domains of combinatorial optimisation, binary constraint
                  satisfaction, Boolean satisfiability, and the travelling
                  salesman problem. The problem instances acquired through this
                  technique are more difficult than the ones found in popular
                  benchmarks. In this paper, these evolved instances are
                  analysed with the aim to explain their difficulty in terms of
                  structural properties, thereby exposing the weaknesses of
                  corresponding algorithms. }
}
@article{HeuNieKru2020publish,
  author = {Robert Heum{\"u}ller and Sebastian Nielebock and Jacob
                  Kr{\"u}ger and Frank Ortmeier},
  title = {Publish or perish, but do not forget your software artifacts},
  doi = {10.1007/s10664-020-09851-6},
  year = 2020,
  volume = 25,
  number = 6,
  pages = {4585--4616},
  journal = {Empirical Software Engineering}
}
@book{Heyman2003,
  title = {Stochastic models in operations research: stochastic
                  optimization},
  author = {Heyman, Daniel P and Sobel, Matthew J},
  volume = 2,
  year = 2003,
  publisher = {Courier Corporation}
}
@article{Hic2006,
  author = {Christian Hicks},
  title = {A Genetic Algorithm tool for optimising cellular or
                  functional layouts in the capital goods industry},
  journal = {International Journal of Production Economics},
  volume = 104,
  number = 2,
  pages = {598--614},
  year = 2006,
  doi = {10.1016/j.ijpe.2005.03.010}
}
@article{HinSal2006autoencoder,
  title = {Reducing the dimensionality of data with neural networks},
  author = {Hinton, Geoffrey E. and Salakhutdinov, Ruslan R.},
  journal = {Science},
  volume = 313,
  number = 5786,
  pages = {504--507},
  year = 2006
}
@article{Hoeffding1963,
  title = {Probability inequalities for sums of bounded random
                  variables},
  author = {Hoeffding, Wassily},
  journal = {Journal of the American Statistical Association},
  volume = 58,
  number = 301,
  pages = {13--30},
  year = 1963,
  alias = {hoeffding1963probability}
}
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  title = {Claspfolio 2: Advances in Algorithm Selection for Answer Set
                  Programming},
  author = { Holger H. Hoos  and  Marius Thomas Lindauer  and Schaub, Torsten},
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}
@book{HooStu04:sls-elsevier,
  author = { Holger H. Hoos  and  Thomas St{\"u}tzle },
  title = {Stochastic Local Search: Foundations and Applications},
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  address = {Amsterdam, The Netherlands},
  year = 2004,
  anote = {superseed by~\cite{{HooStu05sls-mk}}}
}
@book{HooStu05sls-mk,
  author = { Holger H. Hoos  and  Thomas St{\"u}tzle },
  title = {Stochastic Local Search---Foundations and Applications},
  publisher = {Morgan Kaufmann Publishers},
  address = { San Francisco, CA},
  year = 2005,
  alias = {HooStu04:book}
}
@inproceedings{HooStu1998uai,
  author = { Holger H. Hoos  and  Thomas St{\"u}tzle },
  title = {Evaluating {Las} {Vegas} Algorithms --- Pitfalls and
                  Remedies},
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                  Artificial Intelligence},
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  publisher = {Morgan Kaufmann Publishers, San Francisco, CA},
  pages = {238--245}
}
@article{HooStu2014,
  author = { Holger H. Hoos  and  Thomas St{\"u}tzle },
  title = {On the Empirical Scaling of Run-time for Finding Optimal
                  Solutions to the Traveling Salesman Problem},
  journal = {European Journal of Operational Research},
  year = 2014,
  volume = 238,
  number = 1,
  pages = {87--94}
}
@article{HooStu2015:ol,
  author = { Holger H. Hoos  and  Thomas St{\"u}tzle },
  title = {On the Empirical Time Complexity of Finding Optimal Solutions
                  vs.\ Proving Optimality for {Euclidean} {TSP} Instances},
  journal = {Optimization Letters},
  year = 2015,
  volume = 9,
  number = 6,
  pages = {1247--1254}
}
@inproceedings{Hoos2011mic,
  author = { Holger H. Hoos },
  title = {Programming by Optimisation: Towards a new Paradigm
                  for Developing High-Performance Software},
  booktitle = {MIC 2011, the 9th Metaheuristics International
                  Conference},
  year = 2011,
  note = {{Plenary talk}},
  url = {http://mic2011.diegm.uniud.it/uploads/plenaries/Hoos-MIC2011.pdf}
}
@incollection{Hoos2012autsea,
  year = 2012,
  address = { Berlin, Germany },
  publisher = {Springer},
  booktitle = {Autonomous Search},
  editor = {Y. Hamadi and E. Monfroy and F. Saubion},
  author = { Holger H. Hoos },
  title = {Automated Algorithm Configuration and Parameter
                  Tuning},
  pages = {37--71},
  doi = {10.1007/978-3-642-21434-9_3}
}
@article{Hoos:PbO,
  author = { Holger H. Hoos },
  title = {Programming by optimization},
  journal = {Communications of the ACM},
  volume = 55,
  number = 2,
  month = feb,
  year = 2012,
  pages = {70--80},
  numpages = 11,
  doi = {10.1145/2076450.2076469}
}
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  author = {Horoba, Christian and Neumann, Frank},
  title = {Benefits and drawbacks for the use of epsilon-dominance in
                  evolutionary multi-objective optimization},
  pages = {641--648}
}
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}
@inproceedings{HosEec2008cole,
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  editor = {Soffa, Mary Lou and Duesterwald, Evelyn},
  author = {Kenneth Hoste and Lieven Eeckhout},
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  pages = {165--174},
  doi = {10.1145/1356058.1356080}
}
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}
@article{HuKahTsa95,
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@article{HuWanQiu2018nca,
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                  Yan, Liping},
  doi = {10.1007/s00521-016-2508-0},
  journal = {Neural Computing \& Applications},
  keywords = {BML,Optimization,Simulation,Traffic congestion,Updating
                  rules},
  title = {A quantum particle swarm optimization driven urban traffic
                  light scheduling model},
  year = 2018,
  alias = {Hu2018}
}
@article{HuYanWang2017,
  author = {Hu, Wenbin and Yan, Liping and Wang, Huan and Du, Bo and Tao,
                  Dacheng},
  journal = {Information Sciences},
  keywords = {BML model,Prediction,Real-time,Traffic jam,Urban traffic
                  network},
  title = {Real-time traffic jams prediction inspired by {Biham},
                  {Middleton} and {Levine} (BML) model},
  year = 2017,
  alias = {Hu2017}
}
@article{HuaAllNot2006global,
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  year = 2006,
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}
@incollection{HuaYaHaoCai2006,
  volume = 4115,
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  publisher = {Springer},
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  editor = {De-Shuang Huang and Kang Li and George W. Irwin},
  booktitle = {International Conference on Computational Science
                  (3)},
  author = {Han Huang and Xiaowei Yang and Zhifeng Hao and
                  Ruichu Cai},
  title = {A Novel {ACO} Algorithm with Adaptive Parameter},
  pages = {12--21}
}
@inproceedings{HuaYanTse04:ics,
  author = { K. Huang  and  C. Yang  and  K. Tseng },
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  year = 2004,
  publisher = {IEEE Press}
}
@article{HubHinBar2006tec,
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                  While, L.},
  year = 2006,
  pages = {477--506}
}
@article{HubLukHog1997,
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}
@article{HueRioRui2017,
  author = {D. L. Huerta-Mu{\~n}oz and R. Z. R{\'i}os-Mercado and  Rub{\'e}n Ruiz },
  title = {An Iterated Greedy Heuristic for a Market Segmentation Problem with Multiple Attributes},
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  year = 2017,
  volume = 261,
  number = 1,
  pages = {75--87}
}
@inproceedings{Hughes2003msops,
  year = 2003,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  month = dec,
  volume = 4,
  booktitle = {Proceedings of the 2003 Congress on Evolutionary
                  Computation (CEC 2003)},
  key = {IEEE CEC},
  title = {Multiple single objective {Pareto} sampling},
  author = { Hughes, Evan J. },
  pages = {2678--2684}
}
@inproceedings{Hughes2007msops,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  year = 2007,
  booktitle = {Proceedings of the 2007 Congress on Evolutionary
                  Computation (CEC 2007)},
  key = {IEEE CEC},
  title = {{MSOPS-II}: A general-purpose many-objective optimiser},
  author = { Hughes, Evan J. },
  pages = {3944--3951}
}
@incollection{Hughes2011models,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2011,
  editor = {Natalio Krasnogor and Pier Luca Lanzi},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2011},
  title = {Many-objective directed evolutionary line search},
  author = { Hughes, Evan J. },
  pages = {761--768}
}
@article{HumLieTal2013joh,
  author = { J{\'e}r{\'e}mie Humeau  and  Arnaud Liefooghe  and  Talbi, El-Ghazali  and  Verel, S{\'e}bastien },
  title = {{ParadisEO-MO}: From Fitness Landscape Analysis to
                  Efficient Local Search Algorithms},
  volume = 19,
  doi = {10.1007/s10732-013-9228-8},
  number = 6,
  journal = {Journal of Heuristics},
  month = jun,
  year = 2013,
  pages = {881--915}
}
@article{HunJosMel2009dace,
  title = {Design and Analysis of Computer Experiments With Branching
                  and Nested Factors},
  author = {Hung, Ying and Joseph, V. Roshan and Melkote, Shreyes N.},
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  year = 2009,
  number = 4,
  pages = {354--365},
  volume = 51,
  alias = {Hung2009},
  doi = {10.1198/TECH.2009.07097}
}
@inproceedings{HunLop2019turing,
  isbn = {978-1-5262-0820-0},
  organization = {Alan Turing Institute},
  month = nov # {, 21--22},
  year = 2019,
  date = {2019-11-21/2019-11-22},
  address = {London, UK},
  editor = {Iv{\'a}n Palomares},
  booktitle = {International Alan Turing Conference on Decision Support and
                  Recommender systems},
  author = {Maura Hunt and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Modeling a Decision-Maker in Goal Programming by means of
                  Computational Rationality},
  pages = {17--20},
  abstract = {This paper extends a simulation of cognitive mechanisms in
                  the context of multi-criteria decision-making by using ideas
                  from computational rationality. Specifically, this paper
                  improves the simulation of a human decision-maker (DM) by
                  considering how resource constraints impact their evaluation
                  process in an interactive Goal Programming problem. Our
                  analysis confirms and emphasizes a previous simulation study
                  by showing key areas that could be effected by cognitive
                  mechanisms. While the results are promising, the effects
                  should be validated by future experiments with human DMs.},
  aurl = {https://dsrs.blogs.bristol.ac.uk/files/2020/01/DSRS-Turing_19.pdf#page=24}
}
@article{HurMau2010,
  author = {M. Hurtgen and J.-C. Maun},
  title = {Optimal {PMU} placement using Iterated Local Search},
  journal = {International Journal of Electrical Power \& Energy Systems},
  year = 2010,
  volume = 32,
  number = 8,
  pages = {857--860}
}
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  author = {S. H. Hurlbert},
  title = {Pseudoreplication and the Design of Ecological Field
                  Experiments},
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  volume = 54,
  number = 2,
  pages = {187--211},
  year = 1984
}
@incollection{HusStu2009hm,
  address = { Heidelberg },
  publisher = {Springer},
  volume = 5818,
  series = {Lecture Notes in Computer Science},
  editor = { Mar{\'i}a J. Blesa  and  Christian Blum  and Luca {Di Gaspero} and  Andrea Roli  and  M. Sampels  and Andrea Schaerf},
  year = 2009,
  booktitle = {Hybrid Metaheuristics},
  author = {Mohamed Saifullah Hussin and  Thomas St{\"u}tzle },
  title = {Hierarchical Iterated Local Search for the Quadratic
                  Assignment Problem},
  pages = {115--129},
  doi = {10.1007/978-3-642-04918-7_9}
}
@article{HusStu2014cor,
  author = {Mohamed Saifullah Hussin and  Thomas St{\"u}tzle },
  title = {Tabu Search vs. Simulated Annealing for Solving
                  Large Quadratic Assignment Instances},
  journal = {Computers \& Operations Research},
  volume = 43,
  pages = {286--291},
  year = 2014
}
@inproceedings{HutBabHooHu2007fmcad,
  address = {Austin, Texas, USA},
  year = 2007,
  publisher = {IEEE Computer Society, Washington, DC, USA},
  booktitle = {FMCAD'07: Proceedings of the 7th International Conference
                  Formal Methods in Computer Aided Design},
  editor = {Jason Baumgartner and Mary Sheeran},
  author = { Frank Hutter  and  Domagoj Babi{\'c}  and  Holger H. Hoos  and Alan J. Hu},
  title = {Boosting Verification by Automatic Tuning of
                  Decision Procedures},
  pages = {27--34}
}
@incollection{HutHooLey2009gecco,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2009,
  editor = {Franz Rothlauf},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2009},
  author = { Frank Hutter  and  Holger H. Hoos  and  Kevin Leyton-Brown  and Kevin P. Murphy},
  title = {An experimental investigation of model-based
                  parameter optimisation: {SPO} and beyond},
  pages = {271--278},
  doi = {10.1145/1569901.1569940}
}
@incollection{HutHooLey2010:cpaior,
  address = { Heidelberg },
  publisher = {Springer},
  year = 2010,
  editor = {A. Lodi and M. Milano and P. Toth},
  volume = 6140,
  series = {Lecture Notes in Computer Science},
  booktitle = {Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, 7th International Conference, CPAIOR 2010},
  author = { Frank Hutter  and  Holger H. Hoos  and  Kevin Leyton-Brown },
  title = {Automated Configuration of Mixed Integer Programming Solvers},
  pages = {186--202},
  keyword = {mipconfig},
  alias = {HutHooLey10-mipconfig},
  doi = {10.1007/978-3-642-13520-0_23}
}
@article{HutHooLey2010amai,
  author = { Frank Hutter  and  Holger H. Hoos  and  Kevin Leyton-Brown },
  title = {Tradeoffs in the Empirical Evaluation of Competing Algorithm Designs},
  journal = {Annals of Mathematics and Artificial Intelligence},
  year = 2010,
  volume = 60,
  number = {1--2},
  pages = {65--89}
}
@incollection{HutHooLey2011lion,
  volume = 6683,
  editor = { Carlos A. {Coello Coello} },
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  year = 2011,
  booktitle = {Learning and Intelligent Optimization, 5th
                  International Conference, LION 5},
  author = { Frank Hutter  and  Holger H. Hoos  and  Kevin Leyton-Brown },
  title = {Sequential Model-Based Optimization for General
                  Algorithm Configuration},
  pages = {507--523},
  keywords = {SMAC,ROAR},
  doi = {10.1007/978-3-642-25566-3_40}
}
@incollection{HutHooLey2012lion,
  volume = 7219,
  editor = { Youssef Hamadi  and  Marc Schoenauer },
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  year = 2012,
  booktitle = {Learning and Intelligent Optimization, 6th
                  International Conference, LION 6},
  title = {Parallel Algorithm Configuration},
  author = { Frank Hutter  and  Holger H. Hoos  and  Kevin Leyton-Brown },
  pages = {55--70}
}
@article{HutHooLey2013arxiv,
  author = { Frank Hutter  and  Holger H. Hoos  and  Kevin Leyton-Brown },
  title = {Bayesian Optimization With Censored Response Data},
  journal = {Arxiv preprint arXiv:1310.1947},
  year = 2013,
  url = {http://arxiv.org/abs/1310.1947}
}
@incollection{HutHooLey2013lion,
  address = { Heidelberg },
  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 = { Frank Hutter  and  Holger H. Hoos  and  Kevin Leyton-Brown },
  title = {Identifying Key Algorithm Parameters and Instance Features
                  using Forward Selection},
  pages = {364--381},
  doi = {10.1007/978-3-642-44973-4_40},
  keywords = {parameter importance}
}
@inproceedings{HutHooLey2014icml,
  year = 2014,
  volume = 32,
  booktitle = {Proceedings of  the 31st International Conference on Machine Learning, {ICML} 2014},
  editor = {Xing, Eric P. and Jebara, Tony},
  author = { Frank Hutter  and  Holger H. Hoos  and  Kevin Leyton-Brown },
  title = {An Efficient Approach for Assessing Hyperparameter
                  Importance},
  pages = {754--762},
  url = {http://jmlr.org/proceedings/papers/v32/hutter14.html},
  keywords = {fANOVA, parameter importance}
}
@incollection{HutHooLeyMur2010lion,
  address = { Heidelberg },
  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},
  author = { Frank Hutter  and  Holger H. Hoos  and  Kevin Leyton-Brown  and Kevin Murphy},
  title = {Time-Bounded Sequential Parameter Optimization},
  pages = {281--298},
  doi = {10.1007/978-3-642-13800-3_30}
}
@article{HutHooLeyStu2009jair,
  author = { Frank Hutter  and  Holger H. Hoos  and  Kevin Leyton-Brown  and  Thomas St{\"u}tzle },
  title = {{\softwarepackage{ParamILS}:} An Automatic Algorithm Configuration Framework},
  journal = {Journal of Artificial Intelligence Research},
  year = 2009,
  volume = 36,
  pages = {267--306},
  month = oct,
  doi = {10.1613/jair.2861}
}
@inproceedings{HutHooStu07aaai,
  publisher = {{AAAI} Press\slash {MIT} Press, Menlo Park, CA },
  year = 2007,
  editor = {Robert C. Holte and Adele Howe},
  booktitle = {Proceedings of  the {AAAI} Conference on Artificial Intelligence},
  author = { Frank Hutter  and  Holger H. Hoos  and  Thomas St{\"u}tzle },
  title = {Automatic Algorithm Configuration Based on Local
                  Search},
  pages = {1152--1157},
  alias = {HutHooStu07}
}
@article{HutLinBal2017aij,
  author = { Frank Hutter  and  Marius Thomas Lindauer  and Adrian Balint and Sam Bayless and  Holger H. Hoos  and  Kevin Leyton-Brown },
  title = {The Configurable {SAT} Solver Challenge {(CSSC)}},
  journal = {Artificial Intelligence},
  year = 2017,
  volume = 243,
  pages = {1--25},
  alias = {HutLinBal++2017:aij},
  doi = {10.1016/j.artint.2016.09.006}
}
@incollection{HutLopFaw2014lion,
  address = { Heidelberg },
  publisher = {Springer},
  volume = 8426,
  editor = { Panos M. Pardalos  and  Mauricio G. C. Resende  and Chrysafis Vogiatzis and Jose
                  L. Walteros},
  series = {Lecture Notes in Computer Science},
  year = 2014,
  booktitle = {Learning and Intelligent Optimization, 8th International Conference, LION 8},
  author = { Frank Hutter  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Chris Fawcett  and  Marius Thomas Lindauer  and  Holger H. Hoos  and  Kevin Leyton-Brown  and  Thomas St{\"u}tzle },
  title = {{AClib}: A Benchmark Library for Algorithm Configuration},
  pages = {36--40},
  doi = {10.1007/978-3-319-09584-4_4}
}
@article{HutXuHooLey2014,
  title = {Algorithm runtime prediction: Methods \& evaluation},
  author = { Frank Hutter  and  Lin Xu  and  Holger H. Hoos  and  Kevin Leyton-Brown },
  journal = {Artificial Intelligence},
  volume = 206,
  pages = {79--111},
  year = 2014
}
@misc{Hutter2007satbench,
  author = { Frank Hutter },
  title = {{SAT} benchmarks used in automated algorithm configuration},
  howpublished = {\url{http://www.cs.ubc.ca/labs/beta/Projects/AAC/SAT-benchmarks.html}},
  year = 2007
}
@phdthesis{HutterPhD,
  author = { Frank Hutter },
  title = {Automated Configuration of Algorithms for Solving
                  Hard Computational Problems},
  school = {University of British Columbia, Department of
                  Computer Science},
  address = {Vancouver, Canada},
  year = 2009,
  month = oct
}
@misc{IJCAI2021checklist,
  author = {Zhiyuan Liu and Jian Tang},
  title = {IJCAI 2021 Reproducibility Guidelines, 35th International
                  Joint Conference on Artificial Intelligence},
  year = 2021,
  howpublished = {\url{https://ijcai-21.org/wp-content/uploads/2020/12/20201226-IJCAI-Reproducibility.pdf}}
}
@techreport{INRIA-RR-7871,
  author = { J{\'e}r{\'e}mie Humeau  and  Arnaud Liefooghe  and  Talbi, El-Ghazali  and  Verel, S{\'e}bastien },
  title = {{ParadisEO-MO}: From Fitness Landscape Analysis to
                  Efficient Local Search Algorithms},
  institution = {INRIA, France},
  year = 2012,
  type = {Rapport de recherche},
  number = {RR-7871},
  eprint = {http://hal.inria.fr/hal-00665421/PDF/RR-7871.pdf}
}
@techreport{IRIDIA-2003-037,
  author = { Mauro Birattari },
  title = {The {\rpackage{race}} Package for~\proglang{R}: {Racing}
                  Methods for the Selection of the Best},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2003,
  number = {TR/IRIDIA/2003-037}
}
@techreport{IRIDIA-2004-001,
  author = { Mauro Birattari },
  title = {On the Estimation of the Expected Performance of a Metaheuristic on a Class of Instances. How Many Instances, How Many Runs?},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2004,
  number = {TR/IRIDIA/2004-001}
}
@techreport{IRIDIA-2007-019,
  author = { Krzysztof Socha  and  Marco Dorigo },
  title = {Ant Colony Optimization for Mixed-Variable Optimization
                  Problems},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2007,
  number = {TR/IRIDIA/2007-019},
  month = oct,
  alias = {SocDor97:iridia019}
}
@techreport{IRIDIA-2009-015,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
  title = {Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2009,
  number = {TR/IRIDIA/2009-015},
  month = may,
  note = {Published as a book chapter~\cite{LopPaqStu09emaa}}
}
@techreport{IRIDIA-2009-019,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {An Analysis of Algorithmic Components for
                  Multiobjective Ant Colony Optimization: A Case Study
                  on the Biobjective {TSP}},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  number = {TR/IRIDIA/2009-019},
  year = 2009,
  month = jun,
  note = {Published in the proceedings of Evolution Artificielle, 2009~\cite{LopStu09ea}}
}
@techreport{IRIDIA-2009-020,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Effective Hybrid Stochastic Local Search Algorithms
                  for Biobjective Permutation Flowshop Scheduling},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  number = {TR/IRIDIA/2009-020},
  year = 2009,
  month = jun,
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2009-020r001.pdf},
  note = {Published in the proceedings of Hybrid Metaheuristics 2009~\cite{DubLopStu09:hm-bfsp}}
}
@techreport{IRIDIA-2009-026,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Adaptive ``Anytime'' Two-Phase Local Search},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2010,
  number = {TR/IRIDIA/2009-026},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2009-026r001.pdf},
  note = {Published in the proceedings of LION 4~\cite{DubLopStu10:lion-bfsp}}
}
@techreport{IRIDIA-2010-002,
  author = { Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Paola Pellegrini  and  Michael Maur  and  Marco A. {Montes de Oca}  and  Mauro Birattari  and  Marco Dorigo },
  title = {Parameter Adaptation in Ant Colony Optimization},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  number = {TR/IRIDIA/2010-002},
  year = 2010,
  month = jan,
  note = {Published as a book chapter~\cite{StuLopPel2011autsea}}
}
@techreport{IRIDIA-2010-019,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {A Hybrid {TP+PLS} Algorithm for Bi-objective
                  Flow-Shop Scheduling Problems},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2010,
  number = {TR/IRIDIA/2010-019},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2010-019r001.pdf},
  note = {Published in Computers \& Operations Research~\cite{DubLopStu2011cor}}
}
@techreport{IRIDIA-2010-020,
  author = {M. S. Hussin and  Thomas St{\"u}tzle },
  title = {Tabu Search vs. Simulated Annealing for Solving
                  Large Quadratic Assignment Instances},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2010,
  number = {TR/IRIDIA/2010-020}
}
@techreport{IRIDIA-2010-022,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Improving the Anytime Behavior of Two-Phase Local
                  Search},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2010,
  number = {TR/IRIDIA/2010-022},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2010-022r001.pdf},
  note = {Published in Annals of Mathematics and Artificial Intelligence~\cite{DubLopStu2011amai}}
}
@techreport{IRIDIA-2011-001,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Joshua D. Knowles  and  Marco Laumanns },
  title = {On Sequential Online Archiving of Objective Vectors},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2011,
  number = {TR/IRIDIA/2011-001},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2011-001.pdf},
  note = {This is a revised version of the paper published in EMO 2011~\cite{LopKnoLau2011emo}}
}
@techreport{IRIDIA-2011-002,
  author = { Mauro Birattari  and  Marco Chiarandini  and  Marco Saerens  and  Thomas St{\"u}tzle },
  year = 2011,
  title = {Learning graphical models for parameter tuning},
  number = {TR/IRIDIA/2011-002},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2011-002.pdf}
}
@techreport{IRIDIA-2011-003,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {The Automatic Design of Multi-Objective Ant Colony
                  Optimization Algorithms},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2011,
  number = {TR/IRIDIA/2011-003},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2011-003.pdf},
  note = {Published in IEEE Transactions on Evolutionary
                  Computation~\cite{LopStu2012tec}}
}
@techreport{IRIDIA-2011-010,
  author = {Liao, Tianjun  and  Daniel Molina  and  Marco A. {Montes de Oca}  and  Thomas St{\"u}tzle },
  title = {A Note on the Effects of Enforcing Bound Constraints on
                  Algorithm Comparisons using the {IEEE CEC'05} Benchmark
                  Function Suite},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2011,
  number = {TR/IRIDIA/2011-010},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2011-010.pdf},
  note = {Published in Evolutionary Computation~\cite{LiaMolMonStu2014}}
}
@techreport{IRIDIA-2011-022,
  author = {Liao, Tianjun  and  Daniel Molina  and  Marco A. {Montes de Oca}  and  Thomas St{\"u}tzle },
  title = {Computational Results for an Automatically Tuned {IPOP-CMA-ES} on the {CEC'05} Benchmark Set},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2011,
  number = {TR/IRIDIA/2011-022}
}
@techreport{IRIDIA-2012-012,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatically Improving the Anytime Behaviour of
                  Optimisation Algorithms},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2012,
  number = {TR/IRIDIA/2012-012},
  month = may,
  note = {Published in European Journal of Operational Research~\cite{LopStu2013ejor}}
}
@techreport{IRIDIA-2012-019,
  author = { Andreea Radulescu  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatically Improving the Anytime Behaviour of
                  Multiobjective Evolutionary Algorithms},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2012,
  number = {TR/IRIDIA/2012-019},
  note = {Published in the proceedings of EMO 2013~\cite{RadLopStu2013emo}}
}
@techreport{IRIDIA-2013-002,
  author = {Liao, Tianjun  and  Thomas St{\"u}tzle  and  Marco A. {Montes de Oca}  and  Marco Dorigo },
  title = {A Unified Ant Colony Optimization Algorithm for Continuous Optimization},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2013,
  number = {TR/IRIDIA/2013-002}
}
@techreport{IRIDIA-2013-015,
  author = { Franco Mascia  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  J{\'e}r{\'e}mie Dubois-Lacoste  and  Thomas St{\"u}tzle },
  year = 2013,
  title = {Grammar-based generation of stochastic local search
                  heuristics through automatic algorithm configuration
                  tools},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  number = {TR/IRIDIA/2013-015}
}
@techreport{IRIDIA-2014-009,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Arnaud Liefooghe  and  Verel, S{\'e}bastien },
  title = {Local Optimal Sets and Bounded Archiving on
                  Multi-objective {NK}-Landscapes with Correlated
                  Objectives},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2014,
  number = {TR/IRIDIA/2014-009}
}
@techreport{IRIDIA-2014-014,
  author = { Vito Trianni  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Advantages of Multi-Objective Optimisation in Evolutionary
                  Robotics: Survey and Case Studies},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2014,
  number = {TR/IRIDIA/2014-014},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2014-014.pdf}
}
@techreport{IRIDIA-2017-005,
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {A Large-Scale Experimental Evaluation of High-Performing
                  Multi- and Many-Objective Evolutionary Algorithms},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2017,
  number = {TR/IRIDIA/2017-005},
  month = nov
}
@techreport{IRIDIA-2017-006,
  author = { Alberto Franzin  and   P{\'e}rez C{\'a}ceres, Leslie and  Thomas St{\"u}tzle },
  title = {Effect of Transformations of Numerical Parameters
                 in Automatic Algorithm Configuration},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  number = {TR/IRIDIA/2017-006},
  year = 2017,
  month = mar,
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2017-006.pdf}
}
@techreport{IRIDIA-2017-011,
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Configuration of Multi-objective Optimizers and
                  Multi-objective Configuration},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2017,
  number = {TR/IRIDIA/2017-011},
  month = nov,
  alias = {BezLopStu2017:techreport-011},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2017-011.pdf},
  note = {Published as \cite{BezLopStu2020chapter}}
}
@techreport{IRIDIA-2017-012,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Marie-El{\'e}onore Kessaci  and  Thomas St{\"u}tzle },
  title = {Automatic Design of Hybrid Metaheuristics from Algorithmic
                  Components},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2017,
  number = {TR/IRIDIA/2017-012},
  month = dec,
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2017-012.pdf}
}
@techreport{IRIDIA-2018-001,
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatically Designing State-of-the-Art Multi- and
                  Many-Objective Evolutionary Algorithms},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2018,
  number = {TR/IRIDIA/2018-001},
  month = jan,
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2018-001.pdf},
  note = {Published as \cite{BezLopStu2019ec}}
}
@techreport{IRIDIA-2018-010,
  author = { Alberto Franzin  and  Thomas St{\"u}tzle },
  title = {Revisiting Simulated Annealing: a Component-Based Analysis},
  year = 2018,
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  number = {TR/IRIDIA/2018-010},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2018-010.pdf}
}
@techreport{IRIDIA-2021-002,
  author = {Camacho-Villal\'{o}n, Christian Leonardo and  Thomas St{\"u}tzle  and  Marco Dorigo },
  title = {{PSO-X}: A Component-Based Framework for the Automatic Design
                  of Particle Swarm Optimization Algorithms},
  year = 2021,
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  number = {TR/IRIDIA/2021-002},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2021-002.pdf}
}
@techreport{IRIDIA-2021-005,
  author = { Alberto Franzin  and  Thomas St{\"u}tzle },
  title = {A Landscape-based Analysis of Fixed Temperature and Simulated Annealing},
  year = 2021,
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  number = {TR/IRIDIA/2021-005},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2021-005.pdf}
}
@techreport{IRIDIA-2021-006,
  author = {Camacho-Villal\'{o}n, Christian Leonardo and  Thomas St{\"u}tzle  and  Marco Dorigo },
  title = {Cuckoo Search {$\equiv (\mu + \lambda$)}-Evolution Strategy -- 
                  A Rigorous Analysis of an Algorithm That Has Been Misleading
                  the Research Community for More Than 10 Years and Nobody Seems to Have Noticed},
  year = 2021,
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  number = {TR/IRIDIA/2021-006},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2021-006.pdf}
}
@article{IacPal2012:si,
  author = {Claudio Iacopino and Phil Palmer},
  title = {The Dynamics of Ant Colony Optimization Algorithms Applied to Binary Chains},
  journal = {Swarm Intelligence},
  year = 2012,
  volume = 6,
  number = 4,
  pages = {343--377}
}
@article{IacPalPol2014ants,
  journal = {Acta Futura},
  title = {How Ants Can Manage Your Satellites},
  doi = {10.2420/AF09.2014.59},
  volume = 9,
  author = {Iacopino, Claudio and Palmer, Phil and Policella, N. and
                  Donati, A. and Brewer, A.},
  pages = {59--72},
  year = 2014,
  keywords = {ACO, Space}
}
@article{IbaImaNon++2008:dam,
  author = {Toshihide Ibaraki and Shinji Imahori and Koji Nonobe and Kensuke Sobue and Takeaki Uno and  Mutsunori Yagiura },
  title = {An Iterated Local Search Algorithm for the Vehicle Routing Problem
               with Convex Time Penalty Functions},
  journal = {Discrete Applied Mathematics},
  year = 2008,
  volume = 156,
  number = 11,
  pages = {2050--2069}
}
@article{Ibaraki2010:itor,
  author = {Toshihide Ibaraki},
  title = {A Personal Perspective on Problem Solving by General Purpose Solvers},
  journal = {International Transactions in Operational Research},
  year = 2010,
  volume = 17,
  number = 3,
  pages = {303--315}
}
@article{IdeSch2016,
  author = {Ide, Jonas and  Sch{\"o}bel, Anita },
  title = {Robustness for uncertain multi-objective optimization: a
                  survey and analysis of different concepts},
  journal = {OR Spectrum},
  year = 2016,
  volume = 38,
  number = 1,
  pages = {235--271},
  abstract = {In this paper, we discuss various concepts of robustness for
                  uncertain multi-objective optimization problems. We extend
                  the concepts of flimsily, highly, and lightly robust
                  efficiency and we collect different versions of minmax robust
                  efficiency and concepts based on set order relations from the
                  literature. Altogether, we compare and analyze ten different
                  concepts and point out their relations to each
                  other. Furthermore, we present reduction results for the
                  class of objective-wise uncertain multi-objective
                  optimization problems.},
  doi = {10.1007/s00291-015-0418-7}
}
@article{IgeHanRot2007ec,
  author = { Christian Igel  and  Nikolaus Hansen  and S. Roth},
  title = {Covariance Matrix Adaptation for Multi-objective
                  Optimization},
  journal = {Evolutionary Computation},
  year = 2007,
  volume = 15,
  pages = {1--28},
  number = 1
}
@article{IgeHeiGla2008jmlr,
  author = { Christian Igel  and V. Heidrich-Meisner and  T. Glasmachers },
  title = {Shark},
  journal = {Journal of Machine Learning Research},
  year = 2008,
  volume = 9,
  month = jun,
  pages = {993--996},
  url = {http://www.jmlr.org/papers/volume9/igel08a/igel08a.pdf},
  alias = {Igel:2008ut}
}
@incollection{IkeKitShi2001cec,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  year = 2001,
  booktitle = {Proceedings of  the 2001 Congress on Evolutionary Computation (CEC'01)},
  key = {IEEE CEC},
  author = {Ikeda, Kokolo and Hajime Kita and Shigenobu Kobayashi},
  title = {Failure of {Pareto}-based {MOEA}s: {Does} non-dominated really
                  mean near to optimal?},
  pages = {957--962},
  keyword = {dominance resistance}
}
@article{Ilich98,
  author = { Nesa Ilich  and  Slobodan P. Simonovic },
  title = {Evolutionary Algorithm for minimization of pumping
                  cost},
  journal = {Journal of Computing in Civil Engineering, {ASCE}},
  year = 1998,
  volume = 12,
  number = 4,
  pages = {232--240},
  month = oct,
  abstract = {This paper deals with minimizing the total cost of
                  pumping in a liquid pipeline. Previous experience
                  with the most common solution procedures in pipeline
                  optimization is discussed along with their strengths
                  and weaknesses. The proposed method is an
                  evolutionary algorithm with two distinct features:
                  (1) The search is restricted to feasible region
                  only; and (2) it utilizes a floating point decision
                  variable rather than integer or binary as is the
                  case with most other similar approaches. A numerical
                  example is presented as a basis for verification of
                  the proposed method and its comparison with the
                  existing solver that utilizes the nonlinear
                  Newtonian search. The proposed method provides
                  promising improvements in terms of optimality when
                  compared to the widespread gradient search methods
                  because it does not involve evaluation of the
                  gradient of the objective function. It also provides
                  potential to improve the performance of previous
                  evolutionary programs because it restricts the
                  search to the feasible region, thus eliminating
                  large overhead associated with generation and
                  inspection of solutions that are
                  infeasible. Comparison of the two solutions revealed
                  improvement of the solution in favor of the proposed
                  algorithm, which ranged up to 6\% depending on the
                  initial values of the decision variables in the
                  Newtonian search. The proposed method was not
                  sensitive to the starting value of the decision
                  variables.}
}
@book{IllPenSto2008,
  title = {Statistical Analysis and Modelling of Spatial Point Patterns},
  author = {Illian, Janine and Penttinen, Antti and Stoyan, Helga and
                  Stoyan, Dietrich},
  publisher = {Wiley},
  year = 2008,
  alias = {Illian2008}
}
@article{ImaYagNag2009:ejor,
  author = {Takashi Imamichi and  Mutsunori Yagiura  and Hiroshi Nagamochi},
  title = {An Iterated Local Search Algorithm Based on Nonlinear Programming
               for the Irregular Strip Packing Problem},
  journal = {Discrete Optimization},
  year = 2009,
  volume = 6,
  number = 4,
  pages = {345--361}
}
@article{Inselberg1985pc,
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  year = 1985
}
@article{Ioa2005why,
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}
@incollection{IreMerMid2001,
  year = 2001,
  series = {Lecture Notes in Computer Science},
  volume = 1993,
  address = { Heidelberg },
  publisher = {Springer},
  editor = { Eckart Zitzler  and  Kalyanmoy Deb  and  Lothar Thiele  and  Carlos A. {Coello Coello}  and  David Corne },
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2001},
  author = { S. Iredi  and  D. Merkle  and  Martin Middendorf },
  title = {Bi-Criterion Optimization with Multi Colony Ant
                  Algorithms},
  pages = {359--372},
  alias = {IrediMid01,IreMerMid01},
  keyword = {BicriterionAnt}
}
@misc{IridiaSupp2012-011,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {{Automatically Improving the Anytime Behaviour of Optimisation Algorithms: Supplementary material}},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2012-011/}},
  year = 2012
}
@article{Irnich2008,
  author = {Stefan Irnich},
  title = {A Unified Modeling and Solution Framework for Vehicle Routing and
               Local Search-Based Metaheuristics},
  journal = {INFORMS Journal on Computing},
  year = 2008,
  volume = 20,
  number = 2,
  pages = {270--287}
}
@article{IruCalLoz2016mallows,
  author = { Irurozki, Ekhine  and Borja Calvo and  Jos{\'e} A. Lozano },
  title = {Sampling and Learning {Mallows} and Generalized {Mallows}
                  Models Under the {Cayley} Distance},
  doi = {10.1007/s11009-016-9506-7},
  year = 2016,
  month = jun,
  volume = 20,
  number = 1,
  pages = {1--35},
  journal = {Methodology and Computing in Applied Probability}
}
@article{IruCalLoz2016permallows,
  title = {{\rpackage{PerMallows}}: An {\proglang{R}} Package for Mallows
                  and Generalized Mallows Models},
  author = { Irurozki, Ekhine  and Calvo, Borja and  Jos{\'e} A. Lozano },
  abstract = {In this paper we present the R package PerMallows, which is a
                  complete toolbox to work with permutations, distances and
                  some of the most popular probability models for permutations:
                  Mallows and the Generalized Mallows models. The Mallows model
                  is an exponential location model, considered as analogous to
                  the Gaussian distribution. It is based on the definition of a
                  distance between permutations. The Generalized Mallows model
                  is its best-known extension. The package includes functions
                  for making inference, sampling and learning such
                  distributions. The distances considered in PerMallows are
                  Kendall's $\tau$, Cayley, Hamming and Ulam.},
  doi = {10.18637/jss.v071.i12},
  issn = 15487660,
  journal = {Journal of Statistical Software},
  keywords = {Cayley,Generalized Mallows,Hamming,Kendall's
                  $\tau$,Learning,Mallows,Permutation,R,Ranking,Sampling,Ulam},
  volume = 71,
  year = 2019
}
@article{IruLobPer2020arxiv,
  title = {Rank aggregation for non-stationary data streams},
  author = { Irurozki, Ekhine  and Lobo, Jesus and Perez, Aritz and Del Ser,
                  Javier},
  keywords = {uborda},
  journal = {Arxiv preprint arXiv:1910.08795 [stat.ML]},
  year = 2020,
  url = {https://arxiv.org/abs/1910.08795}
}
@incollection{IruLop2021gecco,
  location = {Lille, France},
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2021,
  editor = { Chicano, Francisco  and  Krzysztof Krawiec },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2021},
  author = { Irurozki, Ekhine  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Unbalanced Mallows Models for Optimizing Expensive Black-Box
                  Permutation Problems},
  doi = {10.1145/3449639.3459366},
  supplement = {https://zenodo.org/record/4500974},
  abstract = {Expensive black-box combinatorial optimization problems arise
                  in practice when the objective function is evaluated by means
                  of a simulator or a real-world experiment. Since each fitness
                  evaluation is expensive in terms of time or resources, only a
                  limited number of evaluations is possible, typically several
                  orders of magnitude smaller than in non-expensive
                  problems. In this scenario, classical optimization methods
                  such as mixed-integer programming and local search are not
                  useful.  In the continuous case, Bayesian optimization, in
                  particular using Gaussian processes, has proven very
                  effective under these conditions. Much less research is
                  available in the combinatorial case. In this paper, we
                  propose and analyze UMM, an estimation-of-distribution (EDA)
                  algorithm based on a Mallows probabilistic model and
                  unbalanced rank aggregation (uBorda). Experimental results on
                  black-box versions of LOP and PFSP show that UMM is able to
                  match, and sometimes surpass, the solutions obtained by CEGO,
                  a Bayesian optimization algorithm for combinatorial
                  optimization. Moreover, the computational complexity of UMM
                  increases linearly with both the number of function
                  evaluations and the permutation size.}
}
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  address = { New York, NY },
  publisher = {ACM Press},
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  pages = {695--702}
}
@incollection{IshMasTanNoj2015igd,
  editor = { Ant{\'o}nio Gaspar{-}Cunha  and Carlos Henggeler Antunes and  Carlos A. {Coello Coello} },
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                  Nojima, Yusuke},
  title = {Modified Distance Calculation in Generational Distance and
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  pages = {110--125},
  annote = {Proposed IGD+},
  keywords = {Performance metrics, multi-objective, IGD, IGD+}
}
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  address = { Heidelberg },
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  author = {Sophie Jacquin and  Laetitia Jourdan  and  Talbi, El-Ghazali },
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  keywords = {irace}
}
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  author = { Andrzej Jaszkiewicz  and  Ishibuchi, Hisao  and  Zhang, Qingfu },
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}
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  address = { New York, NY },
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  year = 2020,
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  author = {Jesus, Alexandre D. and  Arnaud Liefooghe  and  Bilel Derbel  and  Lu{\'i}s Paquete },
  pages = {850---858},
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}
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  year = 2014,
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}
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}
@incollection{Johnson2002,
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}
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  editor = {Coelho, H. and Studer, R. and Wooldridge, M.},
  title = {{ISAC}: Instance-Specific Algorithm Configuration},
  author = {Kadioglu, Serdar and Malitsky, Yuri and  Meinolf Sellmann  and  Kevin Tierney },
  pages = {751--756}
}
@article{KahTve1979prospect,
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@article{Kahneman2003maps,
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}
@inproceedings{KajIkeHaj2009cec,
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  booktitle = {Proceedings of  the 2009 Congress on Evolutionary Computation (CEC 2009)},
  key = {IEEE CEC},
  author = {H. Kaji and Ikeda, Kokolo and Hajime Kita},
  title = {Avoidance of constraint violation for experiment-based
                  evolutionary multi-objective optimization},
  pages = {2756--2763},
  keywords = {evolutionary computation;constraint
                  violation;experiment-based evolutionary multiobjective
                  optimization;evolutionary algorithm;risky-constraint
                  violation;Constraint optimization;Diesel
                  engines;Calibration;Evolutionary computation;Electric
                  breakdown;Optimization methods;Uncertainty;Computational
                  fluid dynamics;Cost function;Temperature},
  doi = {10.1109/CEC.2009.4983288}
}
@article{KanHeWei2013,
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}
@article{Kar2016,
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  title = {A hybrid iterated greedy algorithm for total tardiness minimization in permutation flowshops},
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  pages = {300 -- 307},
  year = 2016
}
@article{KarAka2009:airev,
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  volume = 31,
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}
@incollection{KarEibHoo2014generic,
  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 = {Generic parameter control with reinforcement learning},
  author = {Karafotias, Giorgos and  Agoston E. Eiben  and Mark Hoogendoorn},
  pages = {1319--1326}
}
@article{KarHooEib2015:tec,
  author = {Giorgos Karafotias and Mark Hoogendoorn and  Agoston E. Eiben },
  title = {Parameter Control in Evolutionary Algorithms: Trends and Challenges},
  journal = {IEEE Transactions on Evolutionary Computation},
  year = 2015,
  volume = 19,
  number = 2,
  pages = {167--187}
}
@incollection{KarHooEib2015eval,
  year = 2015,
  volume = 9028,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  booktitle = {Applications of Evolutionary Computation},
  editor = {Antonio M. Mora and Squillero, Giovanni},
  title = {Evaluating reward definitions for parameter control},
  author = {Karafotias, Giorgos and Hoogendoorn, Mark and  Agoston E. Eiben },
  pages = {667--680},
  doi = {10.1007/978-3-319-16549-3_54}
}
@article{KarKok2010tdea,
  title = {A territory defining multiobjective evolutionary algorithms
                  and preference incorporation},
  author = { Karahan, {\.I}brahim  and  Murat K{\"o}ksalan },
  journal = {IEEE Transactions on Evolutionary Computation},
  volume = 14,
  number = 4,
  pages = {636--664},
  year = 2010,
  keywords = {TDEA},
  doi = {10.1109/TEVC.2009.2033586}
}
@inproceedings{KarKorSom2013,
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  year = 2013,
  volume = 28,
  booktitle = {Proceedings of  the 30th International Conference on Machine Learning, {ICML} 2013},
  editor = {Dasgupta, Sanjoy and McAllester, David},
  title = {Almost optimal exploration in multi-armed bandits},
  author = {Karnin, Zohar and Koren, Tomer and Somekh, Oren},
  pages = {1238--1246}
}
@incollection{KarParStu2018lion,
  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},
  title = {Algorithm Configuration: Learning policies for the quick
                  termination of poor performers},
  author = {Karapetyan, Daniel and  Andrew J. Parkes  and  Thomas St{\"u}tzle },
  pages = {220--224},
  doi = {10.1007/978-3-030-05348-2_20}
}
@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},
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  number = 1,
  pages = {147--156},
  year = 2005
}
@incollection{KarSmiEib2012generic,
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  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
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  editor = {Di Chio, Cecillia and others},
  title = {A generic approach to parameter control},
  author = {Karafotias, Giorgos and  Smit, Selmar K.  and  Agoston E. Eiben },
  pages = {366--375},
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}
@article{KarTas2014,
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  pages = {383--395}
}
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}
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  year = 1972,
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                  held March 20-22, 1972, at the {IBM} {T}homas {J}. {W}atson Research Center,
                  Yorktown Heights, New York, USA},
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  pages = {85--103}
}
@article{KasNatRee2017ems,
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                  environmental systems undergoing change},
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                  Robert J.},
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  year = 2013,
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}
@article{KasReeCha2012ems,
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  author = { Kasprzyk, Joseph R.  and  Patrick M. Reed  and Characklis, Gregory W. and
                  Kirsch, Brian R.},
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  volume = 34,
  pages = {87--104},
  year = 2012,
  keywords = {scenario-based}
}
@incollection{KatNar1999:gecco,
  publisher = {Morgan Kaufmann Publishers, San Francisco, CA},
  year = 1999,
  booktitle = {Proceedings of the Genetic and Evolutionary
                  Computation Conference, GECCO 1999},
  shorteditor = {Wolfgang Banzhaf and others},
  editor = {Wolfgang Banzhaf and Jason M. Daida and A. E. Eiben
                  and Max H. Garzon and Vasant Honavar and Mark
                  J. Jakiela and Robert E. Smith},
  author = {K. Katayama and H. Narihisa},
  title = {Iterated Local Search Approach using Genetic Transformation to the
  Traveling Salesman Problem},
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  pages = {321--328}
}
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  author = { Kauffman, S. A. },
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@article{KazCohJea2020,
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                  the Complexity of Local Search},
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}
@inproceedings{Kazantzis02,
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  title = {A new methodology for optimizing the daily
                  operations of a pumping plant},
  year = 2002,
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                  Planning},
  address = {Roanoke, USA},
  month = may,
  organization = {ASCE}
}
@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}
}
@inproceedings{KeFenXuShaWan10,
  author = {Liangjun Ke and Zuren Feng and Zongben Xu and Ke Shang and
                  Yonggang Wang},
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                  Pacific-Asia Conference on},
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}
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  year = 2001,
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                  Evolutionary Computation, GECCO 2001},
  title = {An adaptive genetic algorithm},
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  pages = {391--397}
}
@book{KelPfePis04,
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  year = 2004
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@inproceedings{KelPol2007cec,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  year = 2007,
  booktitle = {Proceedings of the 2007 Congress on Evolutionary
                  Computation (CEC 2007)},
  key = {IEEE CEC},
  author = {Robert E. Keller and Riccardo Poli},
  title = {Linear genetic programming of parsimonious metaheuristics},
  pages = {4508--4515},
  doi = {10.1109/CEC.2007.4425062}
}
@inproceedings{KelPol2008ae,
  address = { Heidelberg },
  publisher = {Springer},
  editor = {Lutton, Evelyne and Legrand, Pierrick and Parrend, Pierre and  Nicolas Monmarch{\'e}  and  Marc Schoenauer },
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  title = {Cost-Benefit Investigation of a Genetic-Programming
                  Hyperheuristic},
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}
@article{KenBaiBla2016good,
  author = { Graham Kendall  and Ruibin Bai and Jacek B{\l}azewicz and Patrick {De Causmaecker} and  Michel Gendreau  and Robert John and Jiawei Li and  Barry McCollum  and Erwin Pesch and  Rong Qu  and Nasser Sabar and  Greet Vanden Berghe  and Angelina Yee},
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}
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}
@inproceedings{KenEbe1997binpso,
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                  Systems, Man, and Cybernetics},
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  publisher = {IEEE Press},
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}
@book{KenEbeShi01,
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  year = 2001
}
@book{Kendall1948,
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}
@article{KerHooNeuTra2019,
  author = { Pascal Kerschke  and  Holger H. Hoos  and  Frank Neumann  and  Heike Trautmann },
  title = {Automated Algorithm Selection: Survey and Perspectives},
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  volume = 27,
  number = 1,
  pages = {3--45},
  year = 2019,
  doi = {10.1162/evco_a_00242},
  month = mar
}
@article{KerLin70,
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}
@inproceedings{KerTra2016cec,
  year = 2016,
  isbn = {978-1-5090-0623-6},
  aurl = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7636124},
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  booktitle = {Proceedings of  the 2016 Congress on Evolutionary Computation (CEC 2016)},
  key = {IEEE CEC},
  author = { Pascal Kerschke  and  Heike Trautmann },
  title = {The \proglang{R}-package {FLACCO} for exploratory landscape
                  analysis with applications to multi-objective optimization
                  problems},
  pages = {5262--5269},
  doi = {10.1109/CEC.2016.7748359}
}
@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. }
}
@incollection{KerWanPre2016ppsn,
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  year = 2016,
  volume = 9921,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { Julia Handl  and  Emma Hart  and  Lewis, P. R.  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Gabriela Ochoa  and  Ben Paechter },
  booktitle = {Parallel Problem Solving from Nature -- PPSN XIV},
  author = { Pascal Kerschke  and Hao Wang and  Mike Preuss  and Christian
                  Grimme and   Andr{\'{e}} H. Deutz  and  Heike Trautmann  and  Emmerich, Michael T. M. },
  title = {Towards Analyzing Multimodality of Continuous Multiobjective
                  Landscapes},
  pages = {962--972},
  doi = {10.1007/978-3-319-45823-6_90}
}
@article{KerWanPreuGrim2019search,
  doi = {10.1162/evco_a_00234},
  year = 2019,
  publisher = {MIT Press},
  volume = 27,
  number = 4,
  pages = {577--609},
  author = { Pascal Kerschke  and Hao Wang 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}
}
@misc{Keras,
  author = {Keras development team},
  title = {Keras},
  year = 2017,
  howpublished = {\url{https://https://keras.io}}
}
@article{Kerr1998harking,
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  year = 1998,
  month = aug,
  publisher = {{SAGE} Publications},
  volume = 2,
  number = 3,
  pages = {196--217},
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  journal = {Personality and Social Psychology Review}
}
@incollection{Kerrisk05:POSIX-threads,
  author = {M. Kerrisk},
  booktitle = {Linux Programmer's Manual},
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  year = 2005,
  type = {{Section}},
  note = {(Last accessed May 15 2008)},
  chapter = 7,
  publisher = {\url{http://www.linux-man-pages.org/man7/pthreads/}}
}
@incollection{KhaYaoDeb2003,
  address = { Heidelberg },
  publisher = {Springer},
  volume = 2632,
  series = {Lecture Notes in Computer Science},
  shorteditor = { Carlos M. Fonseca  and  others},
  editor = { Carlos M. Fonseca  and  Peter J. Fleming  and  Eckart Zitzler  and  Kalyanmoy Deb  and  Lothar Thiele },
  year = 2003,
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2003},
  author = {Khare, V. and Yao, X. and  Kalyanmoy Deb },
  title = {Performance Scaling of Multi-objective Evolutionary Algorithms},
  pages = {376--390}
}
@incollection{KhiAlbSol08,
  volume = 5217,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  editor = { Marco Dorigo  and others },
  fulleditor = { Marco Dorigo  and  Mauro Birattari  and  Christian Blum  and M. Clerc and  Thomas St{\"u}tzle  and A. F. T. Winfield},
  year = 2008,
  booktitle = {Ant Colony Optimization and Swarm Intelligence, 6th
                  International Conference, ANTS 2008},
  author = {M. Khichane and P. Albert and  Christine Solnon },
  title = {Integration of {ACO} in a Constraint Programming
                  Language},
  pages = {84--95}
}
@incollection{KhiAlbSol09,
  address = { Heidelberg },
  publisher = {Springer},
  year = 2009,
  editor = { Thomas St{\"u}tzle },
  volume = 5851,
  series = {Lecture Notes in Computer Science},
  booktitle = {Learning and Intelligent Optimization, Third International Conference, LION 3},
  author = {M. Khichane and P. Albert and  Christine Solnon },
  title = {An {ACO}-Based Reactive Framework for Ant Colony
                  Optimization: First Experiments on Constraint
                  Satisfaction Problems},
  adoi = {10.1007/978-3-642-11169-3_9},
  pages = {119--133}
}
@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}
}
@inproceedings{KhuXuHooLey2009:satenstein,
  publisher = {AAAI Press, Menlo Park, CA},
  editor = {Craig Boutilier},
  year = 2009,
  booktitle = {Proceedings of the Twenty-First International Joint
                  Conference on Artificial Intelligence (IJCAI-09)},
  author = { KhudaBukhsh, A. R.  and  Lin Xu  and  Holger H. Hoos  and  Kevin Leyton-Brown },
  title = {{SATenstein}: Automatically Building Local Search
                  {SAT} Solvers from Components},
  pages = {517--524},
  eprint = {http://ijcai.org/papers09/Papers/IJCAI09-093.pdf}
}
@article{KilUrl2015constr,
  author = {Philip Kilby  and Tommaso Urli},
  title = {Fleet design optimisation from historical data using constraint programming and large neighbourhood search},
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  year = 2015,
  pages = {1--20},
  publisher = {Springer, US},
  doi = {10.1007/s10601-015-9203-0},
  keywords = {F-race}
}
@article{Kim1993,
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  title = {Heuristics for Flowshop Scheduling Problems Minimizing Mean
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  year = 1993,
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  number = 1,
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}
@article{KimAllLop2020arxiv,
  author = { Kim, Youngmin  and  Allmendinger, Richard  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Safe Learning and Optimization Techniques: Towards a Survey
                  of the State of the Art},
  journal = {Arxiv preprint arXiv:2101.09505 [cs.LG]},
  year = 2020,
  url = {https://arxiv.org/abs/2101.09505},
  abstract = {Safe learning and optimization deals with learning and
                  optimization problems that avoid, as much as possible, the
                  evaluation of non-safe input points, which are solutions,
                  policies, or strategies that cause an irrecoverable loss
                  (e.g., breakage of a machine or equipment, or life
                  threat). Although a comprehensive survey of safe
                  reinforcement learning algorithms was published in 2015, a
                  number of new algorithms have been proposed thereafter, and
                  related works in active learning and in optimization were not
                  considered. This paper reviews those algorithms from a number
                  of domains including reinforcement learning, Gaussian process
                  regression and classification, evolutionary algorithms, and
                  active learning. We provide the fundamental concepts on which
                  the reviewed algorithms are based and a characterization of
                  the individual algorithms. We conclude by explaining how the
                  algorithms are connected and suggestions for future
                  research. }
}
@incollection{KimAllLop2020safe,
  year = 2020,
  volume = 12641,
  address = { Cham, Switzerland },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  booktitle = {Trustworthy AI -- Integrating Learning, Optimization and
                  Reasoning. TAILOR 2020},
  editor = {Fredrik Heintz and Michela Milano and Barry O'Sullivan},
  author = { Kim, Youngmin  and  Allmendinger, Richard  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Safe Learning and Optimization Techniques: Towards a Survey
                  of the State of the Art},
  pages = {123--139},
  doi = {10.1007/978-3-030-73959-1_12},
  abstract = {Safe learning and optimization deals with learning and
                  optimization problems that avoid, as much as possible, the
                  evaluation of non-safe input points, which are solutions,
                  policies, or strategies that cause an irrecoverable loss
                  (e.g., breakage of a machine or equipment, or life
                  threat). Although a comprehensive survey of safe
                  reinforcement learning algorithms was published in 2015, a
                  number of new algorithms have been proposed thereafter, and
                  related works in active learning and in optimization were not
                  considered. This paper reviews those algorithms from a number
                  of domains including reinforcement learning, Gaussian process
                  regression and classification, evolutionary computing, and
                  active learning. We provide the fundamental concepts on which
                  the reviewed algorithms are based and a characterization of
                  the individual algorithms. We conclude by explaining how the
                  algorithms are connected and suggestions for future
                  research.}
}
@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}
}
@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,
  title = {Optimization by Simulated Annealing: Quantitative Studies},
  author = { Scott Kirkpatrick },
  journal = {Journal of Statistical Physics},
  volume = 34,
  number = {5-6},
  pages = {975--986},
  year = 1984
}
@article{Kirkpatrick83,
  author = { Scott Kirkpatrick  and C. D. Gelatt and M. P. Vecchi},
  title = {Optimization by Simulated Annealing},
  journal = {Science},
  year = 1983,
  volume = 220,
  pages = {671--680},
  number = 4598,
  doi = {10.1126/science.220.4598.671}
}
@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}
}
@inproceedings{Kno2005:isda,
  year = 2005,
  booktitle = {Proceedings of the 5th International Conference on
                  Intelligent Systems Design and Applications},
  editor = {Abraham, Ajith and Paprzycki, Marcin},
  author = { Joshua D. Knowles },
  title = {A summary-attainment-surface plotting method for
                  visualizing the performance of stochastic
                  multiobjective optimizers},
  pages = {552--557},
  supplement = {http://dbkgroup.org/knowles/plot_attainments/},
  doi = {10.1109/ISDA.2005.15},
  eprint = {http://dbkgroup.org/knowles/ISDA05_Knowles_J.pdf}
}
@article{Kno2005tec,
  author = { Joshua D. Knowles },
  title = {Par{EGO}: 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},
  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
}
@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}
}
@incollection{KnoCor1999cec,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  year = 1999,
  booktitle = {Proceedings of the 1999 Congress on Evolutionary Computation
                  (CEC 1999)},
  key = {IEEE CEC},
  author = { Joshua D. Knowles  and  David Corne },
  pages = {98--105},
  title = {The {Pareto} Archived Evolution Strategy: A New
                  Baseline Algorithm for Multiobjective Optimisation},
  annote = {first mention of Adaptive Grid Archiving}
}
@incollection{KnoCor2002cec,
  year = 2002,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  booktitle = {Proceedings of  the 2002 Congress on Evolutionary Computation (CEC'02)},
  key = {IEEE CEC},
  author = { Joshua D. Knowles  and  David Corne },
  title = {On Metrics for Comparing Non-Dominated Sets},
  pages = {711--716}
}
@inproceedings{KnoCor2003emo,
  address = { Heidelberg },
  publisher = {Springer},
  volume = 2632,
  series = {Lecture Notes in Computer Science},
  shorteditor = { Carlos M. Fonseca  and  others},
  editor = { Carlos M. Fonseca  and  Peter J. Fleming  and  Eckart Zitzler  and  Kalyanmoy Deb  and  Lothar Thiele },
  year = 2003,
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2003},
  author = { Joshua D. Knowles  and  David Corne },
  title = {Instance Generators and Test Suites for the
                  Multiobjective Quadratic Assignment Problem},
  pages = {295--310}
}
@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}
}
@incollection{KnoCor2004lnems,
  year = 2004,
  address = { Berlin, Germany },
  publisher = {Springer},
  volume = 535,
  series = {Lecture Notes in Economics and Mathematical Systems},
  editor = { Xavier Gandibleux  and Marc Sevaux and  Kenneth S{\"o}rensen  and  V. {T'Kindt} },
  booktitle = {Metaheuristics for Multiobjective Optimisation},
  author = { Joshua D. Knowles  and  David Corne },
  title = {Bounded {Pareto} Archiving: {Theory} and Practice},
  pages = {39--64},
  alias = {Knowles+CorneLNEMS535},
  doi = {10.1007/978-3-642-17144-4_2}
}
@incollection{KnoCor2005mem,
  address = {Berlin\slash Heidelberg},
  publisher = {Springer},
  series = {Studies in Fuzziness and Soft Computing},
  volume = 166,
  year = 2005,
  editor = {Hart W. E. and Smith J. E. and Krasnogor N.},
  booktitle = {Recent Advances in Memetic Algorithms},
  title = {Memetic algorithms for multiobjective optimization: issues,
                  methods and prospects},
  author = { Joshua D. Knowles  and  David Corne },
  pages = {313--352},
  doi = {10.1007/3-540-32363-5_14}
}
@incollection{KnoCorDeb2008,
  publisher = {Springer},
  series = {Natural Computing Series},
  editor = { Joshua D. Knowles  and  David Corne  and  Kalyanmoy Deb  and Deva Raj Chair},
  year = 2008,
  booktitle = {Multiobjective Problem Solving from Nature},
  title = {Introduction: {Problem} solving, {EC} and {EMO}},
  author = { Joshua D. Knowles  and  David Corne  and  Kalyanmoy Deb },
  pages = {1--28},
  doi = {10.1007/978-3-540-72964-8_1}
}
@incollection{KnoCorFle2003,
  year = 2003,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  month = dec,
  volume = 4,
  booktitle = {Proceedings of the 2003 Congress on Evolutionary
                  Computation (CEC 2003)},
  key = {IEEE CEC},
  title = {Bounded archiving using the {Lebesgue} measure},
  author = { Joshua D. Knowles  and  David Corne  and Fleischer, Mark},
  pages = {2490--2497}
}
@incollection{KnoCorRey09emo,
  address = { Heidelberg },
  publisher = {Springer},
  year = 2009,
  series = {Lecture Notes in Computer Science},
  volume = 5467,
  editor = {Matthias Ehrgott and  Carlos M. Fonseca  and  Xavier Gandibleux  and Jin-Kao Hao and Marc Sevaux},
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2009},
  author = { Joshua D. Knowles  and  David Corne  and Alan P. Reynolds},
  title = {Noisy Multiobjective Optimization on a Budget of 250 Evaluations},
  pages = {36--50}
}
@techreport{KnoThiZit06:tutorial,
  author = { Joshua D. Knowles  and  Lothar Thiele  and  Eckart Zitzler },
  title = {A tutorial on the performance assessment of stochastic
                  multiobjective optimizers},
  institution = {Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Z{\"u}rich, Switzerland},
  year = 2006,
  type = {TIK-Report},
  number = 214,
  month = feb,
  note = {Revised version},
  eprint = {https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/23822/2/KTZ2006a.pdf}
}
@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}
}
@incollection{KnoWatCor2001reducing,
  year = 2001,
  series = {Lecture Notes in Computer Science},
  volume = 1993,
  address = { Heidelberg },
  publisher = {Springer},
  editor = { Eckart Zitzler  and  Kalyanmoy Deb  and  Lothar Thiele  and  Carlos A. {Coello Coello}  and  David Corne },
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2001},
  author = { Joshua D. Knowles  and Watson, Richard A. and  David Corne },
  title = {Reducing Local Optima in Single-Objective Problems by
                  Multi-objectivization},
  pages = {269--283},
  doi = {10.1007/3-540-44719-9_19},
  annote = {Proposed multi-objectivization}
}
@phdthesis{Knowles2002PhD,
  author = { Joshua D. Knowles },
  title = {Local-Search and Hybrid Evolutionary Algorithms for
                  {Pareto} Optimization},
  school = {University of Reading, UK},
  annote = {(Examiners: Prof. K. Deb and Prof. K. Warwick)},
  year = 2002
}
@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{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 = {T. C. Koopmans and M. J. Beckmann},
  title = {Assignment Problems and the Location of Economic
                  Activities},
  journal = {Econometrica},
  volume = 25,
  pages = {53--76},
  year = 1957
}
@inproceedings{KopYos2007visualization,
  title = {Visualization of {Pareto}-sets in evolutionary
                  multi-objective optimization},
  author = {Koppen, Mario and Yoshida, Kaori},
  booktitle = {7th International Conference on Hybrid Intelligent Systems
                  (HIS 2007)},
  pages = {156--161},
  year = 2007,
  organization = {IEEE}
}
@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{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.}
}
@incollection{KorSilOblKos07:cec,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  year = 2007,
  booktitle = {Proceedings of the 2007 Congress on Evolutionary
                  Computation (CEC 2007)},
  key = {IEEE CEC},
  author = { P. Koro{\v s}ec  and  Jurij {\v S}ilc  and K. Oblak and F. Kosel},
  title = {The differential ant-stigmergy algorithm: an
                  experimental evaluation and a real-world
                  application},
  pages = {157--164}
}
@incollection{KorSilRob04:ants2004,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 3172,
  editor = { Marco Dorigo  and others },
  fulleditor = { Marco Dorigo  and  L. M. Gambardella  and  Francesco Mondada  and  Thomas St{\"u}tzle  and  Mauro Birattari  and  Christian Blum },
  year = 2004,
  booktitle = {Ant Colony Optimization and Swarm Intelligence, 4th
                  International Workshop, ANTS 2004 },
  author = { P. Koro{\v s}ec  and  Jurij {\v S}ilc  and B. Robi{\v c}},
  title = {Mesh-Partitioning with the Multiple Ant-Colony
                  Algorithm},
  pages = {430--431}
}
@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}
}
@incollection{KorStuExn06,
  address = { Heidelberg },
  publisher = {Springer},
  year = 2006,
  volume = 4150,
  series = {Lecture Notes in Computer Science},
  editor = { Marco Dorigo  and others },
  fulleditor = { Marco Dorigo  and  L. M. Gambardella  and  Mauro Birattari  and 
                  Martinoli, A. and  Poli, R.  and  Thomas St{\"u}tzle },
  booktitle = {Ant Colony Optimization and Swarm Intelligence, 5th
                  International Workshop, ANTS 2006},
  author = { Oliver Korb  and  Thomas St{\"u}tzle  and  Thomas E. Exner },
  title = {{PLANTS}: {Application} of ant colony optimization
                  to structure-based drug design},
  pages = {247--258},
  doi = {10.1007/11839088_22}
}
@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{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
}
@book{KouYu1997:robustopt,
  author = { P. Kouvelis  and  G. Yu },
  title = {Robust discrete optimization and its applications},
  booktitle = {Robust discrete optimization and its applications},
  publisher = {Kluwer Academic Publishers, Dordrecht, The Netherlands},
  series = {Nonconvex optimization and its applications},
  year = 1997
}
@incollection{KovSkr08,
  adoi = {10.1007/978-3-540-87536-9},
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 5163,
  year = 2008,
  editor = {Kurkova-Pohlova, Vera and Koutnik, Jan},
  booktitle = {ICANN'08: Proceedings of the 18th International Conference on
                  Artificial Neural Networks, Part I},
  author = {O. Kov\'{a}\v{r}{\'i}k and M. Skrbek},
  title = {Ant Colony Optimization with Castes},
  pages = {435--442}
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  publisher = {Springer}
}
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  publisher = {{INFORMS}}
}
@book{LemPopBan2003,
  title = {Shaping the Next One Hundred Years: New Methods for
                  Quantitative, Long Term Policy Analysis},
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  publisher = {RAND},
  year = 2003
}
@article{Leon00,
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@incollection{LesDumStu04:ants,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 3172,
  editor = { Marco Dorigo  and others },
  fulleditor = { Marco Dorigo  and  L. M. Gambardella  and  Francesco Mondada  and  Thomas St{\"u}tzle  and  Mauro Birattari  and  Christian Blum },
  year = 2004,
  booktitle = {Ant Colony Optimization and Swarm Intelligence, 4th
                  International Workshop, ANTS 2004 },
  author = {L. Lessing and I. Dumitrescu and  Thomas St{\"u}tzle },
  title = {A Comparison Between {ACO} Algorithms for the Set
                  Covering Problem},
  pages = {1--12}
}
@article{Levin1973,
  title = {Universal'nyie perebornyie zadachi},
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  year = 1973
}
@book{Lewis2016,
  title = {A Guide to Graph Colouring: Algorithms and Applications},
  author = { Lewis, Rhyd M. R. },
  year = 2016,
  doi = {10.1007/978-3-319-25730-3},
  publisher = {Springer},
  address = { Cham, Switzerland },
  annote = {Supplementary material available at \cite{Lewis2016sup}}
}
@misc{Lewis2016sup,
  author = { Lewis, Rhyd M. R. },
  title = {Suite of Graph Colouring Algorithms -- Supplementary Material
                  to the Book ``{A} Guide to Graph Colouring: Algorithms and
                  Applications''},
  howpublished = {\url{http://rhydlewis.eu/resources/gCol.zip}},
  year = 2016
}
@incollection{LeyNudSho2002,
  year = 2002,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  booktitle = {Principles and Practice of Constraint Programming, CP 2002},
  editor = {van Hentenryck, Pascal },
  title = {Learning the Empirical Hardness of Optimization Problems: The
                  Case of Combinatorial Auctions},
  author = { Kevin Leyton-Brown  and Nudelman, Eugene and Shoham, Yoav},
  pages = {556--572}
}
@incollection{LeyPeaSho2000acmec,
  editor = {Anant Jhingran and others},
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2000,
  booktitle = {ACM Conference on Electronic Commerce (EC-00)},
  author = { Kevin Leyton-Brown  and  M. Pearson and Y. Shoham},
  title = {Towards a Universal Test Suite for Combinatorial Auction
                  Algorithms},
  pages = {66--76},
  doi = {10.1145/352871.352879},
  annote = {CPLEX-regions200 benchmark set,
                  \url{http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/results.html}}
}
@article{Li2008two,
  title = {A two-step rejection procedure for testing multiple
                  hypotheses},
  author = {Li, Jianjun David},
  journal = {Journal of Statistical Planning and Inference},
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  number = 6,
  pages = {1521--1527},
  year = 2008
}
@article{Li2016,
  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.},
  author = {Li, Zhiyi and Shahidehpour, Mohammad and Bahramirad, Shay and
                  Khodaei, Amin},
  doi = {10.1109/TSG.2016.2526032},
  journal = {IEEE Transactions on Smart Grid},
  number = 4,
  pages = {1--1},
  title = {Optimizing Traffic Signal Settings in Smart Cities},
  volume = 3053,
  year = 2016
}
@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},
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  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},
  url = {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},
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  number = 3,
  year = 2007,
  pages = {229--242},
  publisher = {IOS Press},
  address = {Amsterdam, The Netherlands}
}
@inproceedings{LiLiTanYao2014taa,
  year = 2014,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  booktitle = {Proceedings of  the 2014 Congress on Evolutionary Computation (CEC 2014)},
  key = {IEEE CEC},
  title = {An Improved Two Archive Algorithm for Many-Objective
                  Optimization},
  author = {Li, Bingdong and Li, Jinlong and Tang, Ke and Yao, Xin},
  pages = {2869--2876}
}
@article{LiLiTanYao2015many,
  author = {Li, Bingdong and Li, Jinlong and Tang, Ke and Yao, Xin},
  title = {Many-Objective Evolutionary Algorithms: A Survey},
  journal = {{ACM} Computing Surveys},
  volume = 48,
  number = 1,
  year = 2015,
  pages = {1--35}
}
@inproceedings{LiWanYuZhaLi08,
  author = {Z. Li and Y. Wang and J. Yu and Y. Zhang and X. Li},
  title = {A Novel Cloud-Based Fuzzy Self-Adaptive Ant Colony
                  System},
  booktitle = {ICNC'08: Proceedings of the 2008 Fourth
                  International Conference on Natural Computation},
  volume = 7,
  year = 2008,
  pages = {460--465},
  publisher = {IEEE Computer Society},
  address = {Washington, DC}
}
@incollection{LiYanLiuShe2013many,
  isbn = {978-3-642-37139-4},
  year = 2013,
  volume = 7811,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2013},
  editor = { Robin C. Purshouse  and  Peter J. Fleming  and  Carlos M. Fonseca  and  Salvatore Greco  and Jane Shaw},
  title = {A Comparative Study on Evolutionary Algorithms for
                  Many-Objective Optimization},
  author = {Li, Miqing and Yang, Shengxiang and Liu, Xiaohui and Shen,
                  Ruimin},
  pages = {261--275}
}
@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{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},
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  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}
}
@incollection{LiaMonDogStuDor2011gecco,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2011,
  editor = {Natalio Krasnogor and Pier Luca Lanzi},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2011},
  author = {Liao, Tianjun  and  Marco A. {Montes de Oca}  and  Do\v{g}an Ayd{\i}n  and  Thomas St{\"u}tzle  and  Marco Dorigo },
  title = {An Incremental Ant Colony Algorithm with Local Search for Continuous Optimization},
  pages = {125--132}
}
@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}
}
@inproceedings{LiaMonStu2011gecco,
  year = 2011,
  address = { New York, NY },
  publisher = {ACM Press},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference Companion, GECCO Companion 2011},
  editor = {Natalio Krasnogor and Pier Luca Lanzi},
  author = {Liao, Tianjun  and  Marco A. {Montes de Oca}  and  Thomas St{\"u}tzle },
  title = {Tuning Parameters across Mixed Dimensional
                  Instances: A Performance Scalability Study of
                  {Sep-G-CMA-ES}},
  annote = {Workshop on Scaling Behaviours of Landscapes,
                  Parameters and Algorithms},
  pages = {703--706}
}
@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}
}
@incollection{LiaStu2013cec,
  year = 2013,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  booktitle = {Proceedings of  the 2013 Congress on Evolutionary Computation (CEC 2013)},
  key = {IEEE CEC},
  author = {Liao, Tianjun  and  Thomas St{\"u}tzle },
  title = {Benchmark results for a simple hybrid algorithm on
                  the {CEC} 2013 benchmark set for real-parameter
                  optimization},
  pages = {1938--1944}
}
@article{LiaStuMonDor2014,
  author = {Liao, Tianjun  and  Thomas St{\"u}tzle  and  Marco A. {Montes de Oca}  and  Marco Dorigo },
  title = {A Unified Ant Colony Optimization Algorithm for Continuous
Optimization},
  journal = {European Journal of Operational Research},
  year = 2014,
  volume = 234,
  number = 3,
  pages = {597--609}
}
@article{LiaTseLua07,
  author = { C.-J. Liao  and  C.-T. Tseng  and  P. Luarn },
  title = {A Discrete Version of Particle Swarm Optimization
                  for Flowshop Scheduling Problems},
  journal = {Computers \& Operations Research},
  volume = 34,
  number = 10,
  pages = {3099--3111},
  year = 2007
}
@phdthesis{Liao2013,
  author = {Liao, Tianjun },
  title = {Population-based Heuristic Algorithms for Continuous and Mixed Discrete-Continuous Optimization Problem},
  school = {IRIDIA, {\'E}cole polytechnique, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2013
}
@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
}
@incollection{LieDerVerAguTan2017evocop,
  address = { Heidelberg },
  publisher = {Springer},
  volume = 10197,
  series = {Lecture Notes in Computer Science},
  year = 2017,
  booktitle = {Proceedings of EvoCOP 2017 -- 17th European Conference on Evolutionary Computation in Combinatorial Optimization },
  editor = { Bin Hu  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  author = { Arnaud Liefooghe  and  Bilel Derbel  and  Verel, S{\'e}bastien  and  Aguirre, Hern\'{a}n E.  and  Tanaka, Kiyoshi },
  title = {Towards Landscape-Aware Automatic Algorithm Configuration:
                  Preliminary Experiments on Neutral and Rugged Landscapes},
  pages = {215--232},
  doi = {10.1007/978-3-319-55453-2_15}
}
@incollection{LieDerVerLop2018ppsn,
  volume = 11102,
  year = 2018,
  address = { Cham, Switzerland },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { Anne Auger  and  Carlos M. Fonseca  and Louren{\c c}o, N. and Machado,
                  P. and  Lu{\'i}s Paquete  and  Darrell Whitley },
  booktitle = {Parallel Problem Solving from Nature - PPSN XV},
  author = { Arnaud Liefooghe  and  Bilel Derbel  and  Verel, S{\'e}bastien  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Aguirre, Hern\'{a}n E.  and  Tanaka, Kiyoshi },
  title = {On {Pareto} Local Optimal Solutions Networks},
  pages = {232--244},
  doi = {10.1007/978-3-319-99259-4_19}
}
@article{LieHumMes2011,
  author = { Arnaud Liefooghe  and  J{\'e}r{\'e}mie Humeau  and  Salma Mesmoudi  and  Laetitia Jourdan  and  Talbi, El-Ghazali },
  title = {On dominance-based multiobjective local search: design,
                  implementation and experimental analysis on scheduling and
                  traveling salesman problems},
  journal = {Journal of Heuristics},
  volume = 18,
  number = 2,
  pages = {317--352},
  year = 2012,
  abstract = {This paper discusses simple local search approaches for
                  approximating the efficient set of multiobjective
                  combinatorial optimization problems. We focus on algorithms
                  defined by a neighborhood structure and a dominance relation
                  that iteratively improve an archive of nondominated
                  solutions. Such methods are referred to as dominance-based
                  multiobjective local search. We first provide a concise
                  overview of existing algorithms, and we propose a model
                  trying to unify them through a fine-grained
                  decomposition. The main problem-independent search components
                  of dominance relation, solution selection, neighborhood
                  exploration and archiving are largely discussed. Then, a
                  number of state-of-the-art and original strategies are
                  experimented on solving a permutation flowshop scheduling
                  problem and a traveling salesman problem, both on a two- and
                  a three-objective formulation. Experimental results and a
                  statistical comparison are reported in the paper, and some
                  directions for future research are highlighted.},
  doi = {10.1007/s10732-011-9181-3}
}
@article{LieJouTal2011paradiseo,
  title = {A Software Framework Based on a Conceptual Unified
                  Model for Evolutionary Multiobjective Optimization:
                  {ParadisEO}-{MOEO}},
  author = { Arnaud Liefooghe  and  Laetitia Jourdan  and  Talbi, El-Ghazali },
  journal = {European Journal of Operational Research},
  volume = 209,
  number = 2,
  pages = {104--112},
  year = 2011
}
@incollection{LieLopPaqVer2018gecco,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2018,
  editor = { Aguirre, Hern\'{a}n E.  and Keiki Takadama},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2018},
  author = { Arnaud Liefooghe  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete  and  Verel, S{\'e}bastien },
  title = {Dominance, Epsilon, and Hypervolume Local Optimal Sets in
                  Multi-objective Optimization, and How to Tell the Difference},
  pages = {324--331},
  doi = {10.1145/3205455.3205572}
}
@incollection{LieMesHum2009sls,
  volume = 5752,
  series = {Lecture Notes in Computer Science},
  year = 2009,
  address = { Heidelberg },
  publisher = {Springer},
  booktitle = {Engineering Stochastic Local Search
                  Algorithms. Designing, Implementing and Analyzing
                  Effective Heuristics. SLS~2009},
  editor = { Thomas St{\"u}tzle  and  Mauro Birattari  and  Holger H. Hoos },
  title = {A Study on Dominance-based Local Search Approaches
                  for Multiobjective Combinatorial Optimization},
  author = { Arnaud Liefooghe  and  Salma Mesmoudi  and  J{\'e}r{\'e}mie Humeau  and  Laetitia Jourdan  and  Talbi, El-Ghazali },
  pages = {120--124}
}
@incollection{LiePaqSim2011,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 6622,
  year = 2011,
  editor = { Peter Merz  and  Jin-Kao Hao },
  booktitle = {Proceedings of EvoCOP 2011 -- 11th European Conference on Evolutionary Computation in Combinatorial Optimization },
  author = { Arnaud Liefooghe  and  Lu{\'i}s Paquete  and Sim{\={o}}es, Marco and  Jos{\'e} Rui Figueira },
  title = {Connectedness and Local Search for Bicriteria Knapsack Problems},
  pages = {48--59},
  doi = {10.1007/978-3-642-20364-0_5}
}
@incollection{LieVerLac2021gecco,
  location = {Lille, France},
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2021,
  editor = { Chicano, Francisco  and  Krzysztof Krawiec },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2021},
  author = { Arnaud Liefooghe  and  Verel, S{\'e}bastien  and Benjamin Lacroix and Alexandru{-}Ciprian Zavoianu and John McCall},
  doi = {10.1145/3449639.3459353},
  pages = {421--429},
  title = {Landscape features and automated algorithm selection for
                  multi-objective interpolated continuous optimisation
                  problems}
}
@article{LikKoc2007predictive,
  title = {Predictive control of a gas--liquid separation plant based on
                  a {Gaussian} process model},
  author = {Likar, Bojan and Kocijan, Ju{\v{s}}},
  journal = {Computers \& Chemical Engineering},
  volume = 31,
  number = 3,
  pages = {142--152},
  year = 2007,
  publisher = {Elsevier},
  doi = {10.1016/j.compchemeng.2006.05.011}
}
@book{Lilja2000measuring,
  author = {Lilja, David J.},
  title = {Measuring Computer Performance: A Practitioner's Guide},
  doi = {10.1017/CBO9780511612398},
  publisher = {Cambridge University Press},
  year = 2000,
  abstract = {Measuring Computer Performance sets out the fundamental
                  techniques used in analyzing and understanding the
                  performance of computer systems. Throughout the book, the
                  emphasis is on practical methods of measurement, simulation,
                  and analytical modeling. The author discusses performance
                  metrics and provides detailed coverage of the strategies used
                  in benchmark programmes. He gives intuitive explanations of
                  the key statistical tools needed to interpret measured
                  performance data. He also describes the general 'design of
                  experiments' technique, and shows how the maximum amount of
                  information can be obtained for the minimum effort. The book
                  closes with a chapter on the technique of queueing
                  analysis. Appendices listing common probability distributions
                  and statistical tables are included, along with a glossary of
                  important technical terms. This practically-oriented book
                  will be of great interest to anyone who wants a detailed, yet
                  intuitive, understanding of computer systems performance
                  analysis.}
}
@inproceedings{LinHooHutSch2015aaai,
  year = 2015,
  publisher = {{AAAI} Press},
  booktitle = {Proceedings of  the {AAAI} Conference on Artificial Intelligence},
  editor = {Blai Bonet and Sven Koenig},
  title = {{AutoFolio}: Algorithm Configuration for Algorithm Selection},
  author = { Marius Thomas Lindauer  and  Holger H. Hoos  and  Frank Hutter  and Schaub, Torsten}
}
@article{LinHooHutSch2015autofolio,
  title = {{AutoFolio}: An Automatically Configured Algorithm Selector},
  author = { Marius Thomas Lindauer  and  Holger H. Hoos  and  Frank Hutter  and Schaub, Torsten},
  journal = {Journal of Artificial Intelligence Research},
  volume = 53,
  pages = {745--778},
  year = 2015
}
@article{LinKer73,
  author = {S. Lin and B. W. Kernighan},
  title = {An Effective Heuristic Algorithm for the Traveling
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  year = 1973,
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@inproceedings{LinLuo07,
  author = {W. Ling and H. Luo},
  title = {An Adaptive Parameter Control Strategy for Ant
                  Colony Optimization},
  booktitle = {CIS'07: Proceedings of the 2007 International
                  Conference on Computational Intelligence and
                  Security},
  year = 2007,
  pages = {142--146},
  publisher = {IEEE Computer Society},
  address = {Washington, DC}
}
@article{LinVanKot2019,
  title = {The algorithm selection competitions 2015 and 2017},
  author = { Marius Thomas Lindauer  and  Van Rijn, Jan N.  and Kotthoff, Lars},
  journal = {Artificial Intelligence},
  volume = 272,
  pages = {86--100},
  year = 2019
}
@article{LisWit2015tcs,
  title = {Runtime Analysis of Ant Colony Optimization on Dynamic
                  Shortest Path Problems},
  journal = {Theoretical Computer Science},
  volume = 561,
  number = {Part A},
  pages = {73--85},
  year = 2015,
  doi = {10.1016/j.tcs.2014.06.035},
  author = { Andrei Lissovoi  and  Carsten Witt },
  abstract = {A simple ACO algorithm called $\lambda$-MMAS for dynamic
                  variants of the single-destination shortest paths problem is
                  studied by rigorous runtime analyses. Building upon previous
                  results for the special case of 1-MMAS, it is studied to what
                  extent an enlarged colony using $\lambda$ ants per vertex
                  helps in tracking an oscillating optimum. It is shown that
                  easy cases of oscillations can be tracked by a constant
                  number of ants. However, the paper also identifies more
                  involved oscillations that with overwhelming probability
                  cannot be tracked with any polynomial-size colony. Finally,
                  parameters of dynamic shortest-path problems which make the
                  optimum difficult to track are discussed. Experiments
                  illustrate theoretical findings and conjectures. }
}
@article{LitMurSweKar63,
  author = {J. D. C. Little and K. G. Murty and D. W. Sweeney and C. Karel},
  title = {An Algorithm for the Traveling Salesman Problem},
  journal = {Operations Research},
  year = 1963,
  volume = 11,
  pages = {972--989}
}
@article{LiuMalWanBre2017vis,
  author = { Shusen Liu  and  Dan Maljovec  and  Bei Wang  and 
                  Peer-Timo Bremer  and  Valerio Pascucci},
  title = {Visualizing High-Dimensional Data: Advances in the Past
                  Decade},
  doi = {10.1109/TVCG.2016.2640960},
  year = 2017,
  journal = { IEEE Transactions on Visualization and Computer Graphics },
  volume = 23,
  number = 3
}
@article{LiuRee2001,
  author = {Jiyin Liu and  Colin R. Reeves },
  title = {Constructive and Composite Heuristic Solutions to the
                  {P//$\Sigma$Ci} Scheduling Problem},
  journal = {European Journal of Operational Research},
  volume = 132,
  number = 2,
  pages = {439--452},
  year = 2001,
  doi = {10.1016/S0377-2217(00)00137-5}
}
@misc{LocalSolver,
  author = {{Innovation 24}},
  title = {{LocalSolver}},
  howpublished = {\url{http://www.localsolver.com/product.html}},
  note = {Last visited, August 15, 2016},
  year = 2016
}
@article{LodMarMon2002,
  title = {Two-dimensional packing problems: A survey},
  author = { Andrea Lodi  and  Silvano Martello  and  Monaci, Michele },
  journal = {European Journal of Operational Research},
  volume = 141,
  number = 2,
  pages = {241--252},
  year = 2002,
  doi = {10.1016/S0377-2217(02)00123-6}
}
@article{LodMarVig1999binpack,
  title = {Heuristic and metaheuristic approaches for a class of
                  two-dimensional bin packing problems},
  author = { Andrea Lodi  and  Silvano Martello  and  Vigo, Daniele },
  journal = {INFORMS Journal on Computing},
  volume = 11,
  number = 4,
  pages = {345--357},
  year = 1999,
  publisher = {{INFORMS}},
  doi = {10.1287/ijoc.11.4.345}
}
@article{LodMarVig2004tspack,
  title = {{TSpack}: a unified tabu search code for multi-dimensional bin
                  packing problems},
  author = { Andrea Lodi  and  Silvano Martello  and  Vigo, Daniele },
  journal = {Annals of Operations Research},
  volume = 131,
  number = {1-4},
  pages = {203--213},
  year = 2004,
  publisher = {Springer},
  doi = {10.1023/B:ANOR.0000039519.03572.08}
}
@incollection{LodTra2013,
  year = 2013,
  editor = {Topaluglu, Huseyin},
  publisher = {{INFORMS}},
  booktitle = {Theory Driven by Influential Applications},
  author = { Andrea Lodi  and Tramontani, Andrea},
  title = {Performance Variability in Mixed-Integer Programming},
  pages = {1--12}
}
@misc{LodiEtAl2004sup,
  author = { Andrea Lodi  and  Silvano Martello  and  Vigo, Daniele },
  title = {Two- and Three-Dimensional Bin Packing -- Source Code of
                  {TSpack}},
  howpublished = {\url{http://or.dei.unibo.it/research_pages/ORcodes/TSpack.html}},
  year = 2004
}
@article{LohHorLin2008antennas,
  author = {Lohn, Jason D. and Hornby, Gregory S. and Linden, Derek S.},
  title = {Human-competitive Evolved Antennas},
  journal = {Artificial Intelligence for Engineering Design, Analysis and Manufacturing},
  volume = 22,
  number = 3,
  year = 2008,
  pages = {235--247},
  doi = {10.1017/s0890060408000164},
  publisher = {Cambridge University Press},
  annote = {Evolutionary optimization of antennas for NASA}
}
@inproceedings{LohNow2013faster,
  address = { Berlin, Germany },
  publisher = {Springer},
  doi = {10.1007/978-3-642-40935-6},
  year = 2013,
  volume = 8139,
  series = {Lecture Notes in Computer Science},
  booktitle = {Proceedings of Algorithmic Learning Theory},
  editor = {Sanjay Jain and R{\'{e}}mi Munos and Frank Stephan and Thomas
                  Zeugmann},
  title = {Faster {Hoeffding} Racing: {Bernstein} Races via Jackknife
                  Estimates},
  author = {Loh, Po-Ling and Nowozin, Sebastian},
  pages = {203--217}
}
@incollection{Lop07:HPC_ACO,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  editor = {Paola Alberigo and Giovanni Erbacci and Francesca
                  Garofalo and Silvia Monfardini},
  booktitle = {Science and Sumpercomputing in Europe},
  title = {High Performance Ant Colony Optimisation of the Pump
                  Scheduling Problem},
  publisher = {CINECA},
  year = 2007,
  pages = {371--375},
  isbn = {978-88-86037-21-1}
}
@techreport{LopBlu08:tsptw,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Christian Blum },
  title = {Beam-{ACO} Based on Stochastic Sampling: {A} Case
                  Study on the {TSP} with Time Windows},
  institution = {Department LSI, Universitat Polit{\`e}cnica de Catalunya},
  year = 2008,
  number = {LSI-08-28},
  note = {Extended version published in Computers \& Operations Research~\cite{LopBlu2010cor}}
}
@incollection{LopBlu09:evocop,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 5482,
  year = 2009,
  editor = { Carlos Cotta  and P. Cowling},
  booktitle = {Proceedings of EvoCOP 2009 -- 9th European Conference on Evolutionary Computation in Combinatorial Optimization },
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Christian Blum  and  Dhananjay Thiruvady  and  Andreas T. Ernst  and  Bernd Meyer },
  title = {Beam-{ACO} based on stochastic sampling for makespan
                  optimization concerning the {TSP} with time windows},
  pages = {97--108},
  doi = {10.1007/978-3-642-01009-5_9},
  alias = {Lop++09}
}
@incollection{LopBlu09:lion,
  address = { Heidelberg },
  publisher = {Springer},
  year = 2009,
  editor = { Thomas St{\"u}tzle },
  volume = 5851,
  series = {Lecture Notes in Computer Science},
  booktitle = {Learning and Intelligent Optimization, Third International Conference, LION 3},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Christian Blum },
  title = {Beam-{ACO} Based on Stochastic Sampling: {A} Case
                  Study on the {TSP} with Time Windows},
  pages = {59--73},
  doi = {10.1007/978-3-642-11169-3_5}
}
@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},
  eprint = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2013-011.pdf}
}
@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},
  eprint = {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{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{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}
}
@misc{LopDubPerStuBir2016irace-supp,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  J{\'e}r{\'e}mie Dubois-Lacoste  and   P{\'e}rez C{\'a}ceres, Leslie and  Thomas St{\"u}tzle  and  Mauro Birattari },
  title = {The {\rpackage{irace}} Package: Iterated Racing for Automatic Algorithm
                  Configuration (Supplementary Material)},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2016-003}},
  year = 2016
}
@techreport{LopDubStu2011irace,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  J{\'e}r{\'e}mie Dubois-Lacoste  and  Thomas St{\"u}tzle  and  Mauro Birattari },
  title = {The {\rpackage{irace}} package, Iterated Race for Automatic
                  Algorithm Configuration},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2011,
  number = {TR/IRIDIA/2011-004},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2011-004.pdf},
  note = {Published in Operations Research Perspectives~\cite{LopDubPerStuBir2016irace}}
}
@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 = {}
}
@incollection{LopKno2015emo,
  editor = { Ant{\'o}nio Gaspar{-}Cunha  and  Carlos Henggeler Antunes and  Carlos A. {Coello Coello} },
  volume = 9019,
  year = 2015,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2015 Part {II}},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Joshua D. Knowles },
  title = {Machine Decision Makers as a Laboratory for Interactive {EMO}},
  pages = {295--309},
  abstract = {A key challenge, perhaps the central challenge, of
                  multi-objective optimization is how to deal with candidate
                  solutions that are ultimately evaluated by the hidden or
                  unknown preferences of a human decision maker (DM) who
                  understands and cares about the optimization problem.
                  Alternative ways of addressing this challenge exist but
                  perhaps the favoured one currently is the interactive
                  approach (proposed in various forms). Here, an evolutionary
                  multi-objective optimization algorithm (EMOA) is controlled
                  by a series of interactions with the DM so that preferences
                  can be elicited and the direction of search controlled. MCDM
                  has a key role to play in designing and evaluating these
                  approaches, particularly in testing them with real DMs, but
                  so far quantitative assessment of interactive EMOAs has been
                  limited.  In this paper, we propose a conceptual framework
                  for this problem of quantitative assessment, based on the
                  definition of machine decision makers (machine DMs), made
                  somewhat realistic by the incorporation of various
                  non-idealities. The machine DM proposed here draws from
                  earlier models of DM biases and inconsistencies in the MCDM
                  literature.  As a practical illustration of our approach, we
                  use the proposed machine DM to study the performance of an
                  interactive EMOA, and discuss how this framework could help
                  in the evaluation and development of better interactive
                  EMOAs.},
  doi = {10.1007/978-3-319-15892-1_20}
}
@incollection{LopKnoLau2011emo,
  address = { Heidelberg },
  publisher = {Springer},
  year = 2011,
  series = {Lecture Notes in Computer Science},
  volume = 6576,
  editor = { Takahashi, R. H. C.  and others},
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2011},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Joshua D. Knowles  and  Marco Laumanns },
  title = {On Sequential Online Archiving of Objective Vectors},
  pages = {46--60},
  doi = {10.1007/978-3-642-19893-9_4},
  abstract = {In this paper, we examine the problem of maintaining
                  an approximation of the set of nondominated points
                  visited during a multiobjective optimization, a
                  problem commonly known as archiving. Most of the
                  currently available archiving algorithms are
                  reviewed, and what is known about their convergence
                  and approximation properties is summarized. The main
                  scenario considered is the restricted case where the
                  archive must be updated online as points are
                  generated one by one, and at most a fixed number of
                  points are to be stored in the archive at any one
                  time. In this scenario, the better-monotonicity of
                  an archiving algorithm is proposed as a weaker, but
                  more practical, property than negative efficiency
                  preservation. This paper shows that
                  hypervolume-based archivers and a recently proposed
                  multi-level grid archiver have this property. On the
                  other hand, the archiving methods used by SPEA2 and
                  NSGA-II do not, and they may better-deteriorate with
                  time. The better-monotonicity property has meaning
                  on any input sequence of points. We also classify
                  archivers according to limit properties,
                  i.e. convergence and approximation properties of the
                  archiver in the limit of infinite (input) samples
                  from a finite space with strictly positive
                  generation probabilities for all points. This paper
                  establishes a number of research questions, and
                  provides the initial framework and analysis for
                  answering them.},
  annote = {Revised version available at \url{http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2011-001.pdf}}
}
@incollection{LopLiaStu2012ppsn,
  volume = 7491,
  year = 2012,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { Carlos A. {Coello Coello}  and others},
  booktitle = {Parallel Problem Solving from Nature, PPSN XII},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and Liao, Tianjun  and  Thomas St{\"u}tzle },
  title = {On the anytime behavior of {IPOP-CMA-ES}},
  pages = {357--366},
  doi = {10.1007/978-3-642-32937-1_36}
}
@misc{LopLiaStu2012ppsn-supp,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and Liao, Tianjun  and  Thomas St{\"u}tzle },
  title = {On the anytime behavior of {IPOP-CMA-ES}: Supplementary material},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2012-010/}},
  year = 2012
}
@incollection{LopLieVer2014ppsn,
  year = 2014,
  volume = 8672,
  address = { Heidelberg },
  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 = {PPSN 2014},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Arnaud Liefooghe  and  Verel, S{\'e}bastien },
  doi = {10.1007/978-3-319-10762-2_61},
  title = {Local Optimal Sets and Bounded Archiving on Multi-objective
                  {NK}-Landscapes with Correlated Objectives},
  pages = {621--630}
}
@inproceedings{LopMasMarStu2013mista,
  year = 2013,
  editor = { Graham Kendall  and  Greet Vanden Berghe  and Barry McCollum},
  address = {Gent, Belgium},
  booktitle = {Multidisciplinary International Conference on Scheduling:
                  Theory and Applications (MISTA 2013)},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Franco Mascia  and  Marie-El{\'e}onore Marmion  and  Thomas St{\"u}tzle },
  title = {Automatic Design of a Hybrid Iterated Local Search for the
                  Multi-Mode Resource-Constrained Multi-Project Scheduling
                  Problem},
  pages = {1--6},
  annote = {\url{https://hal.inria.fr/hal-01094681}}
}
@incollection{LopPaqStu04:ants,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 3172,
  editor = { Marco Dorigo  and others },
  fulleditor = { Marco Dorigo  and  L. M. Gambardella  and  Francesco Mondada  and  Thomas St{\"u}tzle  and  Mauro Birattari  and  Christian Blum },
  year = 2004,
  booktitle = {Ant Colony Optimization and Swarm Intelligence, 4th
                  International Workshop, ANTS 2004 },
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
  title = {On the Design of {ACO} for the Biobjective Quadratic
                  Assignment Problem},
  pages = {214--225},
  doi = {10.1007/978-3-540-28646-2_19}
}
@techreport{LopPaqStu04:hybrid,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
  title = {Hybrid Population-based Algorithms for the
                  Bi-objective Quadratic Assignment Problem},
  institution = {FG Intellektik, FB Informatik, TU Darmstadt},
  year = 2004,
  number = {AIDA--04--11},
  month = dec,
  note = {Published in Journal of Mathematical Modelling and Algorithms~\cite{LopPaqStu05:jmma}}
}
@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.}
}
@incollection{LopPaqStu09emaa,
  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 = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
  title = {Exploratory Analysis of Stochastic Local Search
                  Algorithms in Biobjective Optimization},
  pages = {209--222},
  doi = {10.1007/978-3-642-02538-9_9},
  abstract = {This chapter introduces two Perl programs that
                  implement graphical tools for exploring the
                  performance of stochastic local search algorithms
                  for biobjective optimization problems. These tools
                  are based on the concept of the empirical attainment
                  function (EAF), which describes the probabilistic
                  distribution of the outcomes obtained by a
                  stochastic algorithm in the objective space. In
                  particular, we consider the visualization of
                  attainment surfaces and differences between the
                  first-order EAFs of the outcomes of two
                  algorithms. This visualization allows us to identify
                  certain algorithmic behaviors in a graphical way.
                  We explain the use of these visualization tools and
                  illustrate them with examples arising from
                  practice.}
}
@misc{LopPaqStu2010:eaftools,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
  title = {{EAF} Graphical Tools},
  year = 2010,
  howpublished = {\url{http://lopez-ibanez.eu/eaftools}},
  note = {These tools are described in the book chapter
                  ``\emph{Exploratory analysis of stochastic local search
                  algorithms in biobjective
                  optimization}''~\cite{LopPaqStu09emaa}.},
  annote = {Please cite the book chapter, not this.}
}
@techreport{LopPerDubStuBir2016iraceguide,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and   P{\'e}rez C{\'a}ceres, Leslie and  J{\'e}r{\'e}mie Dubois-Lacoste  and  Thomas St{\"u}tzle  and  Mauro Birattari },
  title = {The {\rpackage{irace}} package: User Guide},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2016,
  number = {TR/IRIDIA/2016-004},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2016-004.pdf}
}
@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}
}
@inproceedings{LopPraPae08:WDSA,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  T. Devi Prasad  and  Ben Paechter },
  title = {Parallel Optimisation Of Pump Schedules With A
                  Thread-Safe Variant Of {EPANET} Toolkit},
  booktitle = {Proceedings of the 10th Annual Water Distribution
                  Systems Analysis Conference (WDSA 2008)},
  year = 2008,
  editor = { Jakobus E. van Zyl  and  A. A. Ilemobade  and  H. E. Jacobs },
  month = aug,
  doi = {10.1061/41024(340)40},
  publisher = {ASCE}
}
@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}},
  eprint = {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.}
}
@incollection{LopPraPae:gecco07,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2007,
  editor = {Dirk Thierens and others},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2007},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  T. Devi Prasad  and  Ben Paechter },
  title = {Solving Optimal Pump Control Problem using
                  {\MaxMinAntSystem}},
  volume = 1,
  pages = 176,
  doi = {10.1145/1276958.1276990}
}
@incollection{LopPraPaech05:cec,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  month = sep,
  year = 2005,
  booktitle = {Proceedings of  the 2005 Congress on Evolutionary Computation (CEC 2005)},
  key = {IEEE CEC},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  T. Devi Prasad  and  Ben Paechter },
  title = {Multi-objective Optimisation of the Pump Scheduling
                  Problem using {SPEA2}},
  pages = {435--442},
  volume = 1,
  doi = {10.1109/CEC.2005.1554716}
}
@inproceedings{LopPraPaech:ccwi2005,
  month = sep,
  address = {University of Exeter, UK},
  volume = 1,
  editor = { Dragan A. Savic  and  Godfrey A. Walters  and  Roger King  and  Soon Thiam-Khu },
  year = 2005,
  booktitle = {Proceedings of the Eighth International Conference on
                  Computing and Control for the Water Industry (CCWI 2005)},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  T. Devi Prasad  and  Ben Paechter },
  title = {Optimal Pump Scheduling: Representation and Multiple
                  Objectives},
  pages = {117--122}
}
@incollection{LopStu09ea,
  address = { Heidelberg },
  publisher = {Springer},
  editor = {Pierre Collet and Nicolas Monmarch{\'e} and Pierrick
                  Legrand and Marc Schoenauer and Evelyne Lutton},
  shorteditor = {Pierre Collet and others},
  volume = 5975,
  series = {Lecture Notes in Computer Science},
  year = 2010,
  booktitle = {Artificial Evolution: 9th International Conference, Evolution Artificielle, EA, 2009},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {An Analysis of Algorithmic Components for
                  Multiobjective Ant Colony Optimization: {A} Case
                  Study on the Biobjective {TSP}},
  pages = {134--145},
  doi = {10.1007/978-3-642-14156-0_12}
}
@incollection{LopStu2010:ants,
  volume = 6234,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  fulleditor = { Marco Dorigo  and  Mauro Birattari  and  Gianni A. {Di Caro}  and Doursat, R. and Engelbrecht, A. P. and Floreano,
                  D. and Gambardella, L. M. and Gro\ss, R. and Sahin,
                  E. and  Thomas St{\"u}tzle  and Sayama, H.},
  editor = { Marco Dorigo  and others },
  year = 2010,
  booktitle = {Swarm Intelligence, 7th International Conference, ANTS 2010},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Configuration of Multi-Objective {ACO}
                  Algorithms},
  pages = {95--106},
  doi = {10.1007/978-3-642-15461-4_9},
  abstract = {In the last few years a significant number of ant
                  colony optimization (ACO) algorithms have been
                  proposed for tackling multi-objective optimization
                  problems. In this paper, we propose a software
                  framework that allows to instantiate the most
                  prominent multi-objective ACO (MOACO)
                  algorithms. More importantly, the flexibility of
                  this MOACO framework allows the application of
                  automatic algorithm configuration techniques. The
                  experimental results presented in this paper show
                  that such an automatic configuration of MOACO
                  algorithms is highly desirable, given that our
                  automatically configured algorithms clearly
                  outperform the best performing MOACO algorithms that
                  have been proposed in the literature. As far as we
                  are aware, this paper is also the first to apply
                  automatic algorithm configuration techniques to
                  multi-objective stochastic local search algorithms.}
}
@incollection{LopStu2010:gecco,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2010,
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2010},
  editor = {Martin Pelikan and  J{\"u}rgen Branke },
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {The impact of design choices of multi-objective ant
                  colony optimization algorithms on performance: An
                  experimental study on the biobjective {TSP}},
  doi = {10.1145/1830483.1830494},
  pages = {71--78},
  abstract = {Over the last few years, there have been a number of
                  proposals of ant colony optimization (ACO)
                  algorithms for tackling multiobjective combinatorial
                  optimization problems. These proposals adapt ACO
                  concepts in various ways, for example, some use
                  multiple pheromone matrices and multiple heuristic
                  matrices and others use multiple ant colonies.\\ In
                  this article, we carefully examine several of the
                  most prominent of these proposals. In particular, we
                  identify commonalities among the approaches by
                  recasting the original formulation of the algorithms
                  in different terms. For example, several proposals
                  described in terms of multiple colonies can be cast
                  equivalently using a single ant colony, where ants
                  use different weights for aggregating the pheromone
                  and/or the heuristic information. We study
                  algorithmic choices for the various proposals and we
                  identify previously undetected trade-offs in their
                  performance.}
}
@misc{LopStu2010:gecco-supp,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {The impact of design choices of multi-objective ant
                  colony optimization algorithms on performance: An
                  experimental study on the biobjective {TSP}},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2010-003/}},
  year = 2010,
  note = {Supplementary material of \cite{LopStu2010:gecco}}
}
@misc{LopStu2011moaco-supp,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {The Automatic Design of Multi-Objective Ant Colony
                  Optimization Algorithms: {Supplementary} material},
  url = {http://iridia.ulb.ac.be/supp/IridiaSupp2011-007/Iridia-2011-007.pdf},
  year = 2011
}
@misc{LopStu2012si-supp,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {An experimental analysis of design choices of
                  multi-objective ant colony optimization algorithms:
                  Supplementary material},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2012-006/}},
  year = 2012
}
@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.}
}
@incollection{LopStuDor2017aco,
  isbn = {978-3-319-07125-1},
  publisher = {Springer International Publishing},
  year = 2018,
  booktitle = {Handbook of Heuristics},
  editor = {Rafael Mart{\'i} and  Panos M. Pardalos  and  Mauricio G. C. Resende },
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle  and  Marco Dorigo },
  title = {Ant Colony Optimization: A Component-Wise Overview},
  doi = {10.1007/978-3-319-07124-4_21},
  pages = {371--407},
  supplement = {http://iridia.ulb.ac.be/aco-tsp-qap/}
}
@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}
}
@phdthesis{LopezDiploma,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Multi-objective Ant Colony Optimization},
  school = {Intellectics Group, Computer Science Department, Technische
                  Universit{\"a}t Darmstadt, Germany},
  year = 2004,
  type = {Diploma thesis}
}
@phdthesis{LopezIbanezPhD,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Operational Optimisation of Water Distribution
                  Networks},
  school = {School of Engineering and the Built Environment},
  year = 2009,
  address = {Edinburgh Napier University, UK},
  url = {http://researchrepository.napier.ac.uk/id/eprint/3044}
}
@incollection{LosSchSeb2012ppsn,
  volume = 7491,
  year = 2012,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { Carlos A. {Coello Coello}  and others},
  booktitle = {Parallel Problem Solving from Nature, PPSN XII},
  author = {Ilya Loshchilov  and  Marc Schoenauer  and  Mich{\`e}le Sebag },
  title = {Alternative Restart Strategies for {CMA-ES}},
  pages = {296--305},
  doi = {10.1007/978-3-642-32937-1_30}
}
@incollection{LotMie2008vis,
  editor = { J{\"u}rgen Branke  and  Kalyanmoy Deb  and  Kaisa Miettinen  and  Roman S{\l}owi{\'n}ski },
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 5252,
  year = 2008,
  booktitle = {Multiobjective Optimization: Interactive and Evolutionary
                  Approaches},
  author = {Lotov, A. V.  and  Kaisa Miettinen },
  title = {Visualizing the {Pareto} Frontier},
  pages = {213--243}
}
@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}
}
@incollection{LouMarStu01,
  alias = {GloKoc02:handbook-mh},
  publisher = {Kluwer Academic Publishers, Norwell, MA},
  year = 2002,
  editor = { Fred Glover  and Gary A. Kochenberger},
  booktitle = {Handbook of Metaheuristics},
  author = { Helena R. {Louren{\c c}o}  and  Olivier Martin  and  Thomas St{\"u}tzle },
  title = {Iterated Local Search},
  pages = {321--353},
  doi = {10.1007/0-306-48056-5_11}
}
@incollection{LouMarStu2010:mh,
  address = { New York, NY },
  publisher = {Springer},
  edition = {2nd},
  series = {International Series in Operations Research \& Management
                  Science},
  volume = 146,
  booktitle = {Handbook of Metaheuristics},
  year = 2010,
  editor = { Michel Gendreau  and  Jean-Yves Potvin },
  author = { Helena R. {Louren{\c c}o}  and  Olivier Martin  and  Thomas St{\"u}tzle },
  title = {Iterated Local Search: Framework and Applications},
  chapter = 9,
  pages = {363--397},
  doi = {10.1007/978-1-4419-1665-5_12}
}
@incollection{LouMarStu2019hb,
  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 = { Helena R. {Louren{\c c}o}  and  Olivier Martin  and  Thomas St{\"u}tzle },
  title = {Iterated Local Search: Framework and Applications},
  chapter = 5,
  pages = {129--168},
  doi = {10.1007/978-3-319-91086-4_5}
}
@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{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,
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@incollection{LusTeg2010,
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  volume = 272,
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  booktitle = {Advances in Multi-Objective Nature Inspired Computing},
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  title = {The multiobjective traveling salesman problem:
                  A survey and a new approach},
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}
@article{LusTeg2010arxiv,
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  journal = {Arxiv preprint arXiv:1007.4063},
  year = 2010,
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}
@article{LusTeg2012itor,
  title = {The multiobjective multidimensional knapsack
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}
@article{LustJasz09btsp,
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}
@article{LuvBarBri2014,
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}
@misc{MOEAD2007,
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                  Michael},
  title = {Revisiting the {Arcade} {Learning} {Environment}: Evaluation
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  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
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                  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
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                  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
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}
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}
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}
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}
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}
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  year = 2004,
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}
@article{ManCar2000:fgcs,
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  month = sep,
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  volume = 1,
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}
@article{MarDeBHaeVanSnoBae07,
  author = {D. Martens and M. De Backer and R. Haesen and
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  address = { Heidelberg },
  publisher = {Springer},
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  author = { Marie-El{\'e}onore Marmion  and  Dhaenens, Clarisse  and  Laetitia Jourdan  and  Arnaud Liefooghe  and  Verel, S{\'e}bastien },
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}
@article{MarMarWeiWol2002,
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  author = { Marie-El{\'e}onore Marmion  and  Franco Mascia  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Design of Hybrid Stochastic Local Search
                  Algorithms},
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  doi = {10.1007/978-3-642-38516-2_12}
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                  classification and function approximation},
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}
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                  Objective Optimization Problems},
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}
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@article{MarOttFel91:cs,
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  publisher = {Max-Planck-Institut f{\"{u}}r Informatik, Saarbr\"ucken, Germany},
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}
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  address = { Heidelberg },
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  series = {Lecture Notes in Computer Science},
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                  D. Smith and David Corne and Martin J. Oates and Emma Hart
                  and Pier Luca Lanzi and Egbert J. W. Boers and Ben Paechter
                  and Terence C. Fogarty},
  author = { Elena Marchiori  and Adri G. Steenbeek},
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                  Problems with Application to Airline Crew Scheduling},
  pages = {367--381}
}
@book{MarStu98:cp,
  author = {K. Marriott and P. Stuckey},
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  year = 1998
}
@article{MarTot1990dam,
  author = { Silvano Martello  and  Paolo Toth },
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}
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  author = { Silvano Martello  and  Paolo Toth },
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  publisher = {John Wiley \& Sons},
  address = { Chichester, UK},
  year = 1990,
  keywords = {bin packing}
}
@article{MarVig1998exact,
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  publisher = {{INFORMS}},
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}
@mastersthesis{Maron1994hoeffding,
  title = {{Hoeffding} Races: {Model} selection for {MRI}
                  classification},
  author = {Maron, Oded},
  year = 1994,
  school = {Massachusetts Institute of Technology}
}
@incollection{MasBirStu2013lion,
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  author = { Franco Mascia  and  Mauro Birattari  and  Thomas St{\"u}tzle },
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  pages = {410--422},
  doi = {10.1007/978-3-642-44973-4_44}
}
@incollection{MasDevHen2012,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 7298,
  editor = {Nicolas Beldiceanu and Narendra Jussien and Eric Pinson},
  isbn = {978-3-642-29827-1},
  year = 2012,
  booktitle = {Integration of AI and OR Techniques in Contraint Programming
                  for Combinatorial Optimization Problems},
  author = { Florence Massen  and  Yves Deville  and van Hentenryck, Pascal },
  title = {Pheromone-Based Heuristic Column Generation for Vehicle
                  Routing Problems with Black Box Feasibility},
  pages = {260--274},
  doi = {10.1007/978-3-642-29828-8_17}
}
@misc{MasLopDub2014-supp,
  title = {Grammar-based generation of stochastic local search
                  heuristics through automatic algorithm configuration
                  tools: Supplementary material},
  author = { Franco Mascia  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  J{\'e}r{\'e}mie Dubois-Lacoste  and  Thomas St{\"u}tzle },
  year = 2013,
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}
@incollection{MasLopDub2014hm,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 8457,
  isbn = {978-3-319-07643-0},
  editor = { Mar{\'i}a J. Blesa  and  Christian Blum  and  Stefan Vo{\ss} },
  year = 2014,
  booktitle = {Hybrid Metaheuristics},
  author = { Franco Mascia  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  J{\'e}r{\'e}mie Dubois-Lacoste  and  Marie-El{\'e}onore Marmion  and  Thomas St{\"u}tzle },
  title = {Algorithm Comparison by Automatically Configurable Stochastic
                  Local Search Frameworks: A Case Study Using Flow-Shop
                  Scheduling Problems},
  pages = {30--44},
  doi = {10.1007/978-3-319-07644-7_3}
}
@incollection{MasLopDubStu2013lion,
  address = { Heidelberg },
  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 = { 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 = {From Grammars to Parameters: Automatic Iterated
                  Greedy Design for the Permutation Flow-shop Problem
                  with Weighted Tardiness},
  pages = {321--334},
  doi = {10.1007/978-3-642-44973-4_36}
}
@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}
}
@incollection{MasLopStu2013hm,
  address = { Heidelberg },
  publisher = {Springer},
  volume = 7919,
  series = {Lecture Notes in Computer Science},
  editor = { Mar{\'i}a J. Blesa  and  Christian Blum  and Paola Festa and  Andrea Roli  and  M. Sampels },
  isbn = {978-3-642-38515-5},
  year = 2013,
  booktitle = {Hybrid Metaheuristics},
  author = { Florence Massen  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle  and  Yves Deville },
  title = {Experimental Analysis of Pheromone-Based Heuristic
                  Column Generation Using irace},
  pages = {92--106},
  doi = {10.1007/978-3-642-38516-2_8}
}
@inproceedings{MasNesDor2020,
  year = 2020,
  editor = {Vlastelica, Marin and Song, Jialin and Ferber, Aaron and Amos, Brandon and
                  Martius, Georg and Dilkina, Bistra and Yue, Yisong},
  booktitle = {Learning Meets Combinatorial Algorithms Workshop at NeurIPS 2020,
                  {LMCA} 2020, Vancouver, Canada, December 12, 2020},
  author = {Massobrio, Renzo and Nesmachnow, Sergio and Dorronsoro, Bernab{\'e}},
  title = {Virtual Savant: learning for optimization},
  pages = {1--5}
}
@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}
}
@incollection{MauLopStu2010:cec,
  year = 2010,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  booktitle = {Proceedings of  the 2010 Congress on Evolutionary Computation (CEC 2010)},
  editor = { Ishibuchi, Hisao  and others},
  key = {IEEE CEC},
  author = { Michael Maur  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Pre-scheduled and adaptive parameter variation in
                  {\MaxMinAntSystem}},
  pages = {3823--3830},
  doi = {10.1109/CEC.2010.5586332}
}
@incollection{MazChuMietLop2019emo,
  isbn = {978-3-030-12597-4},
  year = 2019,
  address = { Cham, Switzerland },
  publisher = {Springer International Publishing},
  volume = 11411,
  series = {Lecture Notes in Computer Science},
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2019},
  editor = { Kalyanmoy Deb  and Erik D. Goodman and  Carlos A. {Coello Coello}  and Kathrin
                  Klamroth and  Kaisa Miettinen  and Sanaz Mostaghim and Patrick
                  Reed},
  author = { Atanu Mazumdar  and  Tinkle Chugh  and  Kaisa Miettinen  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {On Dealing with Uncertainties from Kriging Models in Offline
                  Data-Driven Evolutionary Multiobjective Optimization},
  pages = {463--474},
  doi = {10.1007/978-3-030-12598-1_37}
}
@article{McConMehNah2011certifying,
  title = {Certifying algorithms},
  journal = {Computer Science Review},
  volume = 5,
  number = 2,
  pages = {119--161},
  year = 2011,
  issn = {1574-0137},
  doi = {10.1016/j.cosrev.2010.09.009},
  author = {Ross M. McConnell and Kurt Mehlhorn and Stefan N{\"a}her and
                  Pascal Schweitzer},
  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},
  eprint = {http://link.aip.org/link/?QWR/129/372/1},
  doi = {10.1061/(ASCE)0733-9496(2003)129:5(372)}
}
@incollection{McCormick03,
  publisher = {CRC Press},
  year = 2003,
  booktitle = {Advances in Water Supply Management},
  editor = { C. Maksimovi{\'c}  and  David Butler  and  Fayyaz Ali Memon },
  author = { G. McCormick  and  R. S. Powell },
  title = {A progressive mixed integer-programming method for
                  pump scheduling},
  pages = {307--313}
}
@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},
  doi = {10.1057/palgrave.jors.2601718},
  month = jul,
  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}
}
@book{McG2012guide,
  author = { Catherine C. McGeoch },
  title = {A Guide to Experimental Algorithmics},
  year = 2012,
  publisher = {Cambridge University Press}
}
@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}
}
@misc{MeiCla2014prep,
  title = {A versatile heuristic approach for generalized hub location
                  problems},
  author = {J. Fabian Meier and Uwe Clausen},
  howpublished = {Preprint, Provided upon personal request},
  year = 2014,
  keyword = {irace}
}
@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}
}
@incollection{MelPerCos2009:ea,
  address = { Heidelberg },
  publisher = {Springer},
  editor = {Pierre Collet and Nicolas Monmarch{\'e} and Pierrick
                  Legrand and Marc Schoenauer and Evelyne Lutton},
  shorteditor = {Pierre Collet and others},
  volume = 5975,
  series = {Lecture Notes in Computer Science},
  year = 2010,
  booktitle = {Artificial Evolution: 9th International Conference, Evolution Artificielle, EA, 2009},
  title = {{MC-ANT}: a Multi-colony Ant Algorithm},
  author = {Melo, L. and Pereira, F. and Costa, E.}
}
@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
}
@incollection{MenCoe2015gd,
  editor = { Ant{\'o}nio Gaspar{-}Cunha  and Carlos Henggeler Antunes and  Carlos A. {Coello Coello} },
  volume = 9018,
  year = 2015,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2015 Part {I}},
  author = {Menchaca-Mendez, Adriana and  Carlos A. {Coello Coello} },
  title = {{GD-MOEA}: A New Multi-Objective Evolutionary Algorithm Based
                  on the Generational Distance Indicator},
  pages = {156--170}
}
@inproceedings{MenCoe2015gde,
  year = 2015,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  booktitle = {Proceedings of  the 2015 Congress on Evolutionary Computation (CEC 2015)},
  key = {IEEE CEC},
  author = {Menchaca-Mendez, Adriana and  Carlos A. {Coello Coello} },
  title = {{GDE-MOEA}: A New {MOEA} based on the generational distance
                  indicator and $\epsilon$-dominance},
  pages = {947--955}
}
@inproceedings{MenKleFeuSprHut2016autoNN,
  title = {Towards automatically-tuned neural networks},
  author = {Mendoza, Hector and Klein, Aaron and Feurer, Matthias and
                  Springenberg, Jost Tobias and  Frank Hutter },
  booktitle = {Workshop on Automatic Machine Learning},
  pages = {58--65},
  year = 2016
}
@incollection{MerBisTraPreuWeiRud11:gecco,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2011,
  editor = {Natalio Krasnogor and Pier Luca Lanzi},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2011},
  author = { Olaf Mersmann  and  Bernd Bischl  and  Heike Trautmann  and  Mike Preuss  and  Claus Weihs  and  G{\"u}nther Rudolph },
  title = {Exploratory Landscape Analysis},
  pages = {829--836},
  keywords = {continuous optimization, landscape analysis, instance features}
}
@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}
}
@incollection{MerHuh2008:ppsn,
  year = 2008,
  volume = 5199,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  editor = { G{\"u}nther Rudolph  and others},
  aeditor = { G{\"u}nther Rudolph  and Thomas Jansen and Simon Lucas and
                  Carlo Poloni and Nicola Beume},
  booktitle = {Parallel Problem Solving from Nature, PPSN X},
  author = { Peter Merz  and Jutta Huhse},
  title = {An Iterated Local Search Approach for Finding Provably Good Solutions
  for Very Large {TSP} Instances},
  pages = {929--939}
}
@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}
}
@incollection{MerMid01,
  author = { D. Merkle  and  Martin Middendorf },
  title = {Prospects for Dynamic Algorithm Control: Lessons
                  from the Phase Structure of Ant Scheduling
                  Algorithms},
  booktitle = {Proceedings of the 2001 Genetic and Evolutionary
                  Computation Conference -- Workshop Program. Workshop
                  ``The Next Ten Years of Scheduling Research''},
  pages = {121--126},
  year = 2001,
  editor = {R. B. Heckendorn},
  address = {San Francisco, CA},
  publisher = {Morgan Kaufmann Publishers}
}
@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
}
@incollection{MerMidSch00:gecco,
  publisher = {Morgan Kaufmann Publishers, San Francisco, CA},
  editor = { Darrell Whitley  and others},
  fulleditor = { Darrell Whitley  and  David E. Goldberg  and E. Cantu-Paz and L. Spector and
                  I. Parmee and   Hans-Georg Beyer },
  year = 2000,
  booktitle = {Proceedings of the Genetic and Evolutionary Computation
                  Conference, GECCO 2000},
  author = { D. Merkle  and  Martin Middendorf  and  Hartmut Schmeck },
  title = {Ant Colony Optimization for Resource-Constrained Project Scheduling},
  pages = {893--900}
}
@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}
}
@inproceedings{MerTraNau2010cec,
  year = 2010,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  booktitle = {Proceedings of  the 2010 Congress on Evolutionary Computation (CEC 2010)},
  editor = { Ishibuchi, Hisao  and others},
  key = {IEEE CEC},
  author = { Olaf Mersmann  and  Heike Trautmann  and  Boris Naujoks  and  Claus Weihs },
  title = {Benchmarking Evolutionary Multiobjective Optimization Algorithms},
  pages = {1--8},
  annote = {TR: \url{http://hdl.handle.net/2003/26671}}
}
@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
}
@inproceedings{Mey2004:gecco,
  author = { Bernd Meyer },
  title = {Convergence control in {ACO}},
  note = {Late-breaking paper available on CD},
  year = 2004,
  booktitle = {Genetic and Evolutionary Computation Conference
                  (GECCO)},
  address = {Seattle, WA}
}
@incollection{MeyErn04:ants,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 3172,
  editor = { Marco Dorigo  and others },
  fulleditor = { Marco Dorigo  and  L. M. Gambardella  and  Francesco Mondada  and  Thomas St{\"u}tzle  and  Mauro Birattari  and  Christian Blum },
  booktitle = {Ant Colony Optimization and Swarm Intelligence, 4th
                  International Workshop, ANTS 2004 },
  author = { Bernd Meyer  and  Andreas T. Ernst },
  title = {Integrating {ACO} and Constraint Propagation},
  pages = {166--177},
  year = 2004
}
@incollection{MezReyCoe2008,
  address = { Heidelberg },
  publisher = {Springer},
  editor = {Uday K. Chakraborty},
  year = 2008,
  booktitle = {Advances in differential evolution},
  author = { Efr{\'e}n Mezura-Montes  and Reyes-Sierra, M. and  Carlos A. {Coello Coello} },
  title = {Multi-objective optimization using differential
                  evolution: a survey of the state-of-the-art},
  pages = {173--196},
  doi = {10.1007/978-3-540-68830-3_7}
}
@incollection{MezVelCoe2006,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2006,
  editor = {M. Cattolico and others},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2006},
  title = {A comparative study of differential evolution variants for
                  global optimization},
  author = { Efr{\'e}n Mezura-Montes  and Vel{\'a}zquez-Reyes, Jes{\'u}s and  Carlos A. {Coello Coello} },
  pages = {485--492},
  doi = {10.1145/1143997.1144086}
}
@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}
}
@book{MicFog04:howtosolveit,
  author = { Zbigniew Michalewicz  and  David B. Fogel },
  title = {How to Solve It: Modern Heuristics},
  publisher = {Springer},
  year = 2004,
  edition = {2nd}
}
@inproceedings{MicHen2004,
  publisher = {{AAAI} Press\slash {MIT} Press, Menlo Park, CA },
  year = 2004,
  editor = { Shlomo Zilberstein  and J. Koehler and S. Koenig},
  booktitle = {Proceedings of the Fourteenth International Conference on
                  Automated Planning and Scheduling (ICAPS 2004)},
  author = { Laurent D. Michel  and van Hentenryck, Pascal },
  title = {Iterative Relaxations for Iterative Flattening in Cumulative
                  Scheduling},
  pages = {200--208}
}
@incollection{MicMid98,
  address = { Heidelberg },
  publisher = {Springer},
  editor = { Agoston E. Eiben  and  Thomas B{\"a}ck  and  Marc Schoenauer  and  Hans-Paul Schwefel },
  volume = 1498,
  series = {Lecture Notes in Computer Science},
  year = 1998,
  booktitle = {Parallel Problem Solving from Nature -- PPSN V},
  author = {R. Michel and M. Middendorf},
  title = {An Island Model based {Ant} {System} with Lookahead
                  for the Shortest Supersequence Problem},
  pages = {692--701}
}
@article{MicPriAmoYal2014:cor,
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@incollection{MieRuiWie08interactive,
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  address = { Heidelberg },
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  year = 2008,
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                  Approaches},
  author = { Kaisa Miettinen  and  Francisco Ruiz  and  Andrzej Wierzbicki },
  title = {Introduction to Multiobjective Optimization:
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  doi = {10.1007/978-3-540-88908-3_2},
  abstract = {We give an overview of interactive methods developed
                  for solving nonlinear multiobjective optimization
                  problems. In interactive methods, a decision maker
                  plays an important part and the idea is to support
                  her/him in the search for the most preferred
                  solution. In interactive methods, steps of an
                  iterative solution algorithm are repeated and the
                  decision maker progressively provides preference
                  information so that the most preferred solution can
                  be found. We identify three types of specifying
                  preference information in interactive methods and
                  give some examples of methods representing each
                  type. The types are methods based on trade-off
                  information, reference points and classification of
                  objective functions.}
}
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  author = { Misevi{\v{c}}ius, Alfonsas },
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                  Assignment Problem},
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}
@article{Misevicius2003:inf,
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  author = { Misevi{\v{c}}ius, Alfonsas  and Kuznecovait{\.e}, Dovil{\.e} and
                  J{\={u}}rat{\.e} Platu{\v{z}}ien{\.e}},
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                  Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K.
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}
@incollection{MniSzeAud2008,
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  editor = {William W. Cohen and Andrew McCallum and Sam T. Roweis},
  title = {Empirical {Bernstein} stopping},
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                  Jean-Yves},
  pages = {672--679}
}
@article{MocTieZil1978,
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}
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}
@book{Mockus1989,
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}
@incollection{MogAteYalAmo11:lorenz,
  address = { Heidelberg },
  publisher = {Springer},
  year = 2011,
  series = {Lecture Notes in Computer Science},
  volume = 6576,
  editor = { Takahashi, R. H. C.  and others},
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2011},
  title = {{Lorenz} versus {Pareto} Dominance in a Single Machine
                  Scheduling Problem with Rejection},
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                  Lionel},
  pages = {520--534}
}
@inproceedings{MolTeo2012safe,
  year = 2012,
  publisher = {Omnipress},
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  editor = {John Langford and Joelle Pineau},
  author = {Moldovan, Teodor Mihai and Abbeel, Pieter},
  title = {Safe Exploration in {Markov} Decision Processes},
  pages = {1451--1458},
  numpages = 8,
  eprint = {http://icml.cc/2012/papers/838.pdf}
}
@article{MonAydStu2011soco,
  author = { Marco A. {Montes de Oca}  and  Do\v{g}an Ayd{\i}n  and  Thomas St{\"u}tzle },
  title = {An Incremental Particle Swarm for Large-Scale
                  Continuous Optimization Problems: An Example of
                  Tuning-in-the-loop (Re)Design of Optimization
                  Algorithms},
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  year = 2011,
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}
@incollection{MonDevHen2009,
  author = {Jean-No\"el Monette and  Yves Deville  and van Hentenryck, Pascal },
  title = {Aeon: Synthesizing Scheduling Algorithms from High-Level Models},
  booktitle = {Operations Research and Cyber-Infrastructure},
  publisher = {Springer},
  year = 2009,
  editor = {John W. Chinneck and Bjarni Kristjansson and Matthew J. Saltzman},
  volume = 47,
  series = {Operations Research/Computer Science Interfaces},
  pages = {43--59},
  address = { New York, NY }
}
@article{MonFraLiu2018bridge,
  title = {Multi-criteria robust optimization framework for bridge
                  adaptation under climate change},
  author = {Mondoro, Alysson and Frangopol, Dan M. and Liu, Liang},
  journal = {Structural Safety},
  volume = 74,
  pages = {14--23},
  year = 2018
}
@article{MonGamRiz2005,
  author = { Roberto Montemanni  and  L. M. Gambardella  and A. E. Rizzoli and
                  A. V. Donati},
  title = {Ant colony system for a dynamic vehicle routing problem},
  journal = {Journal of Combinatorial Optimization},
  year = 2005,
  volume = 10,
  pages = {327--343},
  alias = {Mon++05}
}
@incollection{MonPerRiffCoe12dummy,
  volume = 7491,
  year = 2012,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { Carlos A. {Coello Coello}  and others},
  booktitle = {Parallel Problem Solving from Nature, PPSN XII},
  author = { Elizabeth Montero  and   P{\'e}rez C{\'a}ceres, Leslie and  Mar{\'i}a-Cristina Riff  and  Carlos A. {Coello Coello} },
  title = {Are State-of-the-Art Fine-Tuning Algorithms Able to
                  Detect a Dummy Parameter?},
  pages = {306--315},
  doi = {10.1007/978-3-642-32937-1_31}
}
@article{MonRandHen08:bias,
  author = { James Montgomery  and  Marcus Randall  and  Tim Hendtlass },
  title = {Solution bias in ant colony optimisation: {Lessons}
                  for selecting pheromone models},
  journal = {Computers \& Operations Research},
  volume = 35,
  number = 9,
  year = 2008,
  pages = {2728--2749},
  doi = {10.1016/j.cor.2006.12.014}
}
@incollection{MonRif2013gecco,
  year = 2013,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  booktitle = {Proceedings of  the 2013 Congress on Evolutionary Computation (CEC 2013)},
  key = {IEEE CEC},
  title = {A new algorithm for reducing metaheuristic design effort},
  author = { Mar{\'i}a-Cristina Riff  and  Elizabeth Montero },
  pages = {3283--3290},
  doi = {10.1109/CEC.2013.6557972}
}
@incollection{MonRif2014ppsn,
  year = 2014,
  volume = 8672,
  address = { Heidelberg },
  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 = {PPSN 2014},
  author = { Elizabeth Montero  and  Mar{\'i}a-Cristina Riff },
  title = {Towards a Method for Automatic Algorithm Configuration: A Design Evaluation Using Tuners},
  pages = {90--99},
  doi = {10.1007/978-3-319-10762-2_9}
}
@inproceedings{MonRifNev2010,
  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 = { Elizabeth Montero  and  Mar{\'i}a-Cristina Riff  and Neveu, Bertrand},
  title = {An Evaluation of Off-line Calibration Techniques for
                  Evolutionary Algorithms},
  pages = {299--300}
}
@article{MonRifNev2014asoc,
  author = { Elizabeth Montero  and  Mar{\'i}a-Cristina Riff  and Neveu, Bertrand},
  title = {A Beginner's Buide to Tuning Methods},
  journal = {Applied Soft Computing},
  volume = 17,
  pages = {39--51},
  year = 2014,
  doi = {10.1016/j.asoc.2013.12.017}
}
@article{MonStuBirDor2009tec,
  author = { Marco A. {Montes de Oca}  and  Thomas St{\"u}tzle  and  Mauro Birattari  and  Marco Dorigo },
  title = {Frankenstein's {PSO}: A Composite Particle Swarm
                  Optimization Algorithm},
  journal = {IEEE Transactions on Evolutionary Computation},
  volume = 13,
  number = 5,
  year = 2009,
  pages = {1120--1132},
  doi = {10.1109/TEVC.2009.2021465}
}
@article{MonVenSli2000:fgcs,
  author = { Nicolas Monmarch{\'e}  and G. Venturini and M. Slimane},
  title = {On how {\em pachycondyla apicalis} ants suggest a
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  journal = {Future Generation Computer Systems},
  year = 2000,
  volume = 16,
  number = 8,
  pages = {937--946}
}
@incollection{MonYos11,
  address = { Heidelberg },
  publisher = {Springer},
  year = 2011,
  series = {Lecture Notes in Computer Science},
  volume = 6576,
  editor = { Takahashi, R. H. C.  and others},
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2011},
  title = {A Framework for Locating Logistic Facilities with
                  Multi-Criteria Decision Analysis},
  author = {Montibeller, Gilberto and Yoshizaki, Hugo},
  pages = {505--519}
}
@phdthesis{Montes2011PhD,
  author = { Marco A. {Montes de Oca} },
  title = {Incremental Social Learning in Swarm Intelligence Systems},
  school = {IRIDIA, {\'E}cole polytechnique, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2011,
  annote = {Supervised by Marco Dorigo}
}
@phdthesis{Montgomery2005phd,
  author = { James Montgomery },
  title = {Solution Biases and Pheromone Representation
                  Selection in Ant Colony Optimisation},
  school = {School of Information Technology, Bond University},
  year = 2005,
  address = {Australia},
  alias = {montgomery05:phd}
}
@book{Montgomery2012,
  author = {Douglas C. Montgomery},
  title = {Design and Analysis of Experiments},
  publisher = {John Wiley \& Sons},
  year = 2012,
  edition = {8th},
  address = { New York, NY }
}
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  editor = {William W. Cohen and Haym Hirsh},
  author = {Moore, Andrew W. and Lee, Mary S.},
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  pages = {190--198}
}
@article{Mor1993sim,
  title = {Simulation of an adaptive behavior mechanism in an
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}
@article{Mor80,
  title = {Reducing the size of the nondominated set: Pruning
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@article{MorGagGra09:ejor,
  author = {Sara Morin and  Caroline Gagn{\'e}  and Marc Gravel},
  title = {Ant colony optimization with a specialized pheromone
                  trail for the car-sequencing problem},
  volume = 197,
  doi = {10.1016/j.ejor.2008.03.033},
  abstract = { This paper studies the learning process in an ant
                  colony optimization algorithm designed to solve the
                  problem of ordering cars on an assembly line
                  (car-sequencing problem). This problem has been
                  shown to be {NP-hard} and evokes a great deal of
                  interest among practitioners. Learning in an ant
                  algorithm is achieved by using an artificial
                  pheromone trail, which is a central element of this
                  metaheuristic. Many versions of the algorithm are
                  found in literature, the main distinction among them
                  being the management of the pheromone
                  trail. Nevertheless, few of them seek to perfect
                  learning by modifying the internal structure of the
                  trail. In this paper, a new pheromone trail
                  structure is proposed that is specifically adapted
                  to the type of constraints in the car-sequencing
                  problem. The quality of the results obtained when
                  solving three sets of benchmark problems is superior
                  to that of the best solutions found in literature
                  and shows the efficiency of the specialized trail.},
  number = 3,
  journal = {European Journal of Operational Research},
  year = 2009,
  keywords = {Ant colony {optimization,Car-sequencing}
                  {problem,Pheromone} {trail,Scheduling}},
  pages = {1185--1191}
}
@incollection{MorKat2011:evo,
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  publisher = {Springer},
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  year = 2011,
  editor = { Peter Merz  and  Jin-Kao Hao },
  booktitle = {Proceedings of EvoCOP 2011 -- 11th European Conference on Evolutionary Computation in Combinatorial Optimization },
  author = { A. Moraglio  and  A. Kattan },
  title = {Geometric Generalisation of Surrogate Model Based
                  Optimization to Combinatorial Spaces},
  pages = {142--154}
}
@incollection{MorKat2011:gecco,
  year = 2011,
  address = { New York, NY },
  publisher = {ACM Press},
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  editor = {Natalio Krasnogor and Pier Luca Lanzi},
  author = { A. Moraglio  and Yong{-}Hyuk Kim and Yourim Yoon},
  title = {Geometric Surrogate-based Optimisation for Permutation-based
                  Problems},
  pages = {133--134}
}
@article{MorMerLarMilTor09,
  author = {Mora, A. M. and  Juan-Juli{\'a}n Merelo  and  Jim{\'e}nez Laredo, Juan Luis  and Millan, C. and Torrecillas, J.},
  title = {{CHAC}, a {MOACO} algorithm for computation of bi-criteria
                  military unit path in the battlefield: Presentation and first
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  volume = 24,
  number = 7,
  publisher = {Wiley Subscription Services, Inc., A Wiley Company},
  pages = {818--843},
  year = 2009
}
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}
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  author = {Pail Morris},
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  pages = {40--45}
}
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}
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                  application, such as when the data are preexisting. Services
                  are now available for preregistration across all disciplines,
                  facilitating a rapid increase in the practice. Widespread
                  adoption of preregistration will increase distinctiveness
                  between hypothesis generation and hypothesis testing and will
                  improve the credibility of research findings.}
}
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  publisher = {Springer},
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}
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}
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}
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                  Nugent, Conor and O'Sullivan, Barry},
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}
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}
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                  A. Vazquez-Rodriguez and James Walker and  Michel Gendreau  and  Graham Kendall  and  Barry McCollum  and  Andrew J. Parkes  and Sanja Petrovic and  Edmund K. Burke },
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}
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}
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  publisher = {Springer}
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}
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                  Sciences (INFORMS), Linthicum, Maryland, USA}
}
@incollection{OjaPodMie2016ppsn,
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  volume = 9921,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { Julia Handl  and  Emma Hart  and  Lewis, P. R.  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Gabriela Ochoa  and  Ben Paechter },
  booktitle = {Parallel Problem Solving from Nature -- PPSN XIV},
  author = {Vesa Ojalehto and Dmitry Podkopaev and  Kaisa Miettinen },
  title = {Towards Automatic Testing of Reference Point Based
                  Interactive Methods},
  pages = {483--492},
  doi = {10.1007/978-3-319-45823-6_45},
  keywords = {artificial DMs},
  annote = {In this research, we proposed to build an automated framework
                  for testing interactive multiobjective optimization methods,
                  without utilizing a value function to represent the DM's
                  preferences. This was achieved by replacing the human DM with
                  an artificial DM constructed from two distinct parts: the
                  steady part and the current context. With the steady part the
                  artificial DM tries to maintain the search towards its
                  preferences, while at the same time the current context
                  allows changing the direction as well as ending the solution
                  process prematurely, mimicking actions of a human DM.}
}
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  title = {Time complexity of evolutionary algorithms for combinatorial
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  publisher = {Springer}
}
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  publisher = {IEEE Press},
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  key = {IEEE CEC},
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  title = {Analysis of the Population-based Ant Colony Optimization Algorithm for the {TSP} and the {QAP}},
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}
@article{OliWit2015,
  author = { Oliveto, Pietro S.  and  Carsten Witt },
  title = {Improved time complexity analysis of the Simple Genetic Algorithm},
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}
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  author = {Olson, David L.},
  title = {Review of Empirical Studies in Multiobjective Mathematical
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                  aiding decision makers facing complex decisions where
                  trade-offs among conflicting objectives must be
                  reconciled. Interactive multiobjective programming provides a
                  means for decision makers to learn what these trade-offs
                  involve, while the mathematical program generates solutions
                  that seek improvement of the implied utility of the decision
                  maker. A variety of multiobjective programming techniques
                  have been presented in the multicriteria decision-making
                  literature. This study reviews published studies with human
                  subjects where some of these techniques were applied. While
                  all of the techniques have the ability to support decision
                  makers under conditions of multiple objectives, a number of
                  features in applying these systems have been tested by these
                  studies. A general evolution of techniques is traced,
                  starting with methods relying upon linear combinations of
                  value, to more recent methods capable of reflecting nonlinear
                  trade-offs of value. Support of nonlinear utility and
                  enhancing decision-maker learning are considered.}
}
@incollection{OlsBarUrb2016gecco,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2016,
  editor = { Tobias Friedrich  and  Frank Neumann  and  Andrew M. Sutton },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2016},
  author = {Olson, Randal S. and Bartley, Nathan and Urbanowicz, Ryan
                  J. and Moore, Jason H.},
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                  Automating Data Science},
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  numpages = 8,
  doi = {10.1145/2908812.2908918},
  acmid = 2908918
}
@article{OlsLok2013joh,
  author = {Roland Olsson and  Arne L{\o}kketangen },
  title = {Using automatic programming to generate
                  state-of-the-art algorithms for random 3-{SAT}},
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}
@incollection{OlsUrbAnd2016evobio,
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  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
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  year = 2016,
  editor = {Squillero, Giovanni and Burelli, Paolo},
  author = {Olson, Randal S. and Urbanowicz, Ryan J. and Andrews, Peter
                  C. and Lavender, Nicole A. and Kidd, La Creis and Moore,
                  Jason H.},
  title = {Automating Biomedical Data Science Through Tree-Based
                  Pipeline Optimization},
  pages = {123--137},
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}
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}
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  year = 2001
}
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}
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}
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  pages = {165--182},
  year = 2002,
  publisher = {Springer}
}
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  year = 2004,
  editor = {Gerald Sehlke and Donald F. Hayes and David
                  K. Stevens},
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                  EPANET for simultaneously optimizing the scheduling
                  of existing pumping and booster disinfection units,
                  as well as the design of new disinfection booster
                  chlorination stations, under unsteady
                  hydraulics. The objective is to minimize the total
                  cost of operating the pumping units and the chlorine
                  booster operation and design for a selected
                  operational time horizon, while delivering the
                  consumers required water quantities, at acceptable
                  pressures and chlorine residual concentrations. The
                  decision variables, for each of the time steps that
                  encompass the total operational time horizon,
                  include: the scheduling of the pumping units,
                  settings of the water distribution system control
                  valves, and the mass injection rates at each of the
                  booster chlorination stations. The constraints are
                  domain heads and chlorine concentrations at the
                  consumer nodes, maximum injection rates at the
                  chlorine injection stations, maximum allowable
                  amounts of water withdraws at the sources, and
                  returning at the end of the operational time horizon
                  to a prescribed total volume in the tanks. The model
                  is demonstrated through an example application.}
}
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  year = 2005,
  publisher = {Springer},
  booktitle = {Multiple Criteria Decision Analysis, State of the
                  Art Surveys},
  author = { Meltem {\"O}zt{\"u}rk  and  Alexis Tsouki{\`a}s  and  Philippe Vincke },
  title = {Preference Modelling},
  chapter = 2,
  pages = {27--72}
}
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@article{PagStu2016,
  author = { Federico Pagnozzi  and  Thomas St{\"u}tzle },
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  year = 2017,
  volume = 11,
  pages = {1283--1292},
  doi = {10.1007/s11590-016-1086-5}
}
@techreport{PagStu2018:pfsp,
  author = { Federico Pagnozzi  and  Thomas St{\"u}tzle },
  title = {Automatic Design of Hybrid Stochastic Local Search Algorithms
                 for Permutation Flowshop Problems},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  number = {TR/IRIDIA/2018-005},
  year = 2018,
  month = apr,
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}
@misc{PagStu2018:pfsp-supp,
  author = { Federico Pagnozzi  and  Thomas St{\"u}tzle },
  title = {Automatic Design of Hybrid Stochastic Local Search Algorithms
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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},
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  volume = 276,
  issue = 2,
  pages = {409--421},
  doi = {10.1016/j.ejor.2019.01.018}
}
@misc{PagStu2019:pfsp-supp,
  author = { Federico Pagnozzi  and  Thomas St{\"u}tzle },
  title = {Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems with additional constraints},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2018-002/}},
  year = 2019
}
@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},
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  year = 2021,
  volume = 8,
  doi = {10.1016/j.orp.2021.100180}
}
@phdthesis{Pagnozzi2019PhD,
  author = { Federico Pagnozzi },
  title = {Automatic Design of Hybrid Stochastic Local Search Algorithms},
  school = {IRIDIA, {\'E}cole polytechnique, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2019,
  annote = {Supervised by Thomas St\"utzle}
}
@article{PalGooSor2014,
  author = {Daniel {Palhazi Cuervo} and Peter Goos and  Kenneth S{\"o}rensen  and Emely Arr{\'{a}}iz},
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  pages = {454--464}
}
@article{Palubeckis2006,
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}
@article{PanRui2012ejor,
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  author = {Pan, Quan-Ke and  Rub{\'e}n Ruiz },
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  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 },
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  volume = 80,
  pages = {50--60}
}
@article{PanTasLia2008,
  title = {A Discrete Differential Evolution Algorithm for the
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  year = 2008,
  author = {Quan-Ke Pan and Mehmet Fatih Tasgetiren and Yun-Chia
                  Liang}
}
@article{PanWanZha2008,
  year = 2008,
  journal = {International Journal of Advanced Manufacturing Technology},
  volume = 38,
  number = {7-8},
  title = {An improved iterated greedy algorithm for the
                  no-wait flow shop scheduling problem with makespan
                  criterion},
  author = {Pan, Quan-Ke and Wang, Ling and Zhao, Bao-Hua},
  pages = {778--786}
}
@article{PanYang2009,
  title = {A survey on transfer learning},
  author = {Pan, Sinno Jialin and Yang, Qiang},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  volume = 22,
  number = 10,
  pages = {1345--1359},
  year = 2009
}
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  author = { Christos H. Papadimitriou  and Steiglitz, K.},
  title = {Combinatorial Optimization -- Algorithms and
                  Complexity},
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  year = 1982
}
@inproceedings{PapYan2000focs,
  publisher = {IEEE Computer Society Press},
  year = 2000,
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  editor = {Avrim Blum},
  author = { Christos H. Papadimitriou  and  M. Yannakakis },
  title = {On the Approximability of Trade-offs and Optimal
                  Access of Web Sources},
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  doi = {10.1109/SFCS.2000.892068}
}
@mastersthesis{Paq2001:Msc,
  author = { Lu{\'i}s Paquete },
  title = {Algoritmos Evolutivos Multiobjectivo para
                  Afecta\c{c}\~ao de Recursos e sua Aplica\c{c}\~ao
                  \`a Gera\c{c}\~ao de Hor\'arios em Universidades
                  ({Multiobjective} Evolutionary Algorithms for
                  Resource Allocation and their Application to
                  University Timetabling)},
  school = {University of Algarve},
  year = 2001,
  note = {In Portuguese},
  abstract = {The aim of this study is the application of
                  multiobjective evolutionary algorithms to resource
                  allocation problems, such as university examination
                  timetabling and course timetabling
                  problems. Usually, these problems are characterized
                  by multiple conflicting objectives. A multiobjective
                  formalization of these problems is presented, based
                  on goals and priorities. Various aspects of
                  evolutionary algorithms are proposed and studied for
                  these problems, particulary, selection methods and
                  types and parameters of mutation operator. The
                  choice of both representation and operators is made
                  so as not to favour excessively certain objectives
                  with respect to others at the level of the
                  exploration mechanism. A comparative study of
                  performance is presented for the proposed algorithms
                  by means of statistical inference, based on real
                  problems of the University of Algarve. The notion of
                  attainment functions is used as a base for the
                  assessment of performance of multiobjective
                  evolutionary algorithms. Finally, the evolution of
                  the solution cost during the runs is analysed by
                  means of attainment functions, as well.}
}
@phdthesis{Paq2005:PhD,
  author = { Lu{\'i}s Paquete },
  title = {Stochastic Local Search Algorithms for
                  Multiobjective Combinatorial Optimization: Methods
                  and Analysis},
  school = {FB Informatik, TU Darmstadt, Germany},
  year = 2005
}
@incollection{PaqChiStu2004mmo,
  year = 2004,
  address = { Berlin, Germany },
  publisher = {Springer},
  volume = 535,
  series = {Lecture Notes in Economics and Mathematical Systems},
  editor = { Xavier Gandibleux  and Marc Sevaux and  Kenneth S{\"o}rensen  and  V. {T'Kindt} },
  booktitle = {Metaheuristics for Multiobjective Optimisation},
  author = { Lu{\'i}s Paquete  and  Marco Chiarandini  and  Thomas St{\"u}tzle },
  title = {{Pareto} Local Optimum Sets in the Biobjective Traveling
                  Salesman Problem: An Experimental Study},
  pages = {177--199},
  alias = {lpaquete:10},
  abstract = {In this article, we study {Pareto} local optimum sets for the
                  biobjective Traveling Salesman Problem applying
                  straightforward extensions of local search algorithms for the
                  single objective case. The performance of the local search
                  algorithms is illustrated by experimental results obtained
                  for well known benchmark instances and comparisons to methods
                  from literature.  In fact, a 3-opt local search is able to
                  compete with the best performing metaheuristics in terms of
                  solution quality. Finally, we also present an empirical study
                  of the features of the solutions found by 3-opt on a set of
                  randomly generated instances. The results indicate the
                  existence of several clusters of near-optimal solutions that
                  are separated by only a few edges.},
  keywords = {Pareto local search, PLS},
  doi = {10.1007/978-3-642-17144-4_7}
}
@techreport{PaqFonLop06-CSI-klee,
  author = { Lu{\'i}s Paquete  and  Carlos M. Fonseca  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {An optimal algorithm for a special case of {Klee}'s
                  measure problem in three dimensions},
  institution = {CSI, Universidade do Algarve},
  year = 2006,
  number = {CSI-RT-I-01/2006},
  abstract = {The measure of the region dominated by (the maxima
                  of) a set of $n$ points in the positive $d$-orthant
                  has been proposed as an indicator of performance in
                  multiobjective optimization, known as the
                  hypervolume indicator, and the problem of computing
                  it efficiently is attracting increasing
                  attention. In this report, this problem is
                  formulated as a special case of Klee's measure
                  problem in $d$ dimensions, which immediately
                  establishes $O(n^{d/2}\log n)$ as a, possibly
                  conservative, upper bound on the required
                  computation time. Then, an $O(n log n)$ algorithm
                  for the 3-dimensional version of this special case
                  is constructed, based on an existing dimension-sweep
                  algorithm for the related maxima problem. Finally,
                  $O(n log n)$ is shown to remain a lower bound on the
                  time required by the hypervolume indicator for
                  $d>1$, which attests the optimality of the algorithm
                  proposed.},
  note = {Superseded by paper in IEEE Transactions on Evolutionary Computation~\cite{BeuFonLopPaqVah09:tec}},
  annote = {Proof of Theorem 3.1 is incorrect}
}
@article{PaqSchStu07:aor,
  author = { Lu{\'i}s Paquete  and  Tommaso Schiavinotto  and  Thomas St{\"u}tzle },
  title = {On Local Optima in Multiobjective Combinatorial
                  Optimization Problems},
  journal = {Annals of Operations Research},
  year = 2007,
  volume = 156,
  pages = {83--97},
  doi = {10.1007/s10479-007-0230-0},
  keywords = {Pareto local search, PLS},
  abstract = {In this article, local optimality in multiobjective
                  combinatorial optimization is used as a baseline for
                  the design and analysis of two iterative improvement
                  algorithms. Both algorithms search in a neighborhood
                  that is defined on a collection of sets of feasible
                  solutions and their acceptance criterion is based on
                  outperformance relations. Proofs of the soundness
                  and completeness of these algorithms are given.}
}
@article{PaqStu06:mqap,
  author = { Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
  title = {A study of stochastic local search algorithms for
                  the biobjective {QAP} with correlated flow matrices},
  journal = {European Journal of Operational Research},
  year = 2006,
  volume = 169,
  number = 3,
  alias = {lpaquete:14},
  pages = {943--959}
}
@incollection{PaqStu08:lnems,
  author = { Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
  title = {Clusters of non-dominated solutions in multiobjective
                  combinatorial optimization: An experimental analysis},
  booktitle = {Multiobjective Programming and Goal Programming: Theoretical
                  Results and Practical Applications},
  pages = {69--77},
  year = 2009,
  volume = 618,
  series = {Lecture Notes in Economics and Mathematical Systems},
  editor = {V. Barichard and M. Ehrgott and  Xavier Gandibleux  and V. T'Kindt},
  publisher = {Springer},
  address = { Berlin, Germany },
  doi = {10.1007/978-3-540-85646-7}
}
@article{PaqStu09:cor,
  author = { Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
  title = {Design and analysis of stochastic local search for
                  the multiobjective traveling salesman problem},
  journal = {Computers \& Operations Research},
  year = 2009,
  volume = 36,
  number = 9,
  pages = {2619--2631},
  doi = {10.1016/j.cor.2008.11.013}
}
@incollection{PaqStu2002:evocop,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 2279,
  editor = {S. Cagnoni and others},
  aeditor = {S. Cagnoni and J. Gottlieb and E. Hart and
                 M. Middendorf and G. R. Raidl},
  year = 2002,
  booktitle = {Applications of Evolutionary Computing,
                  Proceedings of  EvoWorkshops 2002},
  author = { Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
  title = {An Experimental Investigation of Iterated Local Search for Coloring Graphs},
  pages = {122--131}
}
@incollection{PaqStu2003tpls,
  address = { Heidelberg },
  publisher = {Springer},
  volume = 2632,
  series = {Lecture Notes in Computer Science},
  shorteditor = { Carlos M. Fonseca  and  others},
  editor = { Carlos M. Fonseca  and  Peter J. Fleming  and  Eckart Zitzler  and  Kalyanmoy Deb  and  Lothar Thiele },
  year = 2003,
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2003},
  author = { Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
  title = {A Two-Phase Local Search for the Biobjective
                  Traveling Salesman Problem},
  pages = {479--493},
  alias = {lpaquete:8}
}
@techreport{PaqStuLop-IRIDIA-2005-029,
  author = { Lu{\'i}s Paquete  and  Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {On the design and analysis of {SLS} algorithms for
                  multiobjective combinatorial optimization problems},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2005,
  number = {TR/IRIDIA/2005-029},
  abstract = {Effective Stochastic Local Search (SLS) algorithms
                  can be seen as being composed of several algorithmic
                  components, each of which plays some specific role
                  with respect to overall performance. In this
                  article, we explore the application of experimental
                  design techniques to analyze the effect of different
                  choices for these algorithmic components on SLS
                  algorithms applied to Multiobjective Combinatorial
                  Optimization Problems that are solved in terms of
                  {Pareto} optimality. This analysis is done using the
                  example application of SLS algorithms to the
                  biobjective Quadratic Assignment Problem and we show
                  also that the same choices for algorithmic
                  components can lead to different behavior in
                  dependence of various instance features, such as the
                  structure of input data and the correlation between
                  objectives.},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2005-029r001.pdf}
}
@inproceedings{PaqStuLop05mic,
  address = {Vienna, Austria},
  year = 2005,
  booktitle = {6th Metaheuristics International Conference (MIC 2005)},
  editor = { Karl F. Doerner  and  Michel Gendreau  and Peter Greistorfer and  Gutjahr, Walter J.  and  Richard F. Hartl  and  Marc Reimann },
  author = { Lu{\'i}s Paquete  and  Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Towards the Empirical Analysis of {SLS} Algorithms
                  for Multiobjective Combinatorial Optimization
                  Problems through Experimental Design},
  pages = {739--746},
  abstract = { Stochastic Local Search (SLS) algorithms for
                  Multiobjective Combinatorial Optimization Problems
                  (MCOPs) typically involve the selection and
                  parameterization of many algorithm components whose
                  role with respect to their overall performance and
                  relation to certain instance features is often not
                  clear. In this abstract, we use a modular approach
                  for the design of SLS algorithms for MCOPs defined
                  in terms of {Pareto} optimality and we present an
                  extensive analysis of SLS algorithms through
                  experimental design techniques, where each algorithm
                  component is considered a factor. The experimental
                  analysis is based on a sound experimental
                  methodology for analyzing the output of algorithms
                  for MCOPs. We show that different choices for
                  algorithm components can lead to different behavior
                  in dependence of various instance features.}
}
@incollection{PaqStuLop07metaheuristics,
  author = { Lu{\'i}s Paquete  and  Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Using experimental design to analyze stochastic local search
                  algorithms for multiobjective problems},
  booktitle = {Metaheuristics: Progress in Complex Systems Optimization},
  pages = {325--344},
  year = 2007,
  doi = {10.1007/978-0-387-71921-4_17},
  volume = 39,
  series = {Operations Research / Computer Science Interfaces},
  publisher = {Springer},
  address = { New York, NY },
  annote = {Post-Conference Proceedings of the 6th Metaheuristics
                  International Conference (MIC 2005)},
  editor = {Karl F. Doerner and Michel Gendreau and Peter Greistorfer and  Gutjahr, Walter J.  and  Richard F. Hartl  and  Marc Reimann },
  abstract = {Stochastic Local Search (SLS) algorithms can be seen as being
                  composed of several algorithmic components, each playing some
                  specific role with respect to overall performance. This
                  article explores the application of experimental design
                  techniques to analyze the effect of components of SLS
                  algorithms for Multiobjective Combinatorial Optimization
                  problems, in particular for the Biobjective Quadratic
                  Assignment Problem. The analysis shows that there exists a
                  strong dependence between the choices for these components
                  and various instance features, such as the structure of the
                  input data and the correlation between the objectives.}
}
@article{ParDoeHar2009,
  author = {Parragh, S. N. and  Karl F. Doerner  and  Richard F. Hartl  and  Xavier Gandibleux },
  title = {A heuristic two-phase solution approach for the
                  multi-objective dial-a-ride problem},
  journal = {Networks},
  volume = 54,
  number = 4,
  pages = {227--242},
  year = 2009
}
@article{ParJoh1997,
  author = {Rebecca Parsons and Mark Johnson},
  title = {A Case Study in Experimental Design Applied to Genetic Algorithms with Applications to {DNA} Sequence Assembly},
  journal = {American Journal of Mathematical and Management Sciences},
  publisher = {Taylor \& Francis},
  volume = 17,
  number = {3-4},
  pages = {369--396},
  year = 1997,
  doi = {10.1080/01966324.1997.10737444}
}
@article{ParKim98,
  author = {Moon-Won Park and Yeong-Dae Kim},
  title = {A systematic procedure for setting parameters in simulated annealing algorithms},
  journal = {Computers \& Operations Research},
  volume = 25,
  number = 3,
  pages = {207--217},
  year = 1998,
  doi = {10.1016/S0305-0548(97)00054-3}
}
@article{ParLopFre2002:ieee-tec,
  author = {R. S. Parpinelli and H. S. Lopes and A. A. Freitas},
  title = {Data Mining with an Ant Colony Optimization Algorithm},
  journal = {IEEE Transactions on Evolutionary Computation},
  year = 2002,
  volume = 6,
  number = 4,
  pages = {321--332}
}
@article{ParVas2007:ejor,
  author = { R. O. Parreiras  and  J. A. Vascocelos },
  title = {A multiplicative version of {PROMETHEE II} applied to
                   multiobjective optimization problems},
  journal = {European Journal of Operational Research},
  year = 2007,
  volume = 183,
  pages = {729--740}
}
@article{Paul2010,
  title = {Comparative performance of tabu search and simulated annealing heuristics for the quadratic assignment problem},
  author = { Paul, Gerald },
  journal = {Operations Research Letters},
  volume = 38,
  number = 6,
  pages = {577--581},
  year = 2010,
  publisher = {Elsevier}
}
@incollection{Paulli1993,
  publisher = {Springer},
  year = 1993,
  editor = { Ren{\'e} Victor Valqui Vidal },
  booktitle = {Applied Simulated Annealing},
  title = {A computational comparison of simulated annealing and tabu search applied to the quadratic assignment problem},
  author = {Paulli, J},
  pages = {85--102}
}
@inproceedings{PavDelKes2019,
  aurl = {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 = {Pavelski, Lucas Marcondes and Delgado, Myriam Regattieri and  Marie-El{\'e}onore Kessaci },
  title = {Meta-Learning on Flowshop Using Fitness Landscape Analysis},
  pages = {925--933}
}
@book{Pea84,
  author = { Judea Pearl },
  title = {Heuristics: Intelligent Search Strategies for Computer
                  Problem Solving},
  publisher = {Addison-Wesley},
  address = { Reading, MA },
  year = 1984
}
@book{Pea93,
  author = {Glen S. Peace},
  title = {Taguchi Methods: A Hands-On Approach},
  publisher = {Addison-Wesley},
  year = 1993
}
@inproceedings{PeaBar2011aaai,
  year = 2011,
  publisher = {{AAAI} Press},
  booktitle = {Proceedings of  the {AAAI} Conference on Artificial Intelligence},
  editor = {Wolfram Burgard and Dan Roth},
  title = {Transportability of causal and statistical relations: A formal approach},
  author = { Judea Pearl  and  Elias Bareinboim },
  pages = {247--254}
}
@book{PeaMac2018,
  title = {The book of why: the new science of cause and effect},
  author = { Judea Pearl  and Mackenzie, Dana},
  year = 2018,
  publisher = {Basic books}
}
@book{Pearl2009causality,
  author = { Judea Pearl },
  title = {Causality: Models, Reasoning and Inference},
  year = 2009,
  publisher = {Cambridge University Press},
  edition = {2nd}
}
@article{Pearl2019:cacm,
  title = {The seven tools of causal inference, with reflections on machine learning},
  author = { Judea Pearl },
  journal = {Communications of the ACM},
  volume = 62,
  number = 3,
  pages = {54--60},
  year = 2019
}
@incollection{PedCarCanVen2014,
  author = {Juan A. Pedraza and  Carlos Garc{\'i}a-Mart{\'i}nez  and Alberto Cano and Sebasti\'an Ventura},
  title = {Classification Rule Mining with Iterated Greedy},
  booktitle = {Hybrid Artificial Intelligence Systems - 9th International Conference,
               {HAIS} 2014, Salamanca, Spain, June 11-13, 2014. Proceedings},
  publisher = {Springer},
  address = { Heidelberg },
  year = 2014,
  editor = {Marios M. Polycarpou and Andr{\'{e}} Carlos Ponce Leon Ferreira de Carvalho and Jeng{-}Shyang Pan and Michal Wozniak and H{\'{e}}ctor Quinti{\'{a}}n and Emilio Corchado},
  volume = 8480,
  series = {Lecture Notes in Computer Science},
  pages = {585--596}
}
@article{PedNesCan2011:asc,
  author = {Mart{\'i}n Pedemonte and Sergio Nesmachnow and H{\'e}ctor Cancela},
  title = {A survey on parallel ant colony optimization},
  journal = {Applied Soft Computing},
  volume = 11,
  number = 8,
  pages = {5181--5197},
  year = 2011
}
@incollection{PelBir2007:slse,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 4638,
  editor = { Thomas St{\"u}tzle  and  Mauro Birattari  and  Holger H. Hoos },
  year = 2007,
  booktitle = {Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics. SLS~2007},
  author = { Paola Pellegrini  and  Mauro Birattari },
  title = {Implementation Effort and Performance},
  pages = {31--45}
}
@article{PelBirStu2012:si,
  author = { Paola Pellegrini  and  Mauro Birattari  and  Thomas St{\"u}tzle },
  title = {A Critical Analysis of Parameter Adaptation in Ant Colony Optimization},
  journal = {Swarm Intelligence},
  year = 2012,
  volume = 6,
  number = 1,
  pages = {23--48},
  doi = {10.1007/s11721-011-0061-0}
}
@article{PelCasPes2012its,
  author = { Paola Pellegrini  and L. Castelli and R. Pesenti},
  title = {Metaheuristic algorithms for the simultaneous slot
                  allocation problem},
  journal = {IET Intelligent Transport Systems},
  year = 2012,
  volume = 6,
  number = 4,
  pages = {453--462},
  month = dec,
  doi = {10.1049/iet-its.2011.0179}
}
@incollection{PelFavMor06,
  address = { Heidelberg },
  publisher = {Springer},
  year = 2006,
  volume = 4150,
  series = {Lecture Notes in Computer Science},
  editor = { Marco Dorigo  and others },
  fulleditor = { Marco Dorigo  and  L. M. Gambardella  and  Mauro Birattari  and 
                  Martinoli, A. and  Poli, R.  and  Thomas St{\"u}tzle },
  booktitle = {Ant Colony Optimization and Swarm Intelligence, 5th
                  International Workshop, ANTS 2006},
  author = { Paola Pellegrini  and  D. Favaretto  and  E. Moretti },
  title = {On {\MaxMinAntSystem}'s Parameters},
  pages = {203--214}
}
@incollection{PelFavMor09:NICSO,
  doi = {10.1007/978-3-642-03211-0},
  editor = {Natalio Krasnogor and Belén Melián-Batista and José
                  Andrés Moreno-Pérez and J. Marcos Moreno-Vega and David Alejandro Pelta},
  address = { Berlin, Germany },
  volume = 236,
  series = {Studies in Computational Intelligence},
  year = 2009,
  publisher = {Springer},
  booktitle = {Nature Inspired Cooperative Strategies for Optimization
                  (NICSO 2008)},
  author = { Paola Pellegrini  and  D. Favaretto  and  E. Moretti },
  title = {Exploration in stochastic algorithms: An application
                  on {\MaxMinAntSystem}},
  pages = {1--13}
}
@article{PelMasBirStu14,
  author = { Paola Pellegrini  and  Franco Mascia  and  Thomas St{\"u}tzle  and  Mauro Birattari },
  title = {On the Sensitivity of Reactive Tabu Search to its
                  Meta-parameters},
  journal = {Soft Computing},
  year = 2014,
  volume = 18,
  number = 11,
  pages = {2177--2190},
  doi = {10.1007/s00500-013-1192-6}
}
@incollection{PelStuBir2010,
  volume = 6234,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  fulleditor = { Marco Dorigo  and  Mauro Birattari  and  Gianni A. {Di Caro}  and Doursat, R. and Engelbrecht, A. P. and Floreano,
                  D. and Gambardella, L. M. and Gro\ss, R. and Sahin,
                  E. and  Thomas St{\"u}tzle  and Sayama, H.},
  editor = { Marco Dorigo  and others },
  year = 2010,
  booktitle = {Swarm Intelligence, 7th International Conference, ANTS 2010},
  author = { Paola Pellegrini  and  Thomas St{\"u}tzle  and  Mauro Birattari },
  title = {Off-line vs. On-line Tuning: A Study on {\MaxMinAntSystem}
                  for the {TSP}},
  pages = {239--250},
  doi = {10.1007/978-3-642-15461-4_21}
}
@article{PenSubOch2013:joh,
  author = {Puca Huachi {Vaz Penna} and  Anand Subramanian  and  Luiz Satoru Ochi },
  title = {An Iterated Local Search Heuristic for the Heterogeneous Fleet Vehicle Routing Problem},
  journal = {Journal of Heuristics},
  year = 2013,
  volume = 19,
  number = 2,
  pages = {201--232}
}
@article{Per2020challenge,
  title = {Challenge to scientists: does your ten-year-old code still
                  run?},
  author = {Jeffrey M. Perkel},
  journal = {Nature},
  volume = 584,
  pages = {556--658},
  year = 2020,
  doi = {10.1038/d41586-020-02462-7},
  keywords = {reproducibility; software engineering; ReScience C; Ten Years
                  Reproducibility Challenge; code reusability},
  annote = {\url{https://www.nature.com/articles/d41586-020-02462-7}}
}
@inproceedings{PerBisStu2017rf,
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2017,
  editor = {Peter A. N. Bosman},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference Companion, GECCO Companion 2017},
  author = {  P{\'e}rez C{\'a}ceres, Leslie and  Bernd Bischl  and  Thomas St{\"u}tzle },
  title = {Evaluating random forest models for irace},
  pages = {1146--1153},
  doi = {10.1145/3067695.3082057}
}
@incollection{PerLopHooStu2017:lion,
  address = { Cham, Switzerland },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 10556,
  editor = { Roberto Battiti  and Dmitri E. Kvasov and Yaroslav D. Sergeyev},
  year = 2017,
  booktitle = {Learning and Intelligent Optimization, 11th International
                  Conference, LION 11},
  author = {  P{\'e}rez C{\'a}ceres, Leslie and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Holger H. Hoos  and  Thomas St{\"u}tzle },
  title = {An Experimental Study of Adaptive Capping in {\rpackage{irace}}},
  pages = {235--250},
  doi = {10.1007/978-3-319-69404-7_17},
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2016-007/}
}
@misc{PerLopHooStu2017:lion-supp,
  author = {  P{\'e}rez C{\'a}ceres, Leslie and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Holger H. Hoos  and  Thomas St{\"u}tzle },
  title = {An experimental study of adaptive capping in irace: Supplementary material},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2016-007/}},
  year = 2017
}
@incollection{PerLopStu2014ants,
  volume = 8667,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg },
  publisher = {Springer},
  editor = { Marco Dorigo  and others },
  year = 2014,
  booktitle = {Swarm Intelligence, 9th International Conference, ANTS 2014},
  author = {  P{\'e}rez C{\'a}ceres, Leslie and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Ant Colony Optimization on a Budget of 1000},
  doi = {10.1007/978-3-319-09952-1_5},
  pages = {50--61}
}
@incollection{PerLopStu2014evocop,
  address = { Heidelberg },
  publisher = {Springer},
  volume = 8600,
  series = {Lecture Notes in Computer Science},
  year = 2014,
  booktitle = {Proceedings of EvoCOP 2014 -- 14th European Conference on Evolutionary Computation in Combinatorial Optimization },
  editor = { Christian Blum  and  Gabriela Ochoa },
  author = {  P{\'e}rez C{\'a}ceres, Leslie and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {An Analysis of Parameters of irace},
  doi = {10.1007/978-3-662-44320-0_4},
  pages = {37--48}
}
@misc{PerLopStu2015budget-supp,
  author = {  P{\'e}rez C{\'a}ceres, Leslie and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Ant Colony Optimization on a Budget of 1000: {Supplementary} material},
  url = {http://iridia.ulb.ac.be/supp/IridiaSupp2015-004},
  year = 2015
}
@article{PerLopStu2015si,
  author = {  P{\'e}rez C{\'a}ceres, Leslie and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Ant colony optimization on a limited budget of evaluations},
  journal = {Swarm Intelligence},
  year = 2015,
  doi = {10.1007/s11721-015-0106-x},
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2015-004},
  volume = 9,
  number = {2-3},
  pages = {103--124}
}
@incollection{PerPagFraStu2017gcc,
  address = { Heidelberg },
  publisher = {Springer},
  editor = {Lutton, Evelyne and Legrand, Pierrick and Parrend, Pierre and  Nicolas Monmarch{\'e}  and  Marc Schoenauer },
  volume = 10764,
  series = {Lecture Notes in Computer Science},
  year = 2017,
  booktitle = {EA 2017: Artificial Evolution},
  author = {  P{\'e}rez C{\'a}ceres, Leslie and  Federico Pagnozzi  and  Alberto Franzin  and  Thomas St{\"u}tzle },
  title = {Automatic Configuration of {GCC} Using {\rpackage{irace}}},
  pages = {202--216},
  abstract = {Automatic algorithm configuration techniques have proved to
                  be successful in finding performance-optimizing parameter
                  settings of many search-based decision and optimization
                  algorithms. A recurrent, important step in software
                  development is the compilation of source code written in some
                  programming language into machine-executable code. The
                  generation of performance-optimized machine code itself is a
                  difficult task that can be parametrized in many different
                  possible ways. While modern compilers usually offer different
                  levels of optimization as possible defaults, they have a
                  larger number of other flags and numerical parameters that
                  impact properties of the generated machine-code. While the
                  generation of performance-optimized machine code has received
                  large attention and is dealt with in the research area of
                  auto-tuning, the usage of standard automatic algorithm
                  configuration software has not been explored, even though, as
                  we show in this article, the performance of the compiled code
                  has significant stochasticity, just as standard optimization
                  algorithms. As a practical case study, we consider the
                  configuration of the well-known GNU compiler collection (GCC)
                  for minimizing the run-time of machine code for various
                  heuristic search methods. Our experimental results show that,
                  depending on the specific code to be optimized, improvements
                  of up to 40{\%} of execution time when compared to the -O2
                  and -O3 optimization flags is possible.},
  doi = {10.1007/978-3-319-78133-4_15}
}
@misc{PerPagFraStu2017gccsup,
  author = {  P{\'e}rez C{\'a}ceres, Leslie and  Federico Pagnozzi  and  Alberto Franzin  and  Thomas St{\"u}tzle },
  title = {Automatic configuration of {GCC} using irace: Supplementary
                  material},
  howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2017-009/}},
  year = 2017
}
@article{PerRuiNes2018,
  author = {P{\'{e}}res, Matias and Ruiz, Germ{\'{a}}n and Nesmachnow,
                  Sergio and Olivera, Ana C.},
  journal = {Applied Soft Computing},
  keywords = {Multiobjective evolutionary
                  algorithms,Pollution,Simulation,Traffic flow},
  title = {Multiobjective evolutionary optimization of traffic flow and
                  pollution in {Montevideo}, {Uruguay}},
  year = 2018,
  alias = {Peres2018},
  volume = 70,
  pages = {472--485},
  publisher = {Elsevier}
}
@phdthesis{Perez-Caceres2017phd,
  author = {  P{\'e}rez C{\'a}ceres, Leslie and  Thomas St{\"u}tzle },
  title = {Automatic Algorithm Configuration: Analysis, Improvements and
                  Applications},
  school = {IRIDIA, {\'E}cole polytechnique, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2017,
  annote = {Supervised by  Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  eprint = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/262048}
}
@article{PesUchAraRod2010,
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  year = 2010,
  volume = 2,
  number = {3--4},
  pages = {259--290}
}
@article{Pesant98tsptw,
  author = { Gilles Pesant  and  Michel Gendreau  and  Jean-Yves Potvin  and J.-M. Rousseau },
  title = {An Exact Constraint Logic Programming Algorithm
  for the Traveling Salesman Problem with Time Windows},
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  year = 1998,
  volume = 32,
  pages = {12--29}
}
@article{Pet1998boeing,
  author = {Charles W. Petit},
  title = {Touched by nature: putting evolution to work on the assembly
                  line},
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  month = jul,
  year = 1998,
  pages = {43--45},
  url = {http://dynamics.org/~altenber/IN_THE_MEDIA/US_NEWS.7-27-98/index.html},
  annote = {Evolutionary optimization of turbine design of the
                  Boeing~777~GE}
}
@inproceedings{PetEve2002control,
  publisher = {Morgan Kaufmann Publishers, San Francisco, CA},
  editor = {W. B. Langdon and others},
  year = 2002,
  booktitle = {Proceedings of the Genetic and Evolutionary
                  Computation Conference, GECCO 2002},
  title = {Controlling genetic algorithms with reinforcement learning},
  author = {Pettinger, James E. and  Everson, Richard M. },
  pages = {692--692}
}
@article{Pezeshk96,
  author = { S. Pezeshk  and  O. J. Helweg },
  title = {Adaptative Search Optimisation in reducing pump operation
                  costs},
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  volume = 122,
  number = 1,
  pages = {57--63},
  date = {1996-01/1996-02},
  year = 1996,
  month = jan # { / } # feb
}
@article{PheKok2003interactive,
  title = {An interactive evolutionary metaheuristic for multiobjective
                  combinatorial optimization},
  author = {Phelps, Selcen and  Murat K{\"o}ksalan },
  journal = {Management Science},
  volume = 49,
  number = 12,
  pages = {1726--1738},
  year = 2003
}
@article{PieKhuSav2007investigation,
  title = {An investigation on preference order ranking scheme for
                  multiobjective evolutionary optimization},
  author = {di Pierro, Francesco and Khu, Soon-Thiam and  Dragan A. Savic },
  journal = {IEEE Transactions on Evolutionary Computation},
  volume = 11,
  number = 1,
  pages = {17--45},
  year = 2007
}
@inproceedings{PihMus2014,
  publisher = {IEEE Press},
  year = 2014,
  editor = {Papadopoulos, George Angelos},
  booktitle = {26th {IEEE} International Conference on Tools with Artificial Intelligence,
                  {ICTAI} 2014, Limassol, Cyprus, November 10-12, 2014},
  title = {Application of Machine Learning to Algorithm Selection for {TSP}},
  author = {Pihera, Josef and  Musliu, Nysret },
  pages = {47--54}
}
@incollection{PilWhi02,
  address = { Heidelberg },
  publisher = {Springer},
  editor = { Marco Dorigo  and others },
  fulleditor = { Marco Dorigo  and  Gianni A. {Di Caro}  and  M. Sampels },
  volume = 2463,
  series = {Lecture Notes in Computer Science},
  year = 2002,
  booktitle = {Ant Algorithms, Third International Workshop, ANTS
                  2002},
  author = {M. L. Pilat and T. White},
  title = {Using Genetic Algorithms to optimize {ACS-TSP}},
  pages = {282--287}
}
@book{Pin2012,
  author = {Michael L. Pinedo},
  title = {Scheduling: Theory, Algorithms, and Systems},
  publisher = {Springer},
  year = 2012,
  edition = {4th},
  address = { New York, NY }
}
@incollection{PinRunSoup07:maxsat,
  author = {Pedro Pinto and Thomas Runkler and Jo{\~a}o Sousa},
  title = {Ant Colony Optimization and its Application to
                  Regular and Dynamic {MAX-SAT} Problems},
  booktitle = {Advances in Biologically Inspired Information
                  Systems},
  year = 2007,
  volume = 69,
  pages = {285--304},
  series = {Studies in Computational Intelligence},
  doi = {10.1007/978-3-540-72693-7_15},
  publisher = {Springer},
  address = { Berlin, Germany },
  abstract = {In this chapter we discuss the ant colony
                  optimization meta-heuristic {(ACO)} and its
                  application to static and dynamic constraint
                  satisfaction optimization problems, in particular
                  the static and dynamic maximum satisfiability
                  problems {(MAX-SAT).} In the first part of the
                  chapter we give an introduction to meta-heuristics
                  in general and ant colony optimization in
                  particular, followed by an introduction to
                  constraint satisfaction and static and dynamic
                  constraint satisfaction optimization problems.
                  Then, we describe how to apply the {ACO} algorithm
                  to the problems, and do an analysis of the results
                  obtained for several benchmarks.  The adapted ant
                  colony optimization accomplishes very well the task
                  of dealing with systematic changes of dynamic
                  {MAX-SAT} instances derived from static problems.  }
}
@misc{PinSin2020checklist,
  author = {Joelle Pineau and Koustuv Sinha},
  title = {The Machine Learning Reproducibility Checklist (v2.0)},
  howpublished = {\url{https://www.cs.mcgill.ca/~jpineau/ReproducibilityChecklist-v2.0.pdf}},
  year = 2020,
  annote = {Used in NeurIPS 2020}
}
@article{PinVinSin2020improving,
  title = {Improving Reproducibility in Machine Learning Research (A
                  Report from the {NeurIPS} 2019 Reproducibility Program)},
  author = {Joelle Pineau and Philippe Vincent-Lamarre and Koustuv Sinha
                  and Vincent Larivière and Alina Beygelzimer and Florence
                  d'Alché-Buc and Emily Fox and Hugo Larochelle},
  year = 2020,
  journal = {Arxiv preprint arXiv:2003.12206 [cs.LG]},
  url = {https://arxiv.org/abs/2003.12206}
}
@article{PisRop2007:cor,
  author = { David Pisinger  and  Stefan Ropke },
  title = {A General Heuristic for Vehicle Routing Problems},
  journal = {Computers \& Operations Research},
  year = 2007,
  volume = 34,
  number = 8,
  pages = {2403--2435}
}
@incollection{PisRop2010:handbook,
  address = { New York, NY },
  publisher = {Springer},
  edition = {2nd},
  series = {International Series in Operations Research \& Management
                  Science},
  volume = 146,
  booktitle = {Handbook of Metaheuristics},
  year = 2010,
  editor = { Michel Gendreau  and  Jean-Yves Potvin },
  title = {Large Neighborhood Search},
  author = { David Pisinger  and  Stefan Ropke },
  pages = {399--419}
}
@article{PitAlmDoe06,
  author = {Rapeepan Pitakaso and  Christian Almeder  and  Karl F. Doerner  and  Richard F. Hartl },
  title = {Combining exact and population-based methods for the
                  Constrained Multilevel Lot Sizing Problem},
  journal = {International Journal of Production Research},
  year = 2006,
  volume = 44,
  number = 22,
  pages = {4755--4771}
}
@article{PitAlmDoe07,
  author = {Rapeepan Pitakaso and  Christian Almeder  and  Karl F. Doerner  and  Richard F. Hartl },
  title = {A {\MaxMinAntSystem} for unconstrained multi-level
                  lot-sizing problems},
  volume = 34,
  number = 9,
  journal = {Computers \& Operations Research},
  year = 2007,
  keywords = {Ant colony optimization, Material requirements
                  planning, Multi-level lot-sizing, {Wagner-Whitin}
                  algorithm},
  pages = {2533--2552},
  doi = {10.1016/j.cor.2005.09.022},
  abstract = { In this paper, we present an ant-based algorithm
                  for solving unconstrained multi-level lot-sizing
                  problems called ant system for multi-level
                  lot-sizing algorithm {(ASMLLS).} We apply a hybrid
                  approach where we use ant colony optimization in
                  order to find a good lot-sizing sequence, i.e. a
                  sequence of the different items in the product
                  structure in which we apply a modified
                  {Wagner-Whitin} algorithm for each item
                  separately. Based on the setup costs each ant
                  generates a sequence of items. Afterwards a simple
                  single-stage lot-sizing rule is applied with
                  modified setup costs. This modification of the setup
                  costs depends on the position of the item in the
                  lot-sizing sequence, on the items which have been
                  lot-sized before, and on two further parameters,
                  which are tried to be improved by a systematic
                  search. For small-sized problems {ASMLLS} is among
                  the best algorithms, but for most medium- and
                  large-sized problems it outperforms all other
                  approaches regarding solution quality as well as
                  computational time.}
}
@inproceedings{PitBehAff2013,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  year = 2013,
  volume = 7832,
  booktitle = {Proceedings of EvoCOP 2013 -- 13th European Conference on Evolutionary Computation in Combinatorial Optimization },
  editor = { Martin Middendorf  and  Christian Blum },
  author = {Pitzer, Erik and Beham, Andreas and Affenzeller, Michael},
  title = {Automatic Algorithm Selection for the Quadratic Assignment
                   Problem Using Fitness Landscape Analysis},
  pages = {109--120}
}
@article{Ples2018repro,
  doi = {10.3389/fninf.2017.00076},
  year = 2018,
  month = jan,
  publisher = {Frontiers Media {SA}},
  volume = 11,
  author = {Hans E. Plesser},
  title = {Reproducibility vs.\ Replicability: A Brief History of a
                  Confused Terminology},
  journal = {Frontiers in Neuroinformatics}
}
@inproceedings{PloMelVarBucZhuLee2013tact,
  series = {Procedia Computer Science},
  year = 2013,
  volume = 18,
  publisher = {Elsevier},
  editor = {Vassil Alexandrov and Michael Lees and Valeria Krzhizhanovskaya and Jack Dongarra and Peter M.A. Sloot},
  booktitle = {2013 International Conference on Computational Science},
  author = {Dmitry Plotnikov and Dmitry Melnik and Mamikon Vardanyan and
                  Ruben Buchatskiy and Roman Zhuykov and Je-Hyung Lee},
  title = {Automatic Tuning of Compiler Optimizations and Analysis of
                  their Impact},
  pages = {1312--1321},
  doi = {10.1016/j.procs.2013.05.298}
}
@book{Plotnick2009book,
  author = {Robert Plotnick},
  title = {The Genie in the Machine: How Computer-Automated Inventing Is
                  Revolutionizing Law and Business},
  publisher = {Stanford Law Books},
  year = 2009,
  annote = {Mentions evolutionary optimization of Oral-B toothbrushes}
}
@article{PorGonAllHsu2017:ejor,
  author = {Daniel Porumbel and Gilles Goncalves and Hamid Allaoui and Tient{\'e} Hsu},
  title = {Iterated Local Search and Column Generation to solve Arc-Routing as a Permutation Set-Covering Problem},
  journal = {European Journal of Operational Research},
  year = 2017,
  volume = 256,
  number = 2,
  pages = {349--367}
}
@article{PorParDoa++2013,
  author = {Juan Porta and Jorge Parapar and Ram{\'o}n Doallo and Vasco Barbosa and In{\'e}s Sant{\'e} and Rafael Crecente and Carlos D{\'i}az},
  title = {A Population-based Iterated Greedy Algorithm for the Delimitation and Zoning of Rural Settlements},
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  year = 2013,
  volume = 39,
  pages = {12--26}
}
@article{PotBen96vrptw_gene,
  author = { Jean-Yves Potvin  and  S. Bengio },
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  volume = 8,
  pages = {165--172}
}
@techreport{Pow2009bobyqa,
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  institution = {University of Cambridge, UK},
  year = 2009,
  number = {Cambridge NA Report NA2009/06},
  annote = {\url{http://www6.cityu.edu.hk/rcms/publications/preprint26.pdf}}
}
@article{Pra2009jwrpm,
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  number = 4,
  pages = {129--136}
}
@article{PraPac2015:joh,
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  year = 2016,
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  number = 4,
  pages = {587--611},
  doi = {10.1007/s10732-014-9279-5}
}
@inproceedings{PraTraWanBaeKer2020,
  publisher = {IEEE Press},
  year = 2020,
  booktitle = {2020 {IEEE} Symposium Series on Computational Intelligence, {SSCI}
                  2020, Canberra, Australia, December 1-4, 2020},
  editor = { Carlos A. {Coello Coello} },
  title = {Per-Instance Configuration of the Modularized {CMA-ES}
                  by Means of Classifier Chains and Exploratory Landscape Analysis},
  author = {Prager, Raphael Patrick and  Heike Trautmann  and Wang, Hao and  Thomas B{\"a}ck  and  Pascal Kerschke },
  pages = {996--1003}
}
@inproceedings{PraYao2006taa,
  title = {A new multi-objective evolutionary optimisation algorithm:
                  the two-archive algorithm},
  author = {Praditwong, Kata and Yao, Xin},
  booktitle = {Computational intelligence and security, 2006 international
                  conference on},
  volume = 1,
  pages = {286--291},
  year = 2006,
  organization = {IEEE}
}
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  year = 2003,
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  editor = { C. Maksimovi{\'c}  and  David Butler  and  Fayyaz Ali Memon },
  author = { T. Devi Prasad  and  Godfrey A. Walters },
  title = {Optimal rerouting to minimise residence times in
                  water distribution networks},
  pages = {299--306}
}
@book{PrepSha1988:compgeom,
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  year = 1988,
  edition = {2nd},
  alias = {preparata:computational}
}
@book{PriStoLam2005:book,
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  year = 2005,
  publisher = {Springer},
  address = { New York, NY },
  doi = {10.1007/3-540-31306-0}
}
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@article{ProBisBou2018tuna,
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                  Algorithms},
  author = {Probst, Philipp and  Bernd Bischl  and Boulesteix,
                  Anne-Laure},
  journal = {Arxiv preprint arXiv:1802.09596},
  year = 2018,
  url = {https://arxiv.org/abs/1802.09596},
  keywords = {parameter importance}
}
@article{ProBisBou2019tuna,
  author = {Probst, Philipp and  Bernd Bischl  and Boulesteix,
                  Anne-Laure},
  title = {Tunability: Importance of Hyperparameters of Machine Learning
                  Algorithms},
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  year = 2019,
  volume = 20,
  number = 53,
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}
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  number = 3,
  pages = {681--701},
  volume = 22,
  keywords = {Kriging; Entropy; Design of experiments; Space-filling;
                  Sphere packing; Maximin design; Minimax design},
  alias = {Pronzato2012}
}
@incollection{PryMosNaz2007heatmap,
  address = { Heidelberg },
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 4403,
  editor = {S. Obayashi and others},
  year = 2007,
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2007},
  title = {Heatmap visualization of population based multi objective
                  algorithms},
  author = {Pryke, Andy and  Mostaghim, Sanaz  and Nazemi, Alireza},
  pages = {361--375}
}
@article{Psaraftis1995:aor,
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  title = {Dynamic Vehicle Routing: Status and Prospects},
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}
@article{PukHei2006,
  title = {Optimizing heuristic search in forest planning},
  author = {Pukkala, Timo and Heinonen, Tero},
  journal = {Nonlinear Analysis: Real World Applications},
  volume = 7,
  number = 5,
  year = 2006,
  pages = {1284--1297},
  publisher = {Elsevier}
}
@incollection{PulCoe2003emo,
  address = { Heidelberg },
  publisher = {Springer},
  volume = 2632,
  series = {Lecture Notes in Computer Science},
  shorteditor = { Carlos M. Fonseca  and  others},
  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 = { Gregorio {Toscano Pulido}  and  Carlos A. {Coello Coello} },
  title = {The Micro Genetic Algorithm 2: Towards Online
                  Adaptation in Evolutionary Multiobjective
                  Optimization},
  pages = {252--266},
  doi = {10.1007/3-540-36970-8_18}
}
@article{PulTac2009aqme,
  title = {A self-adaptive multi-engine solver for quantified {Boolean}
                  formulas},
  author = {Pulina, Luca and Tacchella, Armando},
  journal = {Constraints},
  volume = 14,
  number = 1,
  pages = {80--116},
  year = 2009
}
@techreport{PurDebMan2014coin,
  title = {A review of hybrid evolutionary multiple criteria decision
                  making methods},
  author = { Robin C. Purshouse  and  Kalyanmoy Deb  and Mansor, Maszatul M. and  Mostaghim, Sanaz  and Wang, Rui},
  year = 2014,
  institution = {Computational Optimization and Innovation (COIN) Laboratory, University of Michigan, USA},
  type = {COIN Report},
  number = 2014005,
  month = jan
}
@article{PurFle2007tec,
  title = {On the Evolutionary Optimization of Many Conflicting
                  Objectives},
  author = { Robin C. Purshouse  and  Peter J. Fleming },
  journal = {IEEE Transactions on Evolutionary Computation},
  year = 2007,
  doi = {10.1109/TEVC.2007.910138},
  volume = 11,
  number = 6,
  pages = {770--784}
}
@incollection{PusFraVor2011spiral,
  doi = {10.1007/978-0-387-09766-4_244},
  publisher = {Springer, US},
  year = 2011,
  editor = {David Padua},
  booktitle = {Encyclopedia of Parallel Computing},
  author = {Markus P\"{u}schel and Franz Franchetti and Yevgen Voronenko},
  title = {Spiral},
  pages = {1920--1933}
}
@incollection{PusHoo2018ppsn,
  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 Machado,
                  P. and  Lu{\'i}s Paquete  and  Darrell Whitley },
  booktitle = {Parallel Problem Solving from Nature - PPSN XV},
  author = {Yasha Pushak and  Holger H. Hoos },
  title = {Algorithm Configuration Landscapes: More Benign Than
                  Expected?},
  pages = {271--283},
  doi = {10.1007/978-3-319-99259-4_22},
  supplement = {http://www.cs.ubc.ca/labs/beta/Projects/ACLandscapes/},
  annote = {Best paper award at PPSN2018}
}
@incollection{PusHoo2020golden,
  aurl = {https://dl.acm.org/citation.cfm?id=3377930},
  location = {Canc{\'u}n, Mexico},
  doi = {10.1145/3377930},
  isbn = {978-1-4503-7128-5},
  address = { New York, NY },
  publisher = {ACM Press},
  year = 2020,
  editor = { Carlos A. {Coello Coello} },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2020},
  author = {Yasha Pushak and  Holger H. Hoos },
  title = {Golden parameter search: exploiting structure to quickly
                  configure parameters in parallel},
  pages = {245--253},
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}
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