LopezIbanez.bib

@comment{{This file has been generated by bib2bib 1.99}}
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@preamble{{\providecommand{\MaxMinAntSystem}{{$\cal MAX$--$\cal MIN$} {Ant} {System}} } # {\providecommand{\rpackage}[1]{{#1}} } # {\providecommand{\softwarepackage}[1]{{#1}} } # {\providecommand{\proglang}[1]{{#1}} }}
@unpublished{CamTriLop2017pseudo,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
}
@techreport{IRIDIA-2018-001,
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatically Designing State-of-the-Art Multi- and
                  Many-Objective Evolutionary Algorithms},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2018,
  number = {TR/IRIDIA/2018-001},
  month = jan,
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2018-001.pdf},
  note = {Published in Evolutionary Computation journal~\cite{BezLopStu2019ec}}
}
@techreport{IRIDIA-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-2017-011,
  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 a book chapter~\cite{BezLopStu2020chapter}}
}
@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
}
@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{LopPerDubStuBir2016iraceguide,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and   P{\'e}rez C{\'a}ceres, Leslie and  J{\'e}r{\'e}mie Dubois-Lacoste  and  Thomas St{\"u}tzle  and  Mauro Birattari },
  title = {The {\rpackage{irace}} package: User Guide},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2016,
  number = {TR/IRIDIA/2016-004},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2016-004.pdf}
}
@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
}
@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-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-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-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-2011-003,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {The Automatic Design of Multi-Objective Ant Colony
                  Optimization Algorithms},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2011,
  number = {TR/IRIDIA/2011-003},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2011-003.pdf},
  note = {Published in IEEE Transactions on Evolutionary
                  Computation~\cite{LopStu2012tec}}
}
@techreport{LopDubStu2011irace,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  J{\'e}r{\'e}mie Dubois-Lacoste  and  Thomas St{\"u}tzle  and  Mauro Birattari },
  title = {The {\rpackage{irace}} package, Iterated Race for Automatic
                  Algorithm Configuration},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2011,
  number = {TR/IRIDIA/2011-004},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2011-004.pdf},
  note = {Published in Operations Research Perspectives~\cite{LopDubPerStuBir2016irace}}
}
@techreport{IRIDIA-2011-001,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Joshua D. Knowles  and  Marco Laumanns },
  title = {On Sequential Online Archiving of Objective Vectors},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2011,
  number = {TR/IRIDIA/2011-001},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2011-001.pdf},
  note = {This is a revised version of the paper published in EMO 2011~\cite{LopKnoLau2011emo}}
}
@techreport{IRIDIA-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-2009-026,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Adaptive ``Anytime'' Two-Phase Local Search},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2010,
  number = {TR/IRIDIA/2009-026},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2009-026.pdf},
  note = {Published in the proceedings of LION 4~\cite{DubLopStu10:lion-bfsp}}
}
@techreport{IRIDIA-2010-019,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {A Hybrid {TP+PLS} Algorithm for Bi-objective
                  Flow-Shop Scheduling Problems},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2010,
  number = {TR/IRIDIA/2010-019},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2010-019.pdf},
  note = {Published in Computers \& Operations Research~\cite{DubLopStu2011cor}}
}
@techreport{IRIDIA-2010-022,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Improving the Anytime Behavior of Two-Phase Local
                  Search},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2010,
  number = {TR/IRIDIA/2010-022},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2010-022.pdf},
  note = {Published in Annals of Mathematics and Artificial Intelligence~\cite{DubLopStu2011amai}}
}
@techreport{IRIDIA-2009-015,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
  title = {Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2009,
  number = {TR/IRIDIA/2009-015},
  month = may,
  note = {Published as a book chapter~\cite{LopPaqStu09emaa}}
}
@techreport{IRIDIA-2009-019,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {An Analysis of Algorithmic Components for
                  Multiobjective Ant Colony Optimization: A Case Study
                  on the Biobjective {TSP}},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  number = {TR/IRIDIA/2009-019},
  year = 2009,
  month = jun,
  note = {Published in the proceedings of Evolution Artificielle, 2009~\cite{LopStu09ea}}
}
@techreport{IRIDIA-2009-020,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Effective Hybrid Stochastic Local Search Algorithms
                  for Biobjective Permutation Flowshop Scheduling},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  number = {TR/IRIDIA/2009-020},
  year = 2009,
  month = jun,
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2009-020.pdf},
  note = {Published in the proceedings of Hybrid Metaheuristics 2009~\cite{DubLopStu09:hm-bfsp}}
}
@techreport{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}}
}
@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}}
}
@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}}
}
@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}
}
@techreport{PaqStuLop-IRIDIA-2005-029,
  author = { Lu{\'i}s Paquete  and  Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {On the design and analysis of {SLS} algorithms for
                  multiobjective combinatorial optimization problems},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2005,
  number = {TR/IRIDIA/2005-029},
  abstract = {Effective Stochastic Local Search (SLS) algorithms
                  can be seen as being composed of several algorithmic
                  components, each of which plays some specific role
                  with respect to overall performance. In this
                  article, we explore the application of experimental
                  design techniques to analyze the effect of different
                  choices for these algorithmic components on SLS
                  algorithms applied to Multiobjective Combinatorial
                  Optimization Problems that are solved in terms of
                  {Pareto} optimality. This analysis is done using the
                  example application of SLS algorithms to the
                  biobjective Quadratic Assignment Problem and we show
                  also that the same choices for algorithmic
                  components can lead to different behavior in
                  dependence of various instance features, such as the
                  structure of input data and the correlation between
                  objectives.},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2005-029.pdf}
}
@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}}
}
@phdthesis{LopezIbanezPhD,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Operational Optimisation of Water Distribution
                  Networks},
  school = {School of Engineering and the Built Environment},
  year = 2009,
  address = {Edinburgh Napier University, UK},
  url = {https://researchrepository.napier.ac.uk/id/eprint/3044}
}
@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},
  pdf = {Lopez-Ibanez_MOACO.pdf}
}
@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}}
}
@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{SouRitLop2020capopt,
  author = { Marcelo {De Souza}  and  Marcus Ritt and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {{CAPOPT}: Capping Methods for the Automatic Configuration of Optimization Algorithms},
  howpublished = {\url{https://github.com/souzamarcelo/capopt}},
  year = 2020
}
@misc{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.}
}
@inproceedings{HunLop2019turing,
  isbn = {978-1-5262-0820-0},
  organization = {Alan Turing Institute},
  month = nov # { 21--22},
  year = 2019,
  date = {2019-11-21/2019-11-22},
  address = {London, UK},
  editor = {Iv{\'a}n Palomares},
  booktitle = {International Alan Turing Conference on Decision Support and
                  Recommender systems},
  author = {Maura Hunt and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Modeling a Decision-Maker in Goal Programming by means of
                  Computational Rationality},
  pages = {17--20},
  abstract = {This paper extends a simulation of cognitive mechanisms in
                  the context of multi-criteria decision-making by using ideas
                  from computational rationality. Specifically, this paper
                  improves the simulation of a human decision-maker (DM) by
                  considering how resource constraints impact their evaluation
                  process in an interactive Goal Programming problem. Our
                  analysis confirms and emphasizes a previous simulation study
                  by showing key areas that could be effected by cognitive
                  mechanisms. While the results are promising, the effects
                  should be validated by future experiments with human DMs.},
  epub = {https://dsrs.blogs.bristol.ac.uk/files/2020/01/DSRS-Turing_19.pdf#page=24}
}
@inproceedings{LopMasMarStu2013mista,
  year = 2013,
  editor = { Graham Kendall  and  Vanden Berghe, Greet   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},
  epub = {https://hal.inria.fr/hal-01094681},
  pdf = {LopMasMarStu2013mista.pdf}
}
@inproceedings{EppLopStuDeS2011:cec,
  year = 2011,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  booktitle = {Proceedings of  the 2011 Congress on Evolutionary Computation (CEC 2011)},
  key = {IEEE CEC},
  author = { Stefan Eppe  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle  and  Yves {De Smet} },
  title = {An Experimental Study of Preference Model Integration into
                  Multi-Objective Optimization Heuristics},
  pages = {2751--2758},
  doi = {10.1109/CEC.2011.5949963}
}
@inproceedings{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},
  pdf = {MauLopStu2010-Parameter Adaptation in Max-Min Ant System.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,
  pdf = {LopezPrasadPaechter-WDSA2008-official.pdf},
  doi = {10.1061/41024(340)40},
  publisher = {ASCE}
}
@inproceedings{FonPaqLop06:hypervolume,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  month = jul,
  year = 2006,
  booktitle = {Proceedings of  the 2006 Congress on Evolutionary Computation (CEC 2006)},
  key = {IEEE CEC},
  author = { Carlos M. Fonseca  and  Lu{\'i}s Paquete  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {An improved dimension-\hspace{0pt}sweep
                  algorithm for the hypervolume indicator},
  pages = {1157--1163},
  doi = {10.1109/CEC.2006.1688440},
  pdf = {FonPaqLop06-hypervolume.pdf},
  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.}
}
@inproceedings{LopPraPaech05:cec,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  month = sep,
  year = 2005,
  booktitle = {Proceedings of  the 2005 Congress on Evolutionary Computation (CEC 2005)},
  key = {IEEE CEC},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  T. Devi Prasad  and  Ben Paechter },
  title = {Multi-objective Optimisation of the Pump Scheduling
                  Problem using {SPEA2}},
  pages = {435--442},
  volume = 1,
  doi = {10.1109/CEC.2005.1554716}
}
@inproceedings{LopPraPaech:ccwi2005,
  month = sep,
  address = {University of Exeter, UK},
  volume = 1,
  editor = { Dragan A. Savic  and  Godfrey A. Walters  and  Roger King  and  Soon Thiam-Khu },
  year = 2005,
  booktitle = {Proceedings of the Eighth International Conference on
                  Computing and Control for the Water Industry (CCWI 2005)},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  T. Devi Prasad  and  Ben Paechter },
  title = {Optimal Pump Scheduling: Representation and Multiple
                  Objectives},
  pdf = {LopPraPae05-ccwi.pdf},
  pages = {117--122}
}
@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.},
  pdf = {PaqStuLop05mic.pdf}
}
@incollection{AyoAllLop2023gecco,
  location = {Lisbon, Portugal},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO Companion 2023},
  annote = {ISBN: 979-8-4007-0120-7},
  address = { New York, NY},
  year = 2023,
  publisher = {ACM Press},
  editor = {Silva, Sara and  Lu{\'i}s Paquete },
  author = { Ayodele, Mayowa  and  Allmendinger, Richard  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Parizy, Matthieu  and  Arnaud Liefooghe },
  title = {Applying {Ising} Machines to Multi-Objective {QUBOs}},
  pages = {2166--2174},
  doi = {10.1145/3583133.3596312},
  abstract = {Multi-objective optimisation problems involve finding
                  solutions with varying trade-offs between multiple and often
                  conflicting objectives. Ising machines are physical devices
                  that aim to find the absolute or approximate ground states of
                  an Ising model. To apply Ising machines to multi-objective
                  problems, a weighted sum objective function is used to
                  convert multi-objective into single-objective
                  problems. However, deriving scalarisation weights that
                  archives evenly distributed solutions across the Pareto front
                  is not trivial. Previous work has shown that adaptive weights
                  based on dichotomic search, and one based on averages of
                  previously explored weights can explore the Pareto front
                  quicker than uniformly generated weights. However, these
                  adaptive methods have only been applied to bi-objective
                  problems in the past. In this work, we extend the adaptive
                  method based on averages in two ways: (i) we extend the
                  adaptive method of deriving scalarisation weights for
                  problems with two or more objectives, and (ii) we use an
                  alternative measure of distance to improve performance. We
                  compare the proposed method with existing ones and show that
                  it leads to the best performance on multi-objective
                  Unconstrained Binary Quadratic Programming (mUBQP) instances
                  with 3 and 4 objectives and that it is competitive with the
                  best one for instances with 2 objectives.},
  numpages = 9,
  keywords = {digital annealer, multi-objective, bi-objective QAP, QUBO}
}
@incollection{LieLop2023many,
  location = {Lisbon, Portugal},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2023},
  annote = {ISBN: 9798400701191},
  address = { New York, NY},
  year = 2023,
  publisher = {ACM Press},
  editor = {Silva, Sara and  Lu{\'i}s Paquete },
  author = { Arnaud Liefooghe  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Many-objective (Combinatorial) Optimization is Easy},
  pages = {704--712},
  doi = {10.1145/3583131.3590475},
  abstract = {It is a common held assumption that problems with many
                  objectives are harder to optimize than problems with two or
                  three objectives. In this paper, we challenge this assumption
                  and provide empirical evidence that increasing the number of
                  objectives tends to reduce the difficulty of the landscape
                  being optimized. Of course, increasing the number of
                  objectives brings about other challenges, such as an increase
                  in the computational effort of many operations, or the memory
                  requirements for storing non-dominated solutions. More
                  precisely, we consider a broad range of multi- and
                  many-objective combinatorial benchmark problems, and we
                  measure how the number of objectives impacts the dominance
                  relation among solutions, the connectedness of the Pareto
                  set, and the landscape multimodality in terms of local
                  optimal solutions and sets. Our analysis shows the limit
                  behavior of various landscape features when adding more
                  objectives to a problem. Our conclusions do not contradict
                  previous observations about the inability of
                  Pareto-optimality to drive search, but we explain these
                  observations from a different perspective. Our findings have
                  important implications for the design and analysis of
                  many-objective optimization algorithms.}
}
@incollection{TraNikCen2022ngopt,
  volume = 13398,
  year = 2022,
  address = { Cham, Switzerland},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { G{\"u}nther Rudolph  and  Anna V. Kononova  and  Aguirre, Hern\'{a}n E.  and  Pascal Kerschke  and  Gabriela Ochoa  and  Tea Tu{\v s}ar },
  booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XVII}},
  author = {Trajanov, Risto and Nikolikj, Ana and Cenikj, Gjorgjina and
                  Teytaud, Fabien and Videau, Mathurin and Olivier Teytaud  and  Tome Eftimov  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Carola Doerr },
  title = {Improving {Nevergrad}'s Algorithm Selection Wizard {NGOpt}
                  Through Automated Algorithm Configuration},
  pages = {18--31},
  doi = {10.1007/978-3-031-14714-2_2},
  abstract = {Algorithm selection wizards are effective and versatile tools
                  that automatically select an optimization algorithm given
                  high-level information about the problem and available
                  computational resources, such as number and type of decision
                  variables, maximal number of evaluations, possibility to
                  parallelize evaluations, etc. State-of-the-art algorithm
                  selection wizards are complex and difficult to improve. We
                  propose in this work the use of automated configuration
                  methods for improving their performance by finding better
                  configurations of the algorithms that compose them. In
                  particular, we use elitist iterated racing (irace) to find
                  CMA configurations for specific artificial benchmarks that
                  replace the hand-crafted CMA configurations currently used in
                  the NGOpt wizard provided by the Nevergrad platform. We
                  discuss in detail the setup of irace for the purpose of
                  generating configurations that work well over the diverse set
                  of problem instances within each benchmark. Our approach
                  improves the performance of the NGOpt wizard, even on
                  benchmark suites that were not part of the tuning by irace.}
}
@incollection{DobNebLop2022ants,
  volume = 13491,
  series = {Lecture Notes in Computer Science},
  address = { Cham, Switzerland},
  publisher = {Springer},
  editor = { Marco Dorigo  and  Heiko Hamann  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Jos{\'e} Garc{\'i}a-Nieto  and  Andries Engelbrecht  and  Carlo Pinciroli  and  Volker Strobel  and Camacho-Villal\'{o}n, Christian Leonardo},
  year = 2022,
  booktitle = {Swarm Intelligence, 13th International Conference, ANTS 2022},
  author = {Doblas, Daniel and  Nebro, Antonio J.  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Jos{\'e} Garc{\'i}a-Nieto  and  Carlos A. {Coello Coello} },
  title = {Automatic Design of Multi-objective Particle Swarm
                  Optimizers},
  doi = {10.1007/978-3-031-20176-9_3},
  pages = {28--40}
}
@incollection{PriAllLop2022gecco,
  location = {Boston, Massachusetts},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2022},
  address = { New York, NY},
  year = 2022,
  publisher = {ACM Press},
  editor = { Jonathan E. Fieldsend  and  Markus Wagner },
  author = { Pricopie, Stefan  and  Allmendinger, Richard  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and Fare,
                  Clyde and Benatan, Matt and  Joshua D. Knowles },
  title = {Expensive Optimization with Production-Graph Resource
                  Constraints: A First Look at a New Problem Class},
  doi = {10.1145/3512290.3528741},
  abstract = {We consider a new class of expensive, resource-constrained
                  optimization problems (here arising from molecular discovery)
                  where costs are associated with the experiments (or
                  evaluations) to be carried out during the optimization
                  process. In the molecular discovery problem, candidate
                  compounds to be optimized must be synthesized in an iterative
                  process that starts from a set of purchasable items and
                  builds up to larger molecules. To produce target molecules,
                  their required resources are either used from
                  already-synthesized items in storage or produced themselves
                  on-demand at an additional cost. Any remaining resources from
                  the production process are stored for reuse for the next
                  evaluations. We model these resource dependencies with a
                  directed acyclic production graph describing the development
                  process from granular purchasable items to evaluable target
                  compounds. Moreover, we develop several resource-eficient
                  algorithms to address this problem. In particular, we develop
                  resource-aware variants of Random Search heuristics and of
                  Bayesian Optimization and analyze their performance in terms
                  of anytime behavior. The experimental results were obtained
                  from a real-world molecular optimization problem. Our results
                  suggest that algorithms that encourage exploitation by
                  reusing existing resources achieve satisfactory results while
                  using fewer resources overall.},
  pages = {840--848},
  numpages = 9,
  keywords = {molecular discovery, resource constraints, expensive
                  optimization, production costs}
}
@incollection{LopChiGil2022evo,
  volume = 13224,
  series = {Lecture Notes in Computer Science},
  address = {Switzerland},
  publisher = {Springer Nature},
  booktitle = {EvoApplications 2022: Applications of Evolutionary Computation},
  year = 2022,
  editor = { Jim{\'e}nez Laredo, Juan Luis  and others},
  fuleditor = { Jim{\'e}nez Laredo, Juan Luis  and Hidalgo Perez, J. Ignacio  and Oluwatoyin Babaagba, Kehinde},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Chicano, Francisco  and Rodrigo Gil-Merino},
  title = {The Asteroid Routing Problem: A Benchmark for Expensive
                  Black-Box Permutation Optimization},
  pages = {124--140},
  abstract = {Inspired by the recent 11th Global Trajectory Optimisation
                  Competition, this paper presents the asteroid routing problem
                  (ARP) as a realistic benchmark of algorithms for expensive
                  bound-constrained black-box optimization in permutation
                  space. Given a set of asteroids' orbits and a departure
                  epoch, the goal of the ARP is to find the optimal sequence
                  for visiting the asteroids, starting from Earth's orbit, in
                  order to minimize both the cost, measured as the sum of the
                  magnitude of velocity changes required to complete the trip,
                  and the time, measured as the time elapsed from the departure
                  epoch until visiting the last asteroid. We provide
                  open-source code for generating instances of arbitrary sizes
                  and evaluating solutions to the problem.  As a preliminary
                  analysis, we compare the results of two methods for expensive
                  black-box optimization in permutation spaces, namely,
                  Combinatorial Efficient Global Optimization (CEGO), a
                  Bayesian optimizer based on Gaussian processes, and
                  Unbalanced Mallows Model (UMM), an estimation-of-distribution
                  algorithm based on probabilistic Mallows models. We
                  investigate the best permutation representation for each
                  algorithm, either rank-based or order-based. Moreover, we
                  analyze the effect of providing a good initial solution,
                  generated by a greedy nearest neighbor heuristic, on the
                  performance of the algorithms. The results suggest directions
                  for improvements in the algorithms being compared.},
  keywords = {Spacecraft Trajectory Optimization, Unbalanced Mallows Model,
                  Combinatorial Efficient Global Optimization, Estimation of
                  Distribution Algorithms, Bayesian Optimization},
  supplement = {https://doi.org/10.5281/zenodo.5725837},
  doi = {10.1007/978-3-031-02462-7_9},
  epub = {https://arxiv.org/abs/2203.15708}
}
@incollection{KimAllLop2022easafe,
  location = {Boston, Massachusetts},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2022},
  address = { New York, NY},
  year = 2022,
  publisher = {ACM Press},
  editor = { Jonathan E. Fieldsend  and  Markus Wagner },
  author = { Kim, Youngmin  and  Allmendinger, Richard  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Are Evolutionary Algorithms Safe Optimizers?},
  doi = {10.1145/3512290.3528818},
  abstract = {We consider a type of constrained optimization problem, where
                  the violation of a constraint leads to an irrevocable loss,
                  such as breakage of a valuable experimental resource/platform
                  or loss of human life. Such problems are referred to as safe
                  optimization problems (SafeOPs). While SafeOPs have received
                  attention in the machine learning community in recent years,
                  there was little interest in the evolutionary computation
                  (EC) community despite some early attempts between 2009 and
                  2011. Moreover, there is a lack of acceptable guidelines on
                  how to benchmark different algorithms for SafeOPs, an area
                  where the EC community has significant experience in. Driven
                  by the need for more eficient algorithms and benchmark
                  guidelines for SafeOPs, the objective of this paper is to
                  reignite the interest of the EC community in this problem
                  class. To achieve this we (i) provide a formal definition of
                  SafeOPs and contrast it to other types of optimization
                  problems that the EC community is familiar with, (ii)
                  investigate the impact of key SafeOP parameters on the
                  performance of selected safe optimization algorithms, (iii)
                  benchmark EC against state-of-the-art safe optimization
                  algorithms from the machine learning community, and (iv)
                  provide an open-source Python framework to replicate and
                  extend our work.},
  pages = {814--822},
  numpages = 9,
  keywords = {Bayesian optimization, constrained optimization,
                  benchmarking, safety constraints, safe optimization}
}
@incollection{VerWanLopDoe2022undersampling,
  location = {Boston, Massachusetts},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2022},
  address = { New York, NY},
  year = 2022,
  publisher = {ACM Press},
  editor = { Jonathan E. Fieldsend  and  Markus Wagner },
  author = { Diederick Vermetten  and Hao Wang and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Carola Doerr  and  Thomas B{\"a}ck },
  title = {Analyzing the Impact of Undersampling on the Benchmarking and
                  Configuration of Evolutionary Algorithms},
  doi = {10.1145/3512290.3528799},
  abstract = {The stochastic nature of iterative optimization heuristics
                  leads to inherently noisy performance measurements. Since
                  these measurements are often gathered once and then used
                  repeatedly, the number of collected samples will have a
                  significant impact on the reliability of algorithm
                  comparisons. We show that care should be taken when making
                  decisions based on limited data. Particularly, we show that
                  the number of runs used in many benchmarking studies, e.g.,
                  the default value of 15 suggested by the COCO environment,
                  can be insufficient to reliably rank algorithms on well-known
                  numerical optimization benchmarks.Additionally, methods for
                  automated algorithm configuration are sensitive to
                  insufficient sample sizes. This may result in the
                  configurator choosing a "lucky" but poor-performing
                  configuration despite exploring better ones. We show that
                  relying on mean performance values, as many configurators do,
                  can require a large number of runs to provide accurate
                  comparisons between the considered configurations. Common
                  statistical tests can greatly improve the situation in most
                  cases but not always. We show examples of performance losses
                  of more than 20\%, even when using statistical races to
                  dynamically adjust the number of runs, as done by irace. Our
                  results underline the importance of appropriately considering
                  the statistical distribution of performance values.},
  pages = {867--875},
  numpages = 9,
  keywords = {parameter tuning, evolution strategies, algorithm
                  configuration, performance measures}
}
@incollection{AyoAllLop2022gecco,
  location = {Boston, Massachusetts},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2022},
  address = { New York, NY},
  year = 2022,
  publisher = {ACM Press},
  editor = { Jonathan E. Fieldsend  and  Markus Wagner },
  author = { Ayodele, Mayowa  and  Allmendinger, Richard  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Parizy, Matthieu },
  title = {Multi-Objective {QUBO} Solver: Bi-Objective Quadratic
                  Assignment Problem},
  pages = {467--475},
  doi = {10.1145/3512290.3528698},
  abstract = {Quantum and quantum-inspired optimisation algorithms are
                  designed to solve problems represented in binary, quadratic
                  and unconstrained form. Combinatorial optimisation problems
                  are therefore often formulated as Quadratic Unconstrained
                  Binary Optimisation Problems (QUBO) to solve them with these
                  algorithms. Moreover, these QUBO solvers are often
                  implemented using specialised hardware to achieve enormous
                  speedups, e.g. Fujitsu's Digital Annealer (DA) and D-Wave's
                  Quantum Annealer. However, these are single-objective
                  solvers, while many real-world problems feature multiple
                  conflicting objectives. Thus, a common practice when using
                  these QUBO solvers is to scalarise such multi-objective
                  problems into a sequence of single-objective problems. Due to
                  design trade-offs of these solvers, formulating each
                  scalarisation may require more time than finding a local
                  optimum. We present the first attempt to extend the algorithm
                  supporting a commercial QUBO solver as a multi-objective
                  solver that is not based on scalarisation. The proposed
                  multi-objective DA algorithm is validated on the bi-objective
                  Quadratic Assignment Problem. We observe that algorithm
                  performance significantly depends on the archiving strategy
                  adopted, and that combining DA with non-scalarisation methods
                  to optimise multiple objectives outperforms the current
                  scalarised version of the DA in terms of final solution
                  quality.},
  numpages = 9,
  keywords = {digital annealer, multi-objective, bi-objective QAP, QUBO}
}
@incollection{ChuLop2021gecco,
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO Companion 2021},
  address = { New York, NY},
  year = 2021,
  publisher = {ACM Press},
  editor = { Chicano, Francisco  and  Krzysztof Krawiec },
  author = { Tinkle Chugh  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Maximising Hypervolume and Minimising $\epsilon$-Indicators
                  using Bayesian Optimisation over Sets},
  doi = {10.1145/3449726.3463178},
  keywords = {multi-objective, surrogate models, epsilon, hypervolume},
  supplement = {https://doi.org/10.5281/zenodo.4675569},
  pages = {1326--1334}
}
@incollection{ShaLopKno2021gecco,
  location = {Lille, France},
  address = { New York, NY},
  publisher = {ACM Press},
  year = 2021,
  editor = { Chicano, Francisco  and  Krzysztof Krawiec },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2021},
  author = { Shavarani, Seyed Mahdi  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Joshua D. Knowles },
  title = {Realistic Utility Functions Prove Difficult for
                  State-of-the-Art Interactive Multiobjective Optimization
                  Algorithms},
  pages = {457--465},
  doi = {10.1145/3449639.3459373},
  pdf = {ShaLopKno2021gecco.pdf}
}
@incollection{KimAllLop2020safe,
  year = 2021,
  volume = 12641,
  address = { Cham, Switzerland},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  booktitle = {Trustworthy AI -- Integrating Learning, Optimization and
                  Reasoning. TAILOR 2020},
  editor = {Fredrik Heintz and Michela Milano and   O'Sullivan, Barry },
  author = { Kim, Youngmin  and  Allmendinger, Richard  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Safe Learning and Optimization Techniques: Towards a Survey
                  of the State of the Art},
  pages = {123--139},
  doi = {10.1007/978-3-030-73959-1_12},
  abstract = {Safe learning and optimization deals with learning and
                  optimization problems that avoid, as much as possible, the
                  evaluation of non-safe input points, which are solutions,
                  policies, or strategies that cause an irrecoverable loss
                  (e.g., breakage of a machine or equipment, or life
                  threat). Although a comprehensive survey of safe
                  reinforcement learning algorithms was published in 2015, a
                  number of new algorithms have been proposed thereafter, and
                  related works in active learning and in optimization were not
                  considered. This paper reviews those algorithms from a number
                  of domains including reinforcement learning, Gaussian process
                  regression and classification, evolutionary computing, and
                  active learning. We provide the fundamental concepts on which
                  the reviewed algorithms are based and a characterization of
                  the individual algorithms. We conclude by explaining how the
                  algorithms are connected and suggestions for future
                  research.}
}
@incollection{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},
  pages = {225--233},
  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.},
  keywords = {UMM, Permutation, Expensive, Black-box}
}
@incollection{CinFerLopAl2021evocop,
  address = { Cham, Switzerland},
  publisher = {Springer},
  volume = 12692,
  series = {Lecture Notes in Computer Science},
  year = 2021,
  booktitle = {Proceedings of EvoCOP 2021 -- 21th European Conference on Evolutionary Computation in Combinatorial Optimization },
  editor = { Christine Zarges  and  Verel, S{\'e}bastien },
  author = { Christian Cintrano  and  Javier Ferrer  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Alba, Enrique },
  title = {Hybridization of Racing Methods with Evolutionary Operators
                  for Simulation Optimization of Traffic Lights Programs},
  abstract = {In many real-world optimization problems, like the traffic
                  light scheduling problem tackled here, the evaluation of
                  candidate solutions requires the simulation of a process
                  under various scenarios. Thus, good solutions should not only
                  achieve good objective function values, but they must be
                  robust (low variance) across all different scenarios.
                  Previous work has revealed the effectiveness of IRACE for
                  this task.  However, the operators used by IRACE to generate
                  new solutions were designed for configuring algorithmic
                  parameters, that have various data types (categorical,
                  numerical, etc.). Meanwhile, evolutionary algorithms have
                  powerful operators for numerical optimization, which could
                  help to sample new solutions from the best ones found in the
                  search. Therefore, in this work, we propose a hybridization
                  of the elitist iterated racing mechanism of IRACE with
                  evolutionary operators from differential evo- lution and
                  genetic algorithms. We consider a realistic case study
                  derived from the traffic network of Malaga (Spain) with 275
                  traffic lights that should be scheduled optimally. After a
                  meticulous study, we discovered that the hybrid algorithm
                  comprising IRACE plus differential evolution offers
                  statistically better results than conventional algorithms and
                  also improves travel times and reduces pollution.},
  keywords = {Hybrid algorithms, Evolutionary algorithms, Simulation
                  optimization, Uncertainty, Traffic light planning},
  pages = {17--33},
  doi = {10.1007/978-3-030-72904-2_2},
  annote = {Extended version published in Evolutionary Computation journal~\cite{CinFerLopAlb2022irace}.}
}
@incollection{AvrAllLop2021evo,
  volume = {12694},
  series = {Lecture Notes in Computer Science},
  address = { Cham, Switzerland},
  publisher = {Springer},
  booktitle = {Applications of Evolutionary Computation},
  year = 2021,
  editor = {Pedro Castillo and  Jim{\'e}nez Laredo, Juan Luis },
  author = { Andreea Avramescu  and  Allmendinger, Richard  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {A Multi-Objective Multi-Type Facility Location Problem for
                  the Delivery of Personalised Medicine},
  pages = {388--403},
  doi = {10.1007/978-3-030-72699-7_25},
  abstract = {Advances in personalised medicine targeting specific
                  sub-populations and individuals pose a challenge to the
                  traditional pharmaceutical industry. With a higher level of
                  personalisation, an already critical supply chain is facing
                  additional demands added by the very sensitive nature of its
                  products. Nevertheless, studies concerned with the efficient
                  development and delivery of these products are scarce. Thus,
                  this paper presents the case of personalised medicine and the
                  challenges imposed by its mass delivery. We propose a
                  multi-objective mathematical model for the
                  location-allocation problem with two interdependent facility
                  types in the case of personalised medicine products. We show
                  its practical application through a cell and gene therapy
                  case study. A multi-objective genetic algorithm with a novel
                  population initialisation procedure is used as solution
                  method.},
  supplement = {https://doi.org/10.5281/zenodo.4495162},
  keywords = {Personalised medicine, Biopharmaceuticals Supply chain,
                  Facility location-allocation, Evolutionary multi-objective
                  optimisation}
}
@incollection{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.}
}
@incollection{StuLop2019hb,
  publisher = {Springer},
  series = {International Series in Operations Research \& Management
                  Science},
  volume = 272,
  booktitle = {Handbook of Metaheuristics},
  year = 2019,
  editor = { Michel Gendreau  and  Jean-Yves Potvin },
  author = { Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Automated Design of Metaheuristic Algorithms},
  pages = {541--579},
  doi = {10.1007/978-3-319-91086-4_17},
  keywords = {automatic design, automatic configuration}
}
@incollection{SaiLopMie2019gecco,
  epub = {https://dl.acm.org/citation.cfm?id=3319619},
  isbn = {978-1-4503-6748-6},
  address = { New York, NY},
  publisher = {ACM Press},
  year = 2019,
  editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Anne Auger  and  Thomas St{\"u}tzle },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO Companion 2019},
  author = { Saini, Bhupinder Singh  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Kaisa Miettinen },
  title = {Automatic Surrogate Modelling Technique Selection based on
                  Features of Optimization Problems},
  doi = {10.1145/3319619.3326890},
  pdf = {SaiLopMie2019gecco.pdf},
  pages = {1765--1772},
  abstract = {A typical scenario when solving industrial single or
                  multiobjective optimization problems is that no explicit
                  formulation of the problem is available. Instead, a dataset
                  containing vectors of decision variables together with their
                  objective function value(s) is given and a surrogate model
                  (or metamodel) is build from the data and used for
                  optimization and decision-making. This data-driven
                  optimization process strongly depends on the ability of the
                  surrogate model to predict the objective value of decision
                  variables not present in the original dataset. Therefore, the
                  choice of surrogate modelling technique is crucial. While
                  many surrogate modelling techniques have been discussed in
                  the literature, there is no standard procedure that will
                  select the best technique for a given problem. In this work,
                  we propose the automatic selection of a surrogate modelling
                  technique based on exploratory landscape features of the
                  optimization problem that underlies the given dataset. The
                  overall idea is to learn offline from a large pool of
                  benchmark problems, on which we can evaluate a large number
                  of surrogate modelling techniques. When given a new dataset,
                  features are used to select the most appropriate surrogate
                  modelling technique. The preliminary experiments reported
                  here suggest that the proposed automatic selector is able to
                  identify high-accuracy surrogate models as long as an
                  appropriate classifier is used for selection.}
}
@incollection{NebLopBarGar2019gecco,
  epub = {https://dl.acm.org/citation.cfm?id=3319619},
  isbn = {978-1-4503-6748-6},
  address = { New York, NY},
  publisher = {ACM Press},
  year = 2019,
  editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Anne Auger  and  Thomas St{\"u}tzle },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO Companion 2019},
  title = {Automatic Configuration of {NSGA-II} with {jMetal} and irace},
  author = { Nebro, Antonio J.  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Barba-Gonz{\'a}lez, Crist{\'o}bal  and  Jos{\'e} Garc{\'i}a-Nieto },
  doi = {10.1145/3319619.3326832},
  pdf = {NebLopBarGar2019gecco.pdf},
  pages = {1374--1381}
}
@incollection{ShaKomLopKaz2019gecco,
  epub = {https://dl.acm.org/citation.cfm?id=3321707},
  isbn = {978-1-4503-6111-8},
  address = { New York, NY},
  publisher = {ACM Press},
  year = 2019,
  editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Anne Auger  and  Thomas St{\"u}tzle },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2019},
  author = { Mudita Sharma  and Alexandros Komninos  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Dimitar Kazakov },
  title = {Deep Reinforcement Learning-Based Parameter Control in
                  Differential Evolution},
  pages = {709--717},
  supplement = {https://dx.doi.org/10.5281/zenodo.2628228},
  doi = {10.1145/3321707.3321813},
  pdf = {ShaKomLopKaz2019gecco.pdf},
  keywords = {DE-DDQN}
}
@incollection{BezLopStu2019gecco,
  epub = {https://dl.acm.org/citation.cfm?id=3321707},
  isbn = {978-1-4503-6111-8},
  address = { New York, NY},
  publisher = {ACM Press},
  year = 2019,
  editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Anne Auger  and  Thomas St{\"u}tzle },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2019},
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Archiver Effects on the Performance of State-of-the-art
                  Multi- and Many-objective Evolutionary Algorithms},
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2019-004/},
  doi = {10.1145/3321707.3321789},
  pdf = {BezLopStu2019gecco.pdf}
}
@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}
}
@incollection{ShaLopKaz2018ppsn,
  volume = 11102,
  year = 2018,
  address = { Cham, Switzerland},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { Anne Auger  and  Carlos M. Fonseca  and Louren{\c c}o, N. and  Penousal Machado  and  Lu{\'i}s Paquete  and  Darrell Whitley },
  booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XV}},
  author = { Mudita Sharma  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Dimitar Kazakov },
  title = {Performance Assessment of Recursive Probability Matching for
                  Adaptive Operator Selection in Differential Evolution},
  supplement = {https://github.com/mudita11/AOS-comparisons},
  doi = {10.1007/978-3-319-99259-4_26},
  pages = {321--333},
  keywords = {Rec-PM}
}
@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/},
  annote = {Proposed ACOTSPQAP software}
}
@incollection{BroCalLop2018dagstuhl,
  keywords = {multiple criteria decision making, evolutionary
                  multiobjective optimization},
  doi = {10.4230/DagRep.8.1.33},
  volume = {8(1)},
  year = 2018,
  series = {Dagstuhl Reports},
  publisher = {Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik,
                  Germany},
  booktitle = {Personalized Multiobjective Optimization: An Analytics
                  Perspective (Dagstuhl Seminar 18031)},
  editor = { Kathrin Klamroth  and  Joshua D. Knowles  and  G{\"u}nther Rudolph  and  Margaret M. Wiecek },
  author = { Dimo Brockhoff  and  Roberto Calandra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Frank Neumann  and Selvakumar Ulaganathan},
  title = {Meta-modeling for (interactive) multi-objective optimization
                  (WG5)},
  pages = {85--94}
}
@incollection{BloLopKesJou2018ppsn,
  volume = 11101,
  year = 2018,
  address = { Cham, Switzerland},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { Anne Auger  and  Carlos M. Fonseca  and Louren{\c c}o, N. and  Penousal Machado  and  Lu{\'i}s Paquete  and  Darrell Whitley },
  booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XV}},
  author = { Aymeric Blot  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Marie-El{\'e}onore Kessaci-Marmion  and  Laetitia Jourdan },
  title = {New Initialisation Techniques for Multi-Objective Local
                  Search: Application to the Bi-objective Permutation Flowshop},
  doi = {10.1007/978-3-319-99253-2_26},
  pages = {323--334}
}
@incollection{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},
  pdf = {LieLopPaqVer2018gecco.pdf}
}
@incollection{LieDerVerLop2018ppsn,
  volume = 11102,
  year = 2018,
  address = { Cham, Switzerland},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { Anne Auger  and  Carlos M. Fonseca  and Louren{\c c}o, N. and  Penousal Machado  and  Lu{\'i}s Paquete  and  Darrell Whitley },
  booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XV}},
  author = { Arnaud Liefooghe  and  Bilel Derbel  and  Verel, S{\'e}bastien  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Aguirre, Hern\'{a}n E.  and  Tanaka, Kiyoshi },
  title = {On {Pareto} Local Optimal Solutions Networks},
  pages = {232--244},
  doi = {10.1007/978-3-319-99259-4_19}
}
@incollection{StuLop2017gecco,
  address = { New York, NY},
  publisher = {ACM Press},
  year = 2017,
  editor = { Peter A. N. Bosman },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO Companion 2017},
  author = { Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Automated Offline Design of Algorithms},
  pages = {1038--1065},
  doi = {10.1145/3067695.3067722}
}
@incollection{BezLopStu2017emo,
  editor = {Heike Trautmann and G{\"{u}}nter Rudolph and Kathrin Klamroth
                  and Oliver Sch{\"{u}}tze and Margaret M. Wiecek and Yaochu
                  Jin and Christian Grimme},
  year = 2017,
  volume = 10173,
  series = {Lecture Notes in Computer Science},
  address = { Cham, Switzerland},
  publisher = {Springer International Publishing},
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2017},
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {An Empirical Assessment of the Properties of Inverted
                  Generational Distance Indicators on Multi- and Many-objective
                  Optimization},
  pages = {31--45},
  doi = {10.1007/978-3-319-54157-0_3}
}
@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},
  pdf = {PerLopHooStu2017lion.pdf},
  doi = {10.1007/978-3-319-69404-7_17},
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2016-007/}
}
@incollection{StuLop2015gecco,
  address = { New York, NY},
  publisher = {ACM Press},
  year = 2015,
  editor = { Jim{\'e}nez Laredo, Juan Luis  and Sara Silva and  Anna I. Esparcia{-}Alc{\'{a}}zar },
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO Companion 2015},
  author = { Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Automatic (Offline) Configuration of Algorithms},
  pages = {681--702},
  doi = {10.1145/2739482.2756581}
}
@incollection{LopKno2015emo,
  editor = { Ant{\'o}nio Gaspar{-}Cunha  and  Carlos Henggeler Antunes and  Carlos A. {Coello Coello} },
  volume = 9019,
  year = 2015,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg, Germany},
  publisher = {Springer},
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2015 Part {II}},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Joshua D. Knowles },
  title = {Machine Decision Makers as a Laboratory for Interactive {EMO}},
  pages = {295--309},
  abstract = {A key challenge, perhaps the central challenge, of
                  multi-objective optimization is how to deal with candidate
                  solutions that are ultimately evaluated by the hidden or
                  unknown preferences of a human decision maker (DM) who
                  understands and cares about the optimization problem.
                  Alternative ways of addressing this challenge exist but
                  perhaps the favoured one currently is the interactive
                  approach (proposed in various forms). Here, an evolutionary
                  multi-objective optimization algorithm (EMOA) is controlled
                  by a series of interactions with the DM so that preferences
                  can be elicited and the direction of search controlled. MCDM
                  has a key role to play in designing and evaluating these
                  approaches, particularly in testing them with real DMs, but
                  so far quantitative assessment of interactive EMOAs has been
                  limited.  In this paper, we propose a conceptual framework
                  for this problem of quantitative assessment, based on the
                  definition of machine decision makers (machine DMs), made
                  somewhat realistic by the incorporation of various
                  non-idealities. The machine DM proposed here draws from
                  earlier models of DM biases and inconsistencies in the MCDM
                  literature.  As a practical illustration of our approach, we
                  use the proposed machine DM to study the performance of an
                  interactive EMOA, and discuss how this framework could help
                  in the evaluation and development of better interactive
                  EMOAs.},
  doi = {10.1007/978-3-319-15892-1_20},
  pdf = {LopKno2015emo.pdf}
}
@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, Germany},
  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}
}
@incollection{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, Germany},
  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}
}
@incollection{BraCorGre2015dagstuhl,
  keywords = {multiple criteria decision making, evolutionary
                  multiobjective optimization},
  doi = {10.4230/DagRep.5.1.96},
  volume = {5(1)},
  year = 2015,
  series = {Dagstuhl Reports},
  publisher = {Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik,
                  Germany},
  booktitle = {Understanding Complexity in Multiobjective Optimization
                  (Dagstuhl Seminar 15031)},
  editor = { Salvatore Greco  and  Kathrin Klamroth  and  Joshua D. Knowles  and  G{\"u}nther Rudolph },
  author = { J{\"u}rgen Branke  and  Salvatore Corrente  and  Salvatore Greco  and  Kadzi{\'n}ski, Mi{\l}osz   and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Vincent Mousseau  and  Mauro Munerato  and  Roman S{\l}owi{\'n}ski },
  title = {Behavior-Realistic Artificial Decision-Makers to Test
                  Preference-Based Multi-objective Optimization Method
                  ({Working} {Group} ``{Machine} {Decision}-{Making}'')},
  pages = {110--116}
}
@incollection{LopLieVer2014ppsn,
  year = 2014,
  volume = 8672,
  address = { Heidelberg, Germany},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { Thomas Bartz-Beielstein  and  J{\"u}rgen Branke  and Bogdan Filipi{\v c} and Jim Smith},
  booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XIII}},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Arnaud Liefooghe  and  Verel, S{\'e}bastien },
  doi = {10.1007/978-3-319-10762-2_61},
  title = {Local Optimal Sets and Bounded Archiving on Multi-objective
                  {NK}-Landscapes with Correlated Objectives},
  pages = {621--630},
  pdf = {LopLieVer2014ppsn.pdf},
  abstract = {The properties of local optimal solutions in multi-objective
                  combinatorial optimization problems are crucial for the
                  effectiveness of local search algorithms, particularly when
                  these algorithms are based on Pareto dominance. Such local
                  search algorithms typically return a set of mutually
                  nondominated Pareto local optimal (PLO) solutions, that is, a
                  PLO-set. This paper investigates two aspects of PLO-sets by
                  means of experiments with Pareto local search (PLS). First,
                  we examine the impact of several problem characteristics on
                  the properties of PLO-sets for multi-objective NK-landscapes
                  with correlated objectives. In particular, we report that
                  either increasing the number of objectives or decreasing the
                  correlation between objectives leads to an exponential
                  increment on the size of PLO-sets, whereas the variable
                  correlation has only a minor effect. Second, we study the
                  running time and the quality reached when using bounding
                  archiving methods to limit the size of the archive handled by
                  PLS, and thus, the maximum size of the PLO-set found. We
                  argue that there is a clear relationship between the running
                  time of PLS and the difficulty of a problem instance.}
}
@incollection{BezLopStu2014:lion,
  address = { Heidelberg, Germany},
  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, Germany},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { Thomas Bartz-Beielstein  and  J{\"u}rgen Branke  and Bogdan Filipi{\v c} and Jim Smith},
  booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XIII}},
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Design of Evolutionary Algorithms for
                  Multi-Objective Combinatorial Optimization},
  doi = {10.1007/978-3-319-10762-2_50},
  pages = {508--517}
}
@incollection{HutLopFaw2014lion,
  address = { Heidelberg, Germany},
  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},
  pdf = {HutLopFaw2014lion.pdf}
}
@incollection{MasLopDub2014hm,
  address = { Heidelberg, Germany},
  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},
  pdf = {MasLopDu2014hm.pdf},
  doi = {10.1007/978-3-319-07644-7_3}
}
@incollection{PerLopStu2014ants,
  volume = 8667,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg, Germany},
  publisher = {Springer},
  editor = { Marco Dorigo  and others },
  year = 2014,
  booktitle = {Swarm Intelligence, 9th International Conference, ANTS 2014},
  author = {  P{\'e}rez C{\'a}ceres, Leslie and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Ant Colony Optimization on a Budget of 1000},
  doi = {10.1007/978-3-319-09952-1_5},
  pages = {50--61}
}
@incollection{PerLopStu2014evocop,
  address = { Heidelberg, Germany},
  publisher = {Springer},
  volume = 8600,
  series = {Lecture Notes in Computer Science},
  year = 2014,
  booktitle = {Proceedings of EvoCOP 2014 -- 14th European Conference on Evolutionary Computation in Combinatorial Optimization },
  editor = { Christian Blum  and  Gabriela Ochoa },
  author = {  P{\'e}rez C{\'a}ceres, Leslie and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {An Analysis of Parameters of irace},
  doi = {10.1007/978-3-662-44320-0_4},
  pages = {37--48}
}
@incollection{MarMasLop2013hm,
  address = { Heidelberg, Germany},
  publisher = {Springer},
  volume = 7919,
  series = {Lecture Notes in Computer Science},
  editor = { Mar{\'i}a J. Blesa  and  Christian Blum  and Paola Festa and  Andrea Roli  and  M. Sampels },
  isbn = {978-3-642-38515-5},
  year = 2013,
  booktitle = {Hybrid Metaheuristics},
  author = { 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},
  pages = {144--158},
  doi = {10.1007/978-3-642-38516-2_12},
  pdf = {MarMasLopStu2013hm.pdf}
}
@incollection{BezLopStu2013evocop,
  address = { Heidelberg, Germany},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  year = 2013,
  volume = 7832,
  booktitle = {Proceedings of EvoCOP 2013 -- 13th European Conference on Evolutionary Computation in Combinatorial Optimization },
  editor = { Martin Middendorf  and  Christian Blum },
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {An Analysis of Local Search for the Bi-objective
                  Bidimensional Knapsack Problem},
  pages = {85--96},
  doi = {10.1007/978-3-642-37198-1_8}
}
@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},
  pdf = {DubLopStu2013hm.pdf}
}
@incollection{MasLopDubStu2013lion,
  address = { Heidelberg, Germany},
  publisher = {Springer},
  volume = 7997,
  editor = { Panos M. Pardalos  and G. Nicosia},
  series = {Lecture Notes in Computer Science},
  year = 2013,
  booktitle = {Learning and Intelligent Optimization, 7th
                  International Conference, LION 7},
  author = { 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},
  pdf = {MasLopDubStu2013lion.pdf},
  doi = {10.1007/978-3-642-44973-4_36}
}
@incollection{MasLopStu2013hm,
  address = { Heidelberg, Germany},
  publisher = {Springer},
  volume = 7919,
  series = {Lecture Notes in Computer Science},
  editor = { Mar{\'i}a J. Blesa  and  Christian Blum  and Paola Festa and  Andrea Roli  and  M. Sampels },
  isbn = {978-3-642-38515-5},
  year = 2013,
  booktitle = {Hybrid Metaheuristics},
  author = { Florence Massen  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle  and  Yves Deville },
  title = {Experimental Analysis of Pheromone-Based Heuristic
                  Column Generation Using irace},
  pages = {92--106},
  doi = {10.1007/978-3-642-38516-2_8},
  pdf = {MasLopStu2013hm.pdf}
}
@incollection{RadLopStu2013emo,
  isbn = {978-3-642-37139-4},
  year = 2013,
  volume = 7811,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg, Germany},
  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},
  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},
  pages = {825--840},
  doi = {10.1007/978-3-642-37140-0_61}
}
@incollection{StuLopPel2011autsea,
  year = 2012,
  address = { Berlin, Germany},
  publisher = {Springer},
  booktitle = {Autonomous Search},
  editor = { Youssef Hamadi  and E. Monfroy and F. Saubion},
  author = { Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Paola Pellegrini  and  Michael Maur  and  Marco A. {Montes de Oca}  and  Mauro Birattari  and  Marco Dorigo },
  title = {Parameter Adaptation in Ant Colony Optimization},
  doi = {10.1007/978-3-642-21434-9_8},
  pages = {191--215}
}
@incollection{LopLiaStu2012ppsn,
  volume = 7491,
  year = 2012,
  address = { Heidelberg, Germany},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  fulleditor = { Carlos A. {Coello Coello}  and Vincenzo Cutello and  Kalyanmoy Deb  and Stephanie
                  Forrest and Giuseppe Nicosia and Mario Pavone},
  editor = { Carlos A. {Coello Coello}  and others},
  booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XII}, Part {I}},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and Liao, Tianjun  and  Thomas St{\"u}tzle },
  title = {On the anytime behavior of {IPOP-CMA-ES}},
  pages = {357--366},
  doi = {10.1007/978-3-642-32937-1_36}
}
@incollection{BezLopStu2012:ants,
  volume = 7461,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg, Germany},
  publisher = {Springer},
  editor = { Marco Dorigo  and others },
  year = 2012,
  booktitle = {Swarm Intelligence, 8th International Conference, ANTS 2012},
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Generation of Multi-Objective {ACO}
                  Algorithms for the Biobjective Knapsack},
  pages = {37--48},
  doi = {10.1007/978-3-642-32650-9_4},
  pdf = {BezLopStu2012ants.pdf},
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2012-008/}
}
@incollection{DubLopStu2012evocop,
  address = { Heidelberg, Germany},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 7245,
  year = 2012,
  editor = { Jin-Kao Hao  and  Martin Middendorf },
  booktitle = {Proceedings of EvoCOP 2012 -- 12th European Conference on Evolutionary Computation in Combinatorial Optimization },
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {{Pareto} Local Search Algorithms for Anytime
                  Bi-objective Optimization},
  pages = {206--217},
  doi = {10.1007/978-3-642-29124-1_18},
  alias = {DubLopStu12:evocop}
}
@incollection{BroLopNau2012ppsn,
  volume = 7491,
  year = 2012,
  address = { Heidelberg, Germany},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  fulleditor = { Carlos A. {Coello Coello}  and Vincenzo Cutello and  Kalyanmoy Deb  and Stephanie
                  Forrest and Giuseppe Nicosia and Mario Pavone},
  editor = { Carlos A. {Coello Coello}  and others},
  booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XII}, Part {I}},
  author = { Dimo Brockhoff  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Boris Naujoks  and  G{\"u}nther Rudolph },
  title = {Runtime Analysis of Simple Interactive Evolutionary
                  Biobjective Optimization Algorithms},
  pages = {123--132},
  doi = {10.1007/978-3-642-32937-1_13},
  abstract = {Development and deployment of interactive evolutionary
                  multiobjective optimization algorithms (EMOAs) have recently
                  gained broad interest. In this study, first steps towards a
                  theory of interactive EMOAs are made by deriving bounds on
                  the expected number of function evaluations and queries to a
                  decision maker. We analyze randomized local search and the
                  (1+1)-EA on the biobjective problems LOTZ and COCZ under the
                  scenario that the decision maker interacts with these
                  algorithms by providing a subjective preference whenever
                  solutions are incomparable. It is assumed that this decision
                  is based on the decision maker's internal utility
                  function. We show that the performance of the interactive
                  EMOAs may dramatically worsen if the utility function is
                  non-linear instead of linear.}
}
@incollection{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}
}
@incollection{StuLopDor2011eorms,
  year = 2011,
  publisher = {John Wiley \& Sons},
  editor = {J. J. Cochran},
  booktitle = {Wiley Encyclopedia of Operations Research and
                  Management Science},
  author = { Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Marco Dorigo },
  title = {A Concise Overview of Applications of Ant Colony
                  Optimization},
  pages = {896--911},
  volume = 2,
  doi = {10.1002/9780470400531.eorms0001}
}
@incollection{LopKnoLau2011emo,
  address = { Heidelberg, Germany},
  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/link/IridiaTr2011-001.pdf}}
}
@incollection{DubLopStu2011gecco,
  address = { New York, NY},
  publisher = {ACM Press},
  year = 2011,
  editor = {Natalio Krasnogor and Pier Luca Lanzi},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2011},
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Configuration of State-of-the-art Multi-Objective
                  Optimizers Using the {TP+PLS} Framework},
  pages = {2019--2026},
  doi = {10.1145/2001576.2001847}
}
@incollection{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{FonGueLopPaq2011emo,
  address = { Heidelberg, Germany},
  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},
  abstract = {The attainment function provides a description of the
                  location of the distribution of a random non-dominated point
                  set. This function can be estimated from experimental data
                  via its empirical counterpart, the empirical attainment
                  function (EAF). However, computation of the EAF in more than
                  two dimensions is a non-trivial task. In this article, the
                  problem of computing the empirical attainment function is
                  formalised, and upper and lower bounds on the corresponding
                  number of output points are presented. In addition, efficient
                  algorithms for the two and three-dimensional cases are
                  proposed, and their time complexities are related to lower
                  bounds derived for each case.},
  pdf = {FonGueLopPaq2011emo.pdf}
}
@incollection{LopStu09ea,
  address = { Heidelberg, Germany},
  publisher = {Springer},
  editor = {Pierre Collet and Nicolas Monmarch{\'e} and Pierrick
                  Legrand and Marc Schoenauer and Evelyne Lutton},
  shorteditor = {Pierre Collet and others},
  volume = 5975,
  series = {Lecture Notes in Computer Science},
  year = 2010,
  booktitle = {Artificial Evolution: 9th International Conference, Evolution Artificielle, EA, 2009},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {An Analysis of Algorithmic Components for
                  Multiobjective Ant Colony Optimization: {A} Case
                  Study on the Biobjective {TSP}},
  pages = {134--145},
  doi = {10.1007/978-3-642-14156-0_12}
}
@incollection{LopStu2010:ants,
  volume = 6234,
  series = {Lecture Notes in Computer Science},
  address = { Heidelberg, Germany},
  publisher = {Springer},
  fulleditor = { Marco Dorigo  and  Mauro Birattari  and  Gianni A. {Di Caro}  and Doursat, R. and Engelbrecht, A. P. and Floreano,
                  D. and Gambardella, L. M. and Gro\ss, R. and Sahin,
                  E. and  Thomas St{\"u}tzle  and Sayama, H.},
  editor = { Marco Dorigo  and others },
  year = 2010,
  booktitle = {Swarm Intelligence, 7th International Conference, ANTS 2010},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Configuration of Multi-Objective {ACO}
                  Algorithms},
  pages = {95--106},
  doi = {10.1007/978-3-642-15461-4_9},
  abstract = {In the last few years a significant number of ant
                  colony optimization (ACO) algorithms have been
                  proposed for tackling multi-objective optimization
                  problems. In this paper, we propose a software
                  framework that allows to instantiate the most
                  prominent multi-objective ACO (MOACO)
                  algorithms. More importantly, the flexibility of
                  this MOACO framework allows the application of
                  automatic algorithm configuration techniques. The
                  experimental results presented in this paper show
                  that such an automatic configuration of MOACO
                  algorithms is highly desirable, given that our
                  automatically configured algorithms clearly
                  outperform the best performing MOACO algorithms that
                  have been proposed in the literature. As far as we
                  are aware, this paper is also the first to apply
                  automatic algorithm configuration techniques to
                  multi-objective stochastic local search algorithms.}
}
@incollection{LopStu2010:gecco,
  address = { New York, NY},
  publisher = {ACM Press},
  year = 2010,
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2010},
  editor = {Martin Pelikan and  J{\"u}rgen Branke },
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {The impact of design choices of multi-objective ant
                  colony optimization algorithms on performance: An
                  experimental study on the biobjective {TSP}},
  doi = {10.1145/1830483.1830494},
  pages = {71--78},
  abstract = {Over the last few years, there have been a number of
                  proposals of ant colony optimization (ACO)
                  algorithms for tackling multiobjective combinatorial
                  optimization problems. These proposals adapt ACO
                  concepts in various ways, for example, some use
                  multiple pheromone matrices and multiple heuristic
                  matrices and others use multiple ant colonies.\\ In
                  this article, we carefully examine several of the
                  most prominent of these proposals. In particular, we
                  identify commonalities among the approaches by
                  recasting the original formulation of the algorithms
                  in different terms. For example, several proposals
                  described in terms of multiple colonies can be cast
                  equivalently using a single ant colony, where ants
                  use different weights for aggregating the pheromone
                  and/or the heuristic information. We study
                  algorithmic choices for the various proposals and we
                  identify previously undetected trade-offs in their
                  performance.}
}
@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.}
}
@incollection{DubLopStu10:lion-bfsp,
  address = { Heidelberg, Germany},
  publisher = {Springer},
  editor = { Christian Blum  and  Roberto Battiti },
  series = {Lecture Notes in Computer Science},
  volume = 6073,
  year = 2010,
  booktitle = {Learning and Intelligent Optimization, 4th International Conference, LION 4},
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Adaptive ``Anytime'' Two-Phase Local Search},
  pages = {52--67},
  doi = {10.1007/978-3-642-13800-3_5}
}
@incollection{LopBlu09:evocop,
  address = { Heidelberg, Germany},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 5482,
  year = 2009,
  editor = { Carlos Cotta  and P. Cowling},
  booktitle = {Proceedings of EvoCOP 2009 -- 9th European Conference on Evolutionary Computation in Combinatorial Optimization },
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Christian Blum  and  Dhananjay Thiruvady  and  Andreas T. Ernst  and  Bernd Meyer },
  title = {Beam-{ACO} based on stochastic sampling for makespan
                  optimization concerning the {TSP} with time windows},
  pages = {97--108},
  pdf = {LopBlu09-Beam-ACO-TSPTW-evocop.pdf},
  doi = {10.1007/978-3-642-01009-5_9},
  alias = {Lop++09}
}
@incollection{LopBlu09:lion,
  address = { Heidelberg, Germany},
  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}
}
@incollection{DubLopStu09:hm-bfsp,
  address = { Heidelberg, Germany},
  publisher = {Springer},
  volume = 5818,
  series = {Lecture Notes in Computer Science},
  editor = { Mar{\'i}a J. Blesa  and  Christian Blum  and Luca {Di Gaspero} and  Andrea Roli  and  M. Sampels  and Andrea Schaerf},
  year = 2009,
  booktitle = {Hybrid Metaheuristics},
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Effective Hybrid Stochastic Local Search Algorithms for
                  Biobjective Permutation Flowshop Scheduling},
  pages = {100--114},
  pdf = {DubLopStu09hm-bfsp.pdf},
  doi = {10.1007/978-3-642-04918-7_8},
  alias = {DuboisHM09}
}
@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},
  pdf = {pap212s1-lopezibanez.pdf}
}
@incollection{PaqStuLop07metaheuristics,
  author = { Lu{\'i}s Paquete  and  Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Using experimental design to analyze stochastic local search
                  algorithms for multiobjective problems},
  booktitle = {Metaheuristics: Progress in Complex Systems Optimization},
  pages = {325--344},
  year = 2007,
  doi = {10.1007/978-0-387-71921-4_17},
  volume = 39,
  series = {Operations Research / Computer Science Interfaces},
  publisher = {Springer},
  address = { New York, NY},
  annote = {Post-Conference Proceedings of the 6th Metaheuristics
                  International Conference (MIC 2005)},
  editor = {Karl F. Doerner and Michel Gendreau and Peter Greistorfer and  Gutjahr, Walter J.  and  Richard F. Hartl  and  Marc Reimann },
  abstract = {Stochastic Local Search (SLS) algorithms can be seen as being
                  composed of several algorithmic components, each playing some
                  specific role with respect to overall performance. This
                  article explores the application of experimental design
                  techniques to analyze the effect of components of SLS
                  algorithms for Multiobjective Combinatorial Optimization
                  problems, in particular for the Biobjective Quadratic
                  Assignment Problem. The analysis shows that there exists a
                  strong dependence between the choices for these components
                  and various instance features, such as the structure of the
                  input data and the correlation between the objectives.}
}
@incollection{LopPaqStu04:ants,
  address = { Heidelberg, Germany},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 3172,
  editor = { Marco Dorigo  and others },
  fulleditor = { Marco Dorigo  and  L. M. Gambardella  and  Francesco Mondada  and  Thomas St{\"u}tzle  and  Mauro Birattari  and  Christian Blum },
  year = 2004,
  booktitle = {Ant Colony Optimization and Swarm Intelligence, 4th
                  International Workshop, ANTS 2004 },
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
  title = {On the Design of {ACO} for the Biobjective Quadratic
                  Assignment Problem},
  pages = {214--225},
  doi = {10.1007/978-3-540-28646-2_19},
  pdf = {LopPaqStu2004antsbQAP.pdf}
}
@book{ANTS2022,
  title = {Swarm Intelligence, 13th International Conference, ANTS 2022,
                  M\'alaga, Spain, November 2-4, 2022, Proceedings},
  booktitle = {Swarm Intelligence, 13th International Conference, ANTS 2022},
  year = 2022,
  editor = { Marco Dorigo  and  Heiko Hamann  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Jos{\'e} Garc{\'i}a-Nieto  and  Andries Engelbrecht  and  Carlo Pinciroli  and  Volker Strobel  and Camacho-Villal\'{o}n, Christian Leonardo},
  publisher = {Springer},
  address = { Cham, Switzerland},
  series = {Lecture Notes in Computer Science},
  volume = 13491,
  doi = {10.1007/978-3-031-20176-9}
}
@book{GECCO2019,
  title = {Proceedings of the Genetic and Evolutionary Computation
                  Conference, {GECCO} 2019, Prague, Czech Republic, July 13-17,
                  2019},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO 2019},
  editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Anne Auger  and  Thomas St{\"u}tzle },
  year = 2019,
  publisher = {ACM Press},
  address = { New York, NY},
  isbn = {978-1-4503-6111-8},
  doi = {10.1145/3321707},
  epub = {https://dl.acm.org/citation.cfm?id=3321707}
}
@book{GECCO2019c,
  title = {Genetic and Evolutionary Computation Conference Companion,
                  {GECCO} 2019, Prague, Czech Republic, July 13-17, 2019},
  booktitle = {Proceedings of  the Genetic and Evolutionary Computation Conference, GECCO Companion 2019},
  editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Anne Auger  and  Thomas St{\"u}tzle },
  year = 2019,
  publisher = {ACM Press},
  address = { New York, NY},
  isbn = {978-1-4503-6748-6},
  doi = {10.1145/3319619},
  epub = {https://dl.acm.org/citation.cfm?id=3319619}
}
@book{EVOCOP2018,
  editor = { Arnaud Liefooghe  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Evolutionary Computation in Combinatorial Optimization --
                  18th European Conference, EvoCOP 2018, Parma, Italy, April
                  4-6, 2018, Proceedings},
  booktitle = {Proceedings of EvoCOP 2018 -- 18th European Conference on Evolutionary Computation in Combinatorial Optimization },
  year = 2018,
  series = {Lecture Notes in Computer Science},
  volume = 10782,
  doi = {10.1007/978-3-319-77449-7},
  publisher = {Springer},
  address = { Heidelberg, Germany}
}
@book{EVOCOP2017,
  editor = { Bin Hu  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Evolutionary Computation in Combinatorial Optimization -- 17th
                  European Conference, EvoCOP 2017, Amsterdam, The Netherlands,
                  April 19-21, 2017, Proceedings},
  booktitle = {Proceedings of EvoCOP 2017 -- 17th European Conference on Evolutionary Computation in Combinatorial Optimization },
  year = 2017,
  series = {Lecture Notes in Computer Science},
  volume = 10197,
  doi = {10.1007/978-3-319-55453-2},
  publisher = {Springer},
  address = { Heidelberg, Germany}
}
@book{ANTS2016,
  title = {Swarm Intelligence, 10th International Conference, ANTS 2016,
                  Brussels, Belgium, September 7-9, 2016, Proceedings},
  booktitle = {Swarm Intelligence, 10th International Conference, ANTS 2016},
  year = 2016,
  editor = { 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 },
  publisher = {Springer},
  address = { Heidelberg, Germany},
  series = {Lecture Notes in Computer Science},
  volume = 9882,
  doi = {10.1007/978-3-319-44427-7}
}
@book{PPSN2016,
  booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XIV}},
  title = {Parallel Problem Solving from Nature - PPSN XIV 14th
                  International Conference, Edinburgh, UK, September 17-21,
                  2016, Proceedings},
  editor = { Julia Handl  and  Emma Hart  and  Lewis, P. R.  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Gabriela Ochoa  and  Ben Paechter },
  series = {Lecture Notes in Computer Science},
  publisher = {Springer},
  address = { Heidelberg, Germany},
  volume = 9921,
  year = 2016,
  doi = {10.1007/978-3-319-45823-6},
  isbn = {978-3-319-45822-9}
}
@article{BliCosRefZha2023aitsp,
  title = {The First {AI4TSP} Competition: Learning to Solve Stochastic
                  Routing Problems},
  journal = {Artificial Intelligence},
  pages = 103918,
  volume = 319,
  year = 2023,
  issn = {0004-3702},
  doi = {10.1016/j.artint.2023.103918},
  author = {Laurens Bliek and Paulo {da Costa} and Reza {Refaei Afshar}
                  and Robbert Reijnen and Yingqian Zhang and Tom Catshoek and
                  Dani{\"e}l Vos and Sicco Verwer and Fynn Schmitt-Ulms and
                  Andr{\'e} Hottung and Tapan Shah and  Meinolf Sellmann  and  Kevin Tierney  and Carl Perreault-Lafleur and Caroline Leboeuf
                  and Federico Bobbio and Justine Pepin and Warley Almeida
                  Silva and Ricardo Gama and Hugo L. Fernandes and  Martin Zaefferer  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Irurozki, Ekhine },
  keywords = {AI for TSP competition, Travelling salesman problem, Routing
                  problem, Stochastic combinatorial optimization,
                  Surrogate-based optimization, Deep reinforcement learning},
  abstract = {This paper reports on the first international competition on
                  AI for the traveling salesman problem (TSP) at the
                  International Joint Conference on Artificial Intelligence
                  2021 (IJCAI-21). The TSP is one of the classical
                  combinatorial optimization problems, with many variants
                  inspired by real-world applications. This first competition
                  asked the participants to develop algorithms to solve an
                  orienteering problem with stochastic weights and time windows
                  (OPSWTW). It focused on two learning approaches:
                  surrogate-based optimization and deep reinforcement
                  learning. In this paper, we describe the problem, the
                  competition setup, and the winning methods, and give an
                  overview of the results. The winning methods described in
                  this work have advanced the state-of-the-art in using AI for
                  stochastic routing problems. Overall, by organizing this
                  competition we have introduced routing problems as an
                  interesting problem setting for AI researchers. The simulator
                  of the problem has been made open-source and can be used by
                  other researchers as a benchmark for new learning-based
                  methods. The instances and code for the competition are
                  available at
                  \url{https://github.com/paulorocosta/ai-for-tsp-competition}.}
}
@article{ShaLopKno2023bench,
  author = { Shavarani, Seyed Mahdi  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Joshua D. Knowles },
  title = {On Benchmarking Interactive Evolutionary Multi-Objective
                  Algorithms},
  journal = {IEEE Transactions on Evolutionary Computation},
  year = 2023,
  doi = {10.1109/TEVC.2023.3289872},
  abstract = {We carry out a detailed performance assessment of two
                  interactive evolutionary multi-objective algorithms (EMOAs)
                  using a machine decision maker that enables us to repeat
                  experiments and study specific behaviours modeled after human
                  decision makers (DMs). Using the same set of benchmark test
                  problems as in the original papers on these interactive EMOAs
                  (in up to 10 objectives), we bring to light interesting
                  effects when we use a machine DM based on sigmoidal utility
                  functions that have support from the psychology literature
                  (replacing the simpler utility functions used in the original
                  papers). Our machine DM enables us to go further and simulate
                  human biases and inconsistencies as well. Our results from
                  this study, which is the most comprehensive assessment of
                  multiple interactive EMOAs so far conducted, suggest that
                  current well-known algorithms have shortcomings that need
                  addressing. These results further demonstrate the value of
                  improving the benchmarking of interactive EMOAs}
}
@article{ShaLopAlm2023hidden,
  author = { Shavarani, Seyed Mahdi  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Allmendinger, Richard },
  title = {Detecting Hidden and Irrelevant Objectives in Interactive
                  Multi-Objective Optimization},
  journal = {IEEE Transactions on Evolutionary Computation},
  year = 2023,
  doi = {10.1109/TEVC.2023.3269348},
  abstract = {Evolutionary multi-objective optimization algorithms (EMOAs)
                  typically assume that all objectives that are relevant to the
                  decision-maker (DM) are optimized by the EMOA. In some
                  scenarios, however, there are irrelevant objectives that are
                  optimized by the EMOA but ignored by the DM, as well as,
                  hidden objectives that the DM considers when judging the
                  utility of solutions but are not optimized. This discrepancy
                  between the EMOA and the DM's preferences may impede the
                  search for the most-preferred solution and waste resources
                  evaluating irrelevant objectives. Research on objective
                  reduction has focused so far on the structure of the problem
                  and correlations between objectives and neglected the role of
                  the DM. We formally define here the concepts of irrelevant
                  and hidden objectives and propose methods for detecting them,
                  based on uni-variate feature selection and recursive feature
                  elimination, that use the preferences already elicited when a
                  DM interacts with a ranking-based interactive EMOA
                  (iEMOA). We incorporate the detection methods into an iEMOA
                  capable of dynamically switching the objectives being
                  optimized. Our experiments show that this approach can
                  efficiently identify which objectives are relevant to the DM
                  and reduce the number of objectives being optimized, while
                  keeping and often improving the utility, according to the DM,
                  of the best solution found.}
}
@article{NebLopGarCoe2023automopso,
  author = { Nebro, Antonio J.  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Jos{\'e} Garc{\'i}a-Nieto  and  Carlos A. {Coello Coello} },
  title = {On the automatic design of multi-objective particle swarm
                  optimizers: experimentation and analysis},
  journal = {Swarm Intelligence},
  year = 2023,
  doi = {10.1007/s11721-023-00227-2},
  abstract = {Research in multi-objective particle swarm optimizers
                  (MOPSOs) progresses by proposing one new MOPSO at a time. In
                  spite of the commonalities among different MOPSOs, it is
                  often unclear which algorithmic components are crucial for
                  explaining the performance of a particular MOPSO
                  design. Moreover, it is expected that different designs may
                  perform best on different problem families and identifying a
                  best overall MOPSO is a challenging task. We tackle this
                  challenge here by: (1) proposing AutoMOPSO, a flexible
                  algorithmic template for designing MOPSOs with a design space
                  that can instantiate thousands of potential MOPSOs; and (2)
                  searching for good-performing MOPSO designs given a family of
                  training problems by means of an automatic configuration tool
                  (irace). We apply this automatic design methodology to
                  generate a MOPSO that significantly outperforms two
                  state-of-the-art MOPSOs on four well-known bi-objective
                  problem families. We also identify the key design choices and
                  parameters of the winning MOPSO by means of
                  ablation. AutoMOPSO is publicly available as part of the
                  jMetal framework.}
}
@article{LiLopYao2023archiving,
  author = { Li, Miqing  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Xin Yao },
  title = {Multi-Objective Archiving},
  journal = {IEEE Transactions on Evolutionary Computation},
  year = 2023,
  doi = {10.1109/TEVC.2023.3314152},
  abstract = {Most multi-objective optimisation algorithms maintain an
                  archive explicitly or implicitly during their search. Such an
                  archive can be solely used to store high-quality solutions
                  presented to the decision maker, but in many cases may
                  participate in the search process (e.g., as the population in
                  evolutionary computation). Over the last two decades,
                  archiving, the process of comparing new solutions with
                  previous ones and deciding how to update the
                  archive/population, stands as an important issue in
                  evolutionary multi-objective optimisation (EMO). This is
                  evidenced by constant efforts from the community on
                  developing various effective archiving methods, ranging from
                  conventional Pareto-based methods to more recent
                  indicator-based and decomposition-based ones. However, the
                  focus of these efforts is on empirical performance comparison
                  in terms of specific quality indicators; there is lack of
                  systematic study of archiving methods from a general
                  theoretical perspective. In this paper, we attempt to conduct
                  a systematic overview of multi-objective archiving, in the
                  hope of paving the way to understand archiving algorithms
                  from a holistic perspective of theory and practice, and more
                  importantly providing a guidance on how to design
                  theoretically desirable and practically useful archiving
                  algorithms. In doing so, we also present that archiving
                  algorithms based on weakly Pareto compliant indicators (e.g.,
                  $\epsilon$-indicator), as long as designed properly, can
                  achieve the same theoretical desirables as archivers based on
                  Pareto compliant indicators (e.g., hypervolume
                  indicator). Such desirables include the property
                  limit-optimal, the limit form of the possible optimal
                  property that a bounded archiving algorithm can have with
                  respect to the most general form of superiority between
                  solution sets.}
}
@article{CinFerLopAlb2022irace,
  author = { Christian Cintrano  and  Javier Ferrer  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Alba, Enrique },
  title = {Hybridization of Evolutionary Operators with Elitist Iterated
                  Racing for the Simulation Optimization of Traffic Lights
                  Programs},
  journal = {Evolutionary Computation},
  year = 2023,
  volume = 31,
  number = 1,
  pages = {31--51},
  doi = {10.1162/evco_a_00314},
  abstract = {In the traffic light scheduling problem the evaluation of
                  candidate solutions requires the simulation of a process
                  under various (traffic) scenarios. Thus, good solutions
                  should not only achieve good objective function values, but
                  they must be robust (low variance) across all different
                  scenarios. Previous work has shown that combining IRACE with
                  evolutionary operators is effective for this task due to the
                  power of evolutionary operators in numerical optimization. In
                  this paper, we further explore the hybridization of
                  evolutionary operators and the elitist iterated racing of
                  IRACE for the simulation-optimization of traffic light
                  programs. We review previous works from the literature to
                  find the evolutionary operators performing the best when
                  facing this problem to propose new hybrid algorithms. We
                  evaluate our approach over a realistic case study derived
                  from the traffic network of Málaga (Spain) with 275 traffic
                  lights that should be scheduled optimally. The experimental
                  analysis reveals that the hybrid algorithm comprising IRACE
                  plus differential evolution offers statistically better
                  results than the other algorithms when the budget of
                  simulations is low. In contrast, IRACE performs better than
                  the hybrids for high simulations budget, although the
                  optimization time is much longer.},
  keywords = {irace, Simulation optimization, Uncertainty, Traffic light
                  planning}
}
@article{MazLopChuMie2023tgp,
  author = { Atanu Mazumdar  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Tinkle Chugh  and  Kaisa Miettinen },
  title = {Treed {Gaussian} Process Regression for Solving Offline
                  Data-Driven Continuous Multiobjective Optimization Problems},
  journal = {Evolutionary Computation},
  year = 2023,
  doi = {10.1162/evco_a_00329},
  abstract = {For offline data-driven multiobjective optimization problems
                  (MOPs), no new data is available during the optimization
                  process. Approximation models (or surrogates) are first built
                  using the provided offline data and an optimizer, e.g. a
                  multiobjective evolutionary algorithm, can then be utilized
                  to find Pareto optimal solutions to the problem with
                  surrogates as objective functions. In contrast to online
                  data-driven MOPs, these surrogates cannot be updated with new
                  data and, hence, the approximation accuracy cannot be
                  improved by considering new data during the optimization
                  process. Gaussian process regression (GPR) models are widely
                  used as surrogates because of their ability to provide
                  uncertainty information. However, building GPRs becomes
                  computationally expensive when the size of the dataset is
                  large. Using sparse GPRs reduces the computational cost of
                  building the surrogates. However, sparse GPRs are not
                  tailored to solve offline data-driven MOPs, where good
                  accuracy of the surrogates is needed near Pareto optimal
                  solutions. Treed GPR (TGPR-MO) surrogates for offline
                  data-driven MOPs with continuous decision variables are
                  proposed in this paper. The proposed surrogates first split
                  the decision space into subregions using regression trees and
                  build GPRs sequentially in regions close to Pareto optimal
                  solutions in the decision space to accurately approximate
                  tradeoffs between the objective functions. TGPR-MO surrogates
                  are computationally inexpensive because GPRs are built only
                  in a smaller region of the decision space utilizing a subset
                  of the data. The TGPR-MO surrogates were tested on
                  distance-based visualizable problems with various data sizes,
                  sampling strategies, numbers of objective functions, and
                  decision variables. Experimental results showed that the
                  TGPR-MO surrogates are computationally cheaper and can handle
                  datasets of large size. Furthermore, TGPR-MO surrogates
                  produced solutions closer to Pareto optimal solutions
                  compared to full GPRs and sparse GPRs.},
  keywords = {Gaussian processes, Kriging, Regression trees, Metamodelling,
                  Surrogate, Pareto optimality}
}
@article{SouRitLop2021cap,
  author = { Marcelo {De Souza}  and  Marcus Ritt and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Capping Methods for the Automatic Configuration of
                  Optimization Algorithms},
  journal = {Computers \& Operations Research},
  doi = {10.1016/j.cor.2021.105615},
  year = 2022,
  volume = 139,
  pages = 105615,
  supplement = {https://github.com/souzamarcelo/supp-cor-capopt},
  abstract = {Automatic configuration techniques are widely and
                  successfully used to find good parameter settings for
                  optimization algorithms. Configuration is costly, because it
                  is necessary to evaluate many configurations on different
                  instances. For decision problems, when the objective is to
                  minimize the running time of the algorithm, many
                  configurators implement capping methods to discard poor
                  configurations early. Such methods are not directly
                  applicable to optimization problems, when the objective is to
                  optimize the cost of the best solution found, given a
                  predefined running time limit. We propose new capping methods
                  for the automatic configuration of optimization
                  algorithms. They use the previous executions to determine a
                  performance envelope, which is used to evaluate new
                  executions and cap those that do not satisfy the envelope
                  conditions. We integrate the capping methods into the irace
                  configurator and evaluate them on different optimization
                  scenarios. Our results show that the proposed methods can
                  save from about 5\% to 78\% of the configuration effort,
                  while finding configurations of the same quality. Based on
                  the computational analysis, we identify two conservative and
                  two aggressive methods, that save an average of about 20\%
                  and 45\% of the configuration effort, respectively. We also
                  provide evidence that capping can help to better use the
                  available budget in scenarios with a configuration time
                  limit.}
}
@article{AyoAllLopPar2022scalarisation,
  author = { Ayodele, Mayowa  and  Allmendinger, Richard  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Parizy, Matthieu },
  title = {A Study of Scalarisation Techniques for Multi-Objective
                  {QUBO} Solving},
  journal = {Arxiv preprint arXiv:2210.11321},
  year = 2022,
  doi = {10.48550/arXiv.2210.11321}
}
@article{RivYanLop2021tweet,
  author = { Rivadeneira, Luc{\'i}a  and  Yang, Jian-Bo  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Predicting tweet impact using a novel evidential reasoning
                  prediction method},
  journal = {Expert Systems with Applications},
  year = 2021,
  volume = 169,
  pages = 114400,
  month = may,
  doi = {10.1016/j.eswa.2020.114400},
  abstract = {This study presents a novel evidential reasoning (ER)
                  prediction model called MAKER-RIMER to examine how different
                  features embedded in Twitter posts (tweets) can predict the
                  number of retweets achieved during an electoral campaign. The
                  tweets posted by the two most voted candidates during the
                  official campaign for the 2017 Ecuadorian Presidential
                  election were used for this research. For each tweet, five
                  features including type of tweet, emotion, URL, hashtag, and
                  date are identified and coded to predict if tweets are of
                  either high or low impact. The main contributions of the new
                  proposed model include its suitability to analyse tweet
                  datasets based on likelihood analysis of data. The model is
                  interpretable, and the prediction process relies only on the
                  use of available data. The experimental results show that
                  MAKER-RIMER performed better, in terms of misclassification
                  error, when compared against other predictive machine
                  learning approaches. In addition, the model allows observing
                  which features of the candidates' tweets are linked to high
                  and low impact. Tweets containing allusions to the contender
                  candidate, either with positive or negative connotations,
                  without hashtags, and written towards the end of the
                  campaign, were persistently those with the highest
                  impact. URLs, on the other hand, is the only variable that
                  performs differently for the two candidates in terms of
                  achieving high impact. MAKER-RIMER can provide campaigners of
                  political parties or candidates with a tool to measure how
                  features of tweets are predictors of their impact, which can
                  be useful to tailor Twitter content during electoral
                  campaigns.},
  keywords = {Evidential reasoning rule,Belief rule-based inference,Maximum
                  likelihood data analysis,Twitter,Retweet,Prediction}
}
@article{ShaLopMie2021visual,
  title = {Visualizations for Decision Support in Scenario-based
                  Multiobjective Optimization},
  author = { Shavazipour, Babooshka  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Kaisa Miettinen },
  journal = {Information Sciences},
  volume = 578,
  pages = {1--21},
  year = 2021,
  abstract = {We address challenges of decision problems when managers need
                  to optimize several conflicting objectives simultaneously
                  under uncertainty. We propose visualization tools to support
                  the solution of such scenario-based multiobjective
                  optimization problems. Suitable graphical visualizations are
                  necessary to support managers in understanding, evaluating,
                  and comparing the performances of management decisions
                  according to all objectives in all plausible scenarios. To
                  date, no appropriate visualization has been suggested. This
                  paper fills this gap by proposing two visualization methods:
                  a novel extension of empirical attainment functions for
                  scenarios and an adapted version of heatmaps. They help a
                  decision-maker in gaining insight into realizations of
                  trade-offs and comparisons between objective functions in
                  different scenarios. Some fundamental questions that a
                  decision-maker may wish to answer with the help of
                  visualizations are also identified. Several examples are
                  utilized to illustrate how the proposed visualizations
                  support a decision-maker in evaluating and comparing
                  solutions to be able to make a robust decision by answering
                  the questions. Finally, we validate the usefulness of the
                  proposed visualizations in a real-world problem with a real
                  decision-maker. We conclude with guidelines regarding which
                  of the proposed visualizations are best suited for different
                  problem classes.},
  doi = {10.1016/j.ins.2021.07.025},
  supplement = {https://doi.org/10.5281/zenodo.5040421}
}
@article{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{LopBraPaq2021arxiv,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  J{\"u}rgen Branke  and  Lu{\'i}s Paquete },
  title = {Reproducibility in Evolutionary Computation},
  journal = {Arxiv preprint arXiv:20102.03380 [cs.AI]},
  year = 2021,
  url = {https://arxiv.org/abs/2102.03380},
  abstract = {Experimental studies are prevalent in Evolutionary
                  Computation (EC), and concerns about the reproducibility and
                  replicability of such studies have increased in recent times,
                  reflecting similar concerns in other scientific fields. In
                  this article, we suggest a classification of different types
                  of reproducibility that refines the badge system of the
                  Association of Computing Machinery (ACM) adopted by TELO. We
                  discuss, within the context of EC, the different types of
                  reproducibility as well as the concepts of artifact and
                  measurement, which are crucial for claiming
                  reproducibility. We identify cultural and technical obstacles
                  to reproducibility in the EC field. Finally, we provide
                  guidelines and suggest tools that may help to overcome some
                  of these reproducibility obstacles.},
  keywords = {Evolutionary Computation, Reproducibility, Empirical study,
                  Benchmarking}
}
@article{LopBraPaq2021telo,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  J{\"u}rgen Branke  and  Lu{\'i}s Paquete },
  title = {Reproducibility in Evolutionary Computation},
  journal = {ACM Transactions on Evolutionary Learning and Optimization},
  year = 2021,
  volume = 1,
  number = 4,
  pages = {1--21},
  doi = {10.1145/3466624},
  epub = {https://arxiv.org/abs/2102.03380},
  abstract = {Experimental studies are prevalent in Evolutionary
                  Computation (EC), and concerns about the reproducibility and
                  replicability of such studies have increased in recent times,
                  reflecting similar concerns in other scientific fields. In
                  this article, we suggest a classification of different types
                  of reproducibility that refines the badge system of the
                  Association of Computing Machinery (ACM) adopted by TELO. We
                  discuss, within the context of EC, the different types of
                  reproducibility as well as the concepts of artifact and
                  measurement, which are crucial for claiming
                  reproducibility. We identify cultural and technical obstacles
                  to reproducibility in the EC field. Finally, we provide
                  guidelines and suggest tools that may help to overcome some
                  of these reproducibility obstacles.},
  keywords = {Evolutionary Computation, Reproducibility, Empirical study,
                  Benchmarking}
}
@article{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{AvrAllLop2021arxiv,
  author = { Andreea Avramescu  and  Allmendinger, Richard  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Managing Manufacturing and Delivery of Personalised Medicine:
                  Current and Future Models},
  year = 2021,
  journal = {Arxiv preprint arXiv:2105.12699 [econ.GN]},
  url = {https://arxiv.org/abs/2105.12699}
}
@article{LopPerStu2020ifors,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and   P{\'e}rez C{\'a}ceres, Leslie and  Thomas St{\"u}tzle },
  title = {{irace}: A Tool for the Automatic Configuration of
                  Algorithms},
  journal = {International Federation of Operational Research Societies
                  (IFORS) News},
  year = 2020,
  volume = 14,
  number = 2,
  pages = {30--32},
  month = jun,
  url = {https://www.ifors.org/newsletter/ifors-news-june2020.pdf}
}
@article{BarDoeBer2020benchmarking,
  title = {Benchmarking in Optimization: Best Practice and Open Issues},
  author = { Thomas Bartz-Beielstein  and  Carola Doerr  and Daan van den Berg and  Jakob Bossek  and Sowmya Chandrasekaran and  Tome Eftimov  and Andreas Fischbach and  Pascal Kerschke  and William {La
                  Cava} and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Katherine M. Malan and Jason H. Moore and  Boris Naujoks  and Patryk Orzechowski and Vanessa Volz and  Markus Wagner  and Thomas Weise},
  year = 2020,
  journal = {Arxiv preprint arXiv:2007.03488 [cs.NE]},
  url = {https://arxiv.org/abs/2007.03488}
}
@article{StrLopBroLee2020,
  title = {General Northern English: Exploring regional variation in the
                  North of England with machine learning},
  author = { Strycharczuk, Patrycja  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Brown, Georgina  and  Adrian Leemann },
  journal = { Frontiers in Artificial Intelligence },
  year = 2020,
  volume = 3,
  number = 48,
  keywords = {vowels, accent features, dialect leveling, Random forest
                  (bagging), Feature selecion},
  doi = {10.3389/frai.2020.00048},
  abstract = {In this paper, we present a novel computational approach to
                  the analysis of accent variation. The case study is dialect
                  leveling in the North of England, manifested as reduction of
                  accent variation across the North and emergence of General
                  Northern English (GNE), a pan-regional standard accent
                  associated with middle-class speakers. We investigated this
                  instance of dialect leveling using random forest
                  classification, with audio data from a crowd-sourced corpus
                  of 105 urban, mostly highly-educated speakers from five
                  northern UK cities: Leeds, Liverpool, Manchester, Newcastle
                  upon Tyne, and Sheffield. We trained random forest models to
                  identify individual northern cities from a sample of other
                  northern accents, based on first two formant measurements of
                  full vowel systems. We tested the models using unseen
                  data. We relied on undersampling, bagging (bootstrap
                  aggregation) and leave-one-out cross-validation to address
                  some challenges associated with the data set, such as
                  unbalanced data and relatively small sample size. The
                  accuracy of classification provides us with a measure of
                  relative similarity between different pairs of cities, while
                  calculating conditional feature importance allows us to
                  identify which input features (which vowels and which
                  formants) have the largest influence in the prediction. We do
                  find a considerable degree of leveling, especially between
                  Manchester, Leeds and Sheffield, although some differences
                  persist. The features that contribute to these differences
                  most systematically are typically not the ones discussed in
                  previous dialect descriptions. We propose that the most
                  systematic regional features are also not salient, and as
                  such, they serve as sociolinguistic regional indicators. We
                  supplement the random forest results with a more traditional
                  variationist description of by-city vowel systems, and we use
                  both sources of evidence to inform a description of the
                  vowels of General Northern English.}
}
@article{BezLopStu2019ec,
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatically Designing State-of-the-Art Multi- and
                  Many-Objective Evolutionary Algorithms},
  journal = {Evolutionary Computation},
  year = 2020,
  volume = 28,
  number = 2,
  pages = {195--226},
  doi = {10.1162/evco_a_00263},
  supplement = {https://github.com/iridia-ulb/automoea-ecj-2020},
  pdf = {BezLopStu2019ec.pdf},
  abstract = {A recent comparison of well-established multiobjective
                  evolutionary algorithms (MOEAs) has helped better identify
                  the current state-of-the-art by considering (i) parameter
                  tuning through automatic configuration, (ii) a wide range of
                  different setups, and (iii) various performance
                  metrics. Here, we automatically devise MOEAs with verified
                  state-of-the-art performance for multi- and many-objective
                  continuous optimization. Our work is based on two main
                  considerations. The first is that high-performing algorithms
                  can be obtained from a configurable algorithmic framework in
                  an automated way. The second is that multiple performance
                  metrics may be required to guide this automatic design
                  process. In the first part of this work, we extend our
                  previously proposed algorithmic framework, increasing the
                  number of MOEAs, underlying evolutionary algorithms, and
                  search paradigms that it comprises. These components can be
                  combined following a general MOEA template, and an automatic
                  configuration method is used to instantiate high-performing
                  MOEA designs that optimize a given performance metric and
                  present state-of-the-art performance. In the second part, we
                  propose a multiobjective formulation for the automatic MOEA
                  design, which proves critical for the context of
                  many-objective optimization due to the disagreement of
                  established performance metrics. Our proposed formulation
                  leads to an automatically designed MOEA that presents
                  state-of-the-art performance according to a set of metrics,
                  rather than a single one.}
}
@article{KimAllLop2020arxiv,
  author = { Kim, Youngmin  and  Allmendinger, Richard  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Safe Learning and Optimization Techniques: Towards a Survey
                  of the State of the Art},
  journal = {Arxiv preprint arXiv:2101.09505 [cs.LG]},
  year = 2020,
  url = {https://arxiv.org/abs/2101.09505},
  abstract = {Safe learning and optimization deals with learning and
                  optimization problems that avoid, as much as possible, the
                  evaluation of non-safe input points, which are solutions,
                  policies, or strategies that cause an irrecoverable loss
                  (e.g., breakage of a machine or equipment, or life
                  threat). Although a comprehensive survey of safe
                  reinforcement learning algorithms was published in 2015, a
                  number of new algorithms have been proposed thereafter, and
                  related works in active learning and in optimization were not
                  considered. This paper reviews those algorithms from a number
                  of domains including reinforcement learning, Gaussian process
                  regression and classification, evolutionary algorithms, and
                  active learning. We provide the fundamental concepts on which
                  the reviewed algorithms are based and a characterization of
                  the individual algorithms. We conclude by explaining how the
                  algorithms are connected and suggestions for future
                  research. }
}
@article{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{FerLopAlb2019asoc,
  author = { Javier Ferrer  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Alba, Enrique },
  title = {Reliable Simulation-Optimization of Traffic Lights in a
                  Real-World City},
  journal = {Applied Soft Computing},
  year = 2019,
  volume = 78,
  pages = {697--711},
  doi = {10.1016/j.asoc.2019.03.016},
  pdf = {FerLopAlb2019asoc.pdf},
  supplement = {https://github.com/MLopez-Ibanez/irace-sumo}
}
@article{WesLop2018ecj,
  title = {Latin Hypercube Designs with Branching and Nested Factors for
                  Initialization of Automatic Algorithm Configuration},
  author = { Simon Wessing  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  doi = {10.1162/evco_a_00241},
  journal = {Evolutionary Computation},
  year = 2018,
  pdf = {WesLop2018ecj.pdf},
  volume = 27,
  number = 1,
  pages = {129--145}
}
@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/},
  pdf = {BezLopStu2017assessment.pdf},
  volume = 26,
  number = 4,
  pages = {621--656},
  alias = {BezLopStu2016assessment},
  abstract = {Research on multi-objective evolutionary algorithms (MOEAs)
                  has produced over the past decades a large number of
                  algorithms and a rich literature on performance assessment
                  tools to evaluate and compare them. Yet, newly proposed MOEAs
                  are typically compared against very few, often a decade older
                  MOEAs. One reason for this apparent contradiction is the lack
                  of a common baseline for comparison, with each subsequent
                  study often devising its own experimental scenario, slightly
                  different from other studies. As a result, the state of the
                  art in MOEAs is a disputed topic. This article reports a
                  systematic, comprehensive evaluation of a large number of
                  MOEAs that covers a wide range of experimental scenarios. A
                  novelty of this study is the separation between the
                  higher-level algorithmic components related to
                  multi-objective optimization (MO), which characterize each
                  particular MOEA, and the underlying parameters-such as
                  evolutionary operators, population size, etc.-whose
                  configuration may be tuned for each scenario. Instead of
                  relying on a common or "default" parameter configuration that
                  may be low-performing for particular MOEAs or scenarios and
                  unintentionally biased, we tune the parameters of each MOEA
                  for each scenario using automatic algorithm configuration
                  methods. Our results confirm some of the assumed knowledge in
                  the field, while at the same time they provide new insights
                  on the relative performance of MOEAs for many-objective
                  problems. For example, under certain conditions,
                  indicator-based MOEAs are more competitive for such problems
                  than previously assumed. We also analyze problem-specific
                  features affecting performance, the agreement between
                  performance metrics, and the improvement of tuned
                  configurations over the default configurations used in the
                  literature. Finally, the data produced is made publicly
                  available to motivate further analysis and a baseline for
                  future comparisons.}
}
@article{KabColKorLop2017jacryst,
  author = { Kabova, Elena A.  and  Cole, Jason C.  and  Oliver Korb  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Williams, Adrian C.  and  Shankland, Kenneth },
  title = {Improved performance of crystal structure solution from
                  powder diffraction data through parameter tuning of a
                  simulated annealing algorithm},
  journal = {Journal of Applied Crystallography},
  year = 2017,
  volume = 50,
  number = 5,
  pages = {1411--1420},
  month = oct,
  doi = {10.1107/S1600576717012602},
  abstract = {Significant gains in the performance of the simulated
                  annealing algorithm in the {\it DASH} software package have
                  been realized by using the {\it irace} automatic
                  configuration tool to optimize the values of three key
                  simulated annealing parameters. Specifically, the success
                  rate in finding the global minimum in intensity $\chi^2$
                  space is improved by up to an order of magnitude. The general
                  applicability of these revised simulated annealing parameters
                  is demonstrated using the crystal structure determinations of
                  over 100 powder diffraction datasets.},
  keywords = {crystal structure determination, powder diffraction,
                  simulated annealing, parameter tuning, irace}
}
@article{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{LopKesStu2017:cim,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Marie-El{\'e}onore Kessaci  and  Thomas St{\"u}tzle },
  title = {Automatic Design of Hybrid Metaheuristics from Algorithmic Components},
  journal = {Submitted},
  year = 2017,
  optvolume = {},
  optnumber = {},
  optpages = {}
}
@article{LopDubPerStuBir2016irace,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  J{\'e}r{\'e}mie Dubois-Lacoste  and   P{\'e}rez C{\'a}ceres, Leslie and  Thomas St{\"u}tzle  and  Mauro Birattari },
  title = {The {\rpackage{irace}} Package: Iterated Racing for Automatic
                  Algorithm Configuration},
  journal = {Operations Research Perspectives},
  year = 2016,
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2016-003/},
  doi = {10.1016/j.orp.2016.09.002},
  volume = 3,
  pages = {43--58}
}
@article{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 = {https://github.com/iridia-ulb/automoea-tevc-2016},
  pdf = {BezLopStu2015tec.pdf}
}
@article{BluPinLopLoz2015cor,
  author = { Christian Blum  and  Pedro Pinacho  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Jos{\'e} A. Lozano },
  title = {Construct, Merge, Solve \& Adapt: A New General Algorithm for
                  Combinatorial Optimization},
  journal = {Computers \& Operations Research},
  year = 2016,
  volume = 68,
  pages = {75--88},
  doi = {10.1016/j.cor.2015.10.014},
  keywords = {irace, CMSA}
}
@article{TriLop2015plos,
  author = { Vito Trianni  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Advantages of Task-Specific Multi-Objective Optimisation in
                  Evolutionary Robotics},
  journal = {PLoS One},
  year = 2015,
  volume = 10,
  number = 8,
  pages = {e0136406},
  doi = {10.1371/journal.pone.0136406},
  abstract = {The application of multi-objective optimisation to
                  evolutionary robotics is receiving increasing attention. A
                  survey of the literature reveals the different possibilities
                  it offers to improve the automatic design of efficient and
                  adaptive robotic systems, and points to the successful
                  demonstrations available for both task-specific and
                  task-agnostic approaches (i.e., with or without reference to
                  the specific design problem to be tackled). However, the
                  advantages of multi-objective approaches over
                  single-objective ones have not been clearly spelled out and
                  experimentally demonstrated. This paper fills this gap for
                  task-specific approaches: starting from well-known results in
                  multi-objective optimisation, we discuss how to tackle
                  commonly recognised problems in evolutionary robotics. In
                  particular, we show that multi-objective optimisation (i)
                  allows evolving a more varied set of behaviours by exploring
                  multiple trade-offs of the objectives to optimise, (ii)
                  supports the evolution of the desired behaviour through the
                  introduction of objectives as proxies, (iii) avoids the
                  premature convergence to local optima possibly introduced by
                  multi-component fitness functions, and (iv) solves the
                  bootstrap problem exploiting ancillary objectives to guide
                  evolution in the early phases. We present an experimental
                  demonstration of these benefits in three different case
                  studies: maze navigation in a single robot domain, flocking
                  in a swarm robotics context, and a strictly collaborative
                  task in collective robotics.}
}
@article{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},
  pdf = {DubLopStu2015ejor.pdf},
  alias = {DubLopStu2013cor},
  keywords = {Pareto local search}
}
@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},
  pdf = {PerLopStu2015si.pdf},
  volume = 9,
  number = {2-3},
  pages = {103--124}
}
@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},
  pdf = {LopStu2014ejor.pdf},
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2012-011/},
  abstract = {Optimisation algorithms with good anytime behaviour try to
                  return as high-quality solutions as possible independently of
                  the computation time allowed. Designing algorithms with good
                  anytime behaviour is a difficult task, because performance is
                  often evaluated subjectively, by plotting the trade-off curve
                  between computation time and solution quality. Yet, the
                  trade-off curve may be modelled also as a set of mutually
                  nondominated, bi-objective points. Using this model, we
                  propose to combine an automatic configuration tool and the
                  hypervolume measure, which assigns a single quality measure
                  to a nondominated set. This allows us to improve the anytime
                  behaviour of optimisation algorithms by means of
                  automatically finding algorithmic configurations that produce
                  the best nondominated sets. Moreover, the recently proposed
                  weighted hypervolume measure is used here to incorporate the
                  decision-maker's preferences into the automatic tuning
                  procedure. We report on the improvements reached when
                  applying the proposed method to two relevant scenarios: (i)
                  the design of parameter variation strategies for MAX-MIN Ant
                  System and (ii) the tuning of the anytime behaviour of SCIP,
                  an open-source mixed integer programming solver with more
                  than 200 parameters.}
}
@article{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},
  pdf = {MasLopDubStu2014cor.pdf},
  volume = 51,
  pages = {190--199}
}
@article{LopBlu2013asoc,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Christian Blum  and  Jeffrey W. Ohlmann  and  Barrett W. Thomas },
  title = {The Travelling Salesman Problem with Time Windows:
                  Adapting Algorithms from Travel-time to Makespan
                  Optimization},
  journal = {Applied Soft Computing},
  year = 2013,
  volume = 13,
  number = 9,
  pages = {3806--3815},
  doi = {10.1016/j.asoc.2013.05.009},
  epub = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2013-011.pdf}
}
@article{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{LopPraPae2011ec,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  T. Devi Prasad  and  Ben Paechter },
  title = {Representations and Evolutionary Operators for the
                  Scheduling of Pump Operations in Water Distribution
                  Networks},
  journal = {Evolutionary Computation},
  year = 2011,
  doi = {10.1162/EVCO_a_00035},
  volume = 19,
  number = 3,
  pages = {429--467},
  abstract = {Reducing the energy consumption of water
                  distribution networks has never had more
                  significance. The greatest energy savings can be
                  obtained by carefully scheduling the operations of
                  pumps. Schedules can be defined either implicitly,
                  in terms of other elements of the network such as
                  tank levels, or explicitly by specifying the time
                  during which each pump is on/off.  The traditional
                  representation of explicit schedules is a string of
                  binary values with each bit representing pump on/off
                  status during a particular time interval.  In this
                  paper, we formally define and analyze two new
                  explicit representations based on time-controlled
                  triggers, where the maximum number of pump switches
                  is established beforehand and the schedule may
                  contain less switches than the maximum. In these
                  representations, a pump schedule is divided into a
                  series of integers with each integer representing
                  the number of hours for which a pump is
                  active/inactive.  This reduces the number of
                  potential schedules compared to the binary
                  representation, and allows the algorithm to operate
                  on the feasible region of the search space.  We
                  propose evolutionary operators for these two new
                  representations. The new representations and their
                  corresponding operations are compared with the two
                  most-used representations in pump scheduling,
                  namely, binary representation and level-controlled
                  triggers. A detailed statistical analysis of the
                  results indicates which parameters have the greatest
                  effect on the performance of evolutionary
                  algorithms. The empirical results show that an
                  evolutionary algorithm using the proposed
                  representations improves over the results obtained
                  by a recent state-of-the-art Hybrid Genetic
                  Algorithm for pump scheduling using level-controlled
                  triggers.}
}
@article{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},
  pdf = {DubLopStu2011amai.pdf}
}
@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/},
  pdf = {DubLopStu2011cor.pdf}
}
@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{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{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},
  pdf = {BluBleLop09-BeamSearch-LCS.pdf},
  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{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}},
  pdf = {LopezPrasadPaechter08-jwrpm.pdf},
  epub = {http://link.aip.org/link/?QWR/134/337/1},
  doi = {10.1061/(ASCE)0733-9496(2008)134:4(337)},
  abstract = {Reducing energy consumption of water distribution
  networks has never had more significance than today. The greatest
  energy savings can be obtained by careful scheduling of operation of
  pumps. Schedules can be defined either implicitly, in terms of other
  elements of the network such as tank levels, or explicitly by
  specifying the time during which each pump is on/off. The
  traditional representation of explicit schedules is a string of
  binary values with each bit representing pump on/off status during a
  particular time interval. In this paper a new explicit
  representation is presented. It is based on time controlled
  triggers, where the maximum number of pump switches is specified
  beforehand. In this representation a pump schedule is divided into a
  series of integers with each integer representing the number of
  hours for which a pump is active/inactive. This reduces the number
  of potential schedules (search space) compared to the binary
  representation. Ant colony optimization (ACO) is a stochastic
  meta-heuristic for combinatorial optimization problems that is
  inspired by the foraging behavior of some species of ants. In this
  paper, an application of the ACO framework was developed for the
  optimal scheduling of pumps. The proposed representation was adapted
  to an ant colony Optimization framework and solved for the optimal
  pump schedules. Minimization of electrical cost was considered as
  the objective, while satisfying system constraints. Instead of using
  a penalty function approach for constraint violations, constraint
  violations were ordered according to their importance and solutions
  were ranked based on this order. The proposed approach was tested on
  a small test network and on a large real-world network. Results are
  compared with those obtained using a simple genetic algorithm based
  on binary representation and a hybrid genetic algorithm that uses
  level-based triggers.}
}
@article{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},
  pdf = {LopPaqStu04-techrepAIDA-04-11.pdf},
  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.}
}