# LopezIbanez.bib

@comment{{This file has been generated by bib2bib 1.99}}

@comment{{Command line: bib2bib --expand --expand-xrefs -s $type -s year -s month -s author -r -c 'author : "L.*pez-Ib.*.*ez" or (editor : "L.*pez-Ib.*.*ez" and ($type : "BOOK"))' authors.bib cvcustom.bib abbrev.bib journals.bib biblio.bib crossref.bib -c '! $key : "Lop07:HPC_ACO"' -c '!$key : "LopStu2010:gecco-supp"' -c '! $key : "DubLopStu10:journal-anytime-supp"' -c '!$key : "DubLopStu10:journal-bfsp-supp"' -c '! $key : "LopStu2011moaco-supp"' -c '!$key : ".*supp"' -c '! $key : "IridiaSupp.*"' -ob LopezIbanez.bib -oc citefile}}  @preamble{{\providecommand{\MaxMinAntSystem}{{$\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} # {\providecommand{\softwarepackage}[1]{{#1}} } # {\providecommand{\proglang}[1]{{#1}} }}  @unpublished{CamTriLop2017pseudo, author = {Felipe Campelo and \'Athila R. Trindade and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }, title = {Pseudoreplication in Racing Methods for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/IridiaTr2018-001.pdf}, note = {Published as \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 \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/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 Operations 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/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/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/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/IridiaTr2009-026r001.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/IridiaTr2010-019r001.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/IridiaTr2010-022r001.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/IridiaTr2009-020r001.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/IridiaTr2005-029r001.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 = {http://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{BezLopStu2016automoea2, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {Automatically designing and understanding evolutionary algorithms for multi- and many-objective optimization}, year = 2016, note = {To be submitted} }  @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}, address = {London, UK}, editor = {Iv{\'a}n Palomares}, booktitle = {International Alan Turing Conference on Decision Support and Recommender systems}, year = 2019, 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. } }  @inproceedings{BezLopStu2015moead, editor = { Ant{\'o}nio Gaspar{-}Cunha and Carlos Henggeler Antunes and Carlos A. {Coello Coello} }, volume = 9018, year = 2015, series = {Lecture Notes in Computer Science}, 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} }  @inproceedings{LopMasMarStu2013mista, year = 2013, editor = { Graham Kendall and Greet Vanden Berghe and Barry McCollum}, address = {Gent, Belgium}, booktitle = {Multidisciplinary International Conference on Scheduling: Theory and Applications (MISTA 2013)}, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Franco Mascia and Marie-El{\'e}onore Marmion and Thomas St{\"u}tzle }, title = {Automatic Design of a Hybrid Iterated Local Search for the Multi-Mode Resource-Constrained Multi-Project Scheduling Problem}, pages = {1--6}, annote = {\url{https://hal.inria.fr/hal-01094681}}, pdf = {LopMasMarStu2013mista.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{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{ChuLop2021gecco, address = { New York, NY }, publisher = {ACM Press}, year = 2021, editor = { Chicano, Francisco and Krzysztof Krawiec }, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO Companion 2021}, author = { Tinkle Chugh and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }, title = {Maximising Hypervolume and Minimising$\epsilon$-Indicators using Bayesian Optimisation over Sets}, doi = {10.1145/3449726.3463178}, keywords = {multi-objective, surrogate models, epsilon, hypervolume}, supplement = {https://doi.org/10.5281/zenodo.4675569} }  @incollection{ShaLopKno2021gecco, address = { New York, NY }, publisher = {ACM Press}, year = 2021, editor = { Chicano, Francisco and Krzysztof Krawiec }, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021}, title = {Realistic Utility Functions Prove Difficult for State-of-the-ArtInteractive Multiobjective Optimization Algorithms}, author = { Shavarani, Seyed Mahdi and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Joshua D. Knowles }, doi = {10.1145/3449639.3459373} }  @incollection{IruLop2021gecco, address = { New York, NY }, publisher = {ACM Press}, year = 2021, editor = { Chicano, Francisco and Krzysztof Krawiec }, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021}, author = { Irurozki, Ekhine and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }, title = {Unbalanced Mallows Models for Optimizing Expensive Black-Box Permutation Problems}, doi = {10.1145/3449639.3459366}, supplement = {https://zenodo.org/record/4500974}, abstract = {Expensive black-box combinatorial optimization problems arise in practice when the objective function is evaluated by means of a simulator or a real-world experiment. Since each fitness evaluation is expensive in terms of time or resources, only a limited number of evaluations is possible, typically several orders of magnitude smaller than in non-expensive problems. In this scenario, classical optimization methods such as mixed-integer programming and local search are not useful. In the continuous case, Bayesian optimization, in particular using Gaussian processes, has proven very effective under these conditions. Much less research is available in the combinatorial case. In this paper, we propose and analyze UMM, an estimation-of-distribution (EDA) algorithm based on a Mallows probabilistic model and unbalanced rank aggregation (uBorda). Experimental results on black-box versions of LOP and PFSP show that UMM is able to match, and sometimes surpass, the solutions obtained by CEGO, a Bayesian optimization algorithm for combinatorial optimization. Moreover, the computational complexity of UMM increases linearly with both the number of function evaluations and the permutation size.} }  @incollection{CinFerLopAl2021evocop, address = { Cham, Switzerland }, publisher = {Springer}, volume = 12692, series = {Lecture Notes in Computer Science}, year = 2021, booktitle = {Proceedings of EvoCOP 2021 -- 21th European Conference on Evolutionary Computation in Combinatorial Optimization }, editor = { Christine Zarges and Verel, S{\'e}bastien }, author = { Christian Cintrano and Javier Ferrer and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Alba, Enrique }, title = {Hybridization of Racing Methods with Evolutionary Operators for Simulation Optimization of Traffic Lights Programs}, abstract = {In many real-world optimization problems, like the traffic light scheduling problem tackled here, the evaluation of candidate solu- tions requires the simulation of a process under various scenarios. Thus, good solutions should not only achieve good objective function values, but they must be robust (low variance) across all different scenarios. Previous work has revealed the effectiveness of IRACE for this task. However, the operators used by IRACE to generate new solutions were designed for configuring algorithmic parameters, that have various data types (categorical, numerical, etc.). Meanwhile, evolutionary algorithms have powerful operators for numerical optimization, which could help to sample new solutions from the best ones found in the search. Therefore, in this work, we propose a hybridization of the elitist iterated racing mechanism of IRACE with evolutionary operators from differential evo- lution and genetic algorithms. We consider a realistic case study derived from the traffic network of Malaga (Spain) with 275 traffic lights that should be scheduled optimally. After a meticulous study, we discovered that the hybrid algorithm comprising IRACE plus differential evolution offers statistically better results than conventional algorithms and also improves travel times and reduces pollution.}, keywords = {Hybrid algorithms, Evolutionary algorithms, Simulation optimization, Uncertainty, Traffic light planning}, pages = {17--33}, doi = {10.1007/978-3-030-72904-2_2} }  @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 Juan Luis {Jiménez Laredo}}, 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{KimAllLop2020safe, year = 2020, volume = 12641, address = { Cham, Switzerland }, publisher = {Springer}, series = {Lecture Notes in Computer Science}, booktitle = {Trustworthy AI -- Integrating Learning, Optimization and Reasoning. TAILOR 2020}, editor = {Fredrik Heintz and Michela Milano and Barry O'Sullivan}, author = { Kim, Youngmin and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }, title = {Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art}, pages = {123--139}, doi = {10.1007/978-3-030-73959-1_12}, abstract = {Safe learning and optimization deals with learning and optimization problems that avoid, as much as possible, the evaluation of non-safe input points, which are solutions, policies, or strategies that cause an irrecoverable loss (e.g., breakage of a machine or equipment, or life threat). Although a comprehensive survey of safe reinforcement learning algorithms was published in 2015, a number of new algorithms have been proposed thereafter, and related works in active learning and in optimization were not considered. This paper reviews those algorithms from a number of domains including reinforcement learning, Gaussian process regression and classification, evolutionary computing, and active learning. We provide the fundamental concepts on which the reviewed algorithms are based and a characterization of the individual algorithms. We conclude by explaining how the algorithms are connected and suggestions for future research.} }  @incollection{NebLopBarGar2019gecco, aurl = {https://dl.acm.org/citation.cfm?id=3319619}, isbn = {978-1-4503-6748-6}, address = { New York, NY }, publisher = {ACM Press}, year = 2019, editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle }, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO Companion 2019}, title = {Automatic Configuration of {NSGA-II} with {jMetal} and irace}, author = {Antonio J. Nebro 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{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} }  @incollection{SaiLopMie2019gecco, aurl = {https://dl.acm.org/citation.cfm?id=3319619}, isbn = {978-1-4503-6748-6}, address = { New York, NY }, publisher = {ACM Press}, year = 2019, editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle }, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO Companion 2019}, author = { 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{ShaKomLopKaz2019gecco, aurl = {https://dl.acm.org/citation.cfm?id=3321707}, isbn = {978-1-4503-6111-8}, address = { New York, NY }, publisher = {ACM Press}, year = 2019, editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle }, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019}, title = {Deep Reinforcement Learning-Based Parameter Control in Differential Evolution}, author = { Mudita Sharma and Alexandros Komninos and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Dimitar Kazakov }, supplement = {https://dx.doi.org/10.5281/zenodo.2628228}, doi = {10.1145/3321707.3321813}, pdf = {ShaKomLopKaz2019gecco.pdf}, keywords = {DE-DDQN} }  @incollection{BezLopStu2019gecco, aurl = {https://dl.acm.org/citation.cfm?id=3321707}, isbn = {978-1-4503-6111-8}, address = { New York, NY }, publisher = {ACM Press}, year = 2019, editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle }, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019}, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {Archiver Effects on the Performance of State-of-the-art Multi- and Many-objective Evolutionary Algorithms: Supplementary material}, supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2019-004/}, doi = {10.1145/3321707.3321789}, 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, publisher = {Springer, Cham}, series = {Lecture Notes in Computer Science}, editor = { Anne Auger and Carlos M. Fonseca and Louren{\c c}o, N. and Machado, P. and Lu{\'\i}s Paquete and Darrell Whitley }, booktitle = {Parallel Problem Solving from Nature - PPSN XV}, author = { 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/} }  @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, publisher = {Springer, Cham}, series = {Lecture Notes in Computer Science}, editor = { Anne Auger and Carlos M. Fonseca and Louren{\c c}o, N. and Machado, P. and Lu{\'\i}s Paquete and Darrell Whitley }, booktitle = {Parallel Problem Solving from Nature - PPSN XV}, author = { Aymeric Blot and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Marie-El{\'e}onore Kessaci-Marmion and Laetitia Jourdan }, title = {New Initialisation Techniques for Multi-Objective Local Search: Application to the Bi-objective Permutation Flowshop}, doi = {10.1007/978-3-319-99253-2_26}, pages = {323--334} }  @incollection{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, publisher = {Springer, Cham}, series = {Lecture Notes in Computer Science}, editor = { Anne Auger and Carlos M. Fonseca and Louren{\c c}o, N. and Machado, P. and Lu{\'\i}s Paquete and Darrell Whitley }, booktitle = {Parallel Problem Solving from Nature - PPSN XV}, author = { Arnaud Liefooghe and Bilel Derbel and Verel, S{\'e}bastien and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Aguirre, Hern\'{a}n E. and Tanaka, Kiyoshi }, title = {On {Pareto} Local Optimal Solutions Networks}, pages = {232--244}, doi = {10.1007/978-3-319-99259-4_19} }  @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 Companion, 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, 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 = {Juan Luis Jim{\'{e}}nez Laredo and Sara Silva and Anna I. Esparcia{-}Alc{\'{a}}zar }, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion, 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}, 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}, 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{BraCorGre2015dagstuhl, keywords = {multiple criteria decision making, evolutionary multiobjective optimization}, doi = {10.4230/DagRep.5.1.96}, volume = {5(1)}, year = 2015, series = {Dagstuhl Reports}, publisher = {Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik, Germany}, booktitle = {Understanding Complexity in Multiobjective Optimization (Dagstuhl Seminar 15031)}, editor = { Salvatore Greco and Kathrin Klamroth and Joshua D. Knowles and G{\"u}nther Rudolph }, author = { J{\"u}rgen Branke and Salvatore Corrente and Salvatore Greco and Milosz Kadzinski and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Vincent Mousseau and Mauro Munerato and Roman S{\l}owi{\'n}ski }, title = {Behavior-Realistic Artificial Decision-Makers to Test Preference-Based Multi-objective Optimization Method ({Working} {Group} {Machine} {Decision}-{Making}'')}, pages = {110--116} }  @incollection{LopLieVer2014ppsn, year = 2014, volume = 8672, publisher = {Springer}, series = {Lecture Notes in Computer Science}, editor = {Thomas Bartz-Beielstein and J{\"u}rgen Branke and Bogdan Filipi{\v c} and Jim Smith}, booktitle = {PPSN 2014}, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Arnaud Liefooghe and Verel, S{\'e}bastien }, doi = {10.1007/978-3-319-10762-2_61}, title = {Local Optimal Sets and Bounded Archiving on Multi-objective {NK}-Landscapes with Correlated Objectives}, pages = {621--630}, pdf = {LopLieVer2014ppsn.pdf} }  @incollection{BezLopStu2014:lion, 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, publisher = {Springer}, series = {Lecture Notes in Computer Science}, editor = {Thomas Bartz-Beielstein and J{\"u}rgen Branke and Bogdan Filipi{\v c} and Jim Smith}, booktitle = {PPSN 2014}, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {Automatic Design of Evolutionary Algorithms for Multi-Objective Combinatorial Optimization}, doi = {10.1007/978-3-319-10762-2_50}, pages = {508--517} }  @incollection{HutLopFaw2014lion, 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, 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}, 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, 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, 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, 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, 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, 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}, 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 = {Y. 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, publisher = {Springer}, series = {Lecture Notes in Computer Science}, editor = { Carlos A. {Coello Coello} and others}, booktitle = {Parallel Problem Solving from Nature, PPSN XII}, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Liao, Tianjun and Thomas St{\"u}tzle }, title = {On the anytime behavior of {IPOP-CMA-ES}}, pages = {357--366}, doi = {10.1007/978-3-642-32937-1_36} }  @incollection{BezLopStu2012:ants, volume = 7461, series = {Lecture Notes in Computer Science}, 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, 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, publisher = {Springer}, series = {Lecture Notes in Computer Science}, editor = { Carlos A. {Coello Coello} and others}, booktitle = {Parallel Problem Solving from Nature, PPSN XII}, author = { Dimo Brockhoff and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Boris Naujoks and G{\"u}nther Rudolph }, title = {Runtime Analysis of Simple Interactive Evolutionary Biobjective Optimization Algorithms}, pages = {123--132}, doi = {10.1007/978-3-642-32937-1_13}, abstract = {Development and deployment of interactive evolutionary multiobjective optimization algorithms (EMOAs) have recently gained broad interest. In this study, first steps towards a theory of interactive EMOAs are made by deriving bounds on the expected number of function evaluations and queries to a decision maker. We analyze randomized local search and the (1+1)-EA on the biobjective problems LOTZ and COCZ under the scenario that the decision maker interacts with these algorithms by providing a subjective preference whenever solutions are incomparable. It is assumed that this decision is based on the decision maker's internal utility function. We show that the performance of the interactive EMOAs may dramatically worsen if the utility function is non-linear instead of linear.} }  @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{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} }  @incollection{LopKnoLau2011emo, publisher = {Springer}, year = 2011, series = {Lecture Notes in Computer Science}, volume = 6576, editor = { Takahashi, R. H. C. and others}, booktitle = { Evolutionary Multi-criterion Optimization, EMO 2011}, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Joshua D. Knowles and Marco Laumanns }, title = {On Sequential Online Archiving of Objective Vectors}, pages = {46--60}, doi = {10.1007/978-3-642-19893-9_4}, abstract = {In this paper, we examine the problem of maintaining an approximation of the set of nondominated points visited during a multiobjective optimization, a problem commonly known as archiving. Most of the currently available archiving algorithms are reviewed, and what is known about their convergence and approximation properties is summarized. The main scenario considered is the restricted case where the archive must be updated online as points are generated one by one, and at most a fixed number of points are to be stored in the archive at any one time. In this scenario, the better-monotonicity of an archiving algorithm is proposed as a weaker, but more practical, property than negative efficiency preservation. This paper shows that hypervolume-based archivers and a recently proposed multi-level grid archiver have this property. On the other hand, the archiving methods used by SPEA2 and NSGA-II do not, and they may better-deteriorate with time. The better-monotonicity property has meaning on any input sequence of points. We also classify archivers according to limit properties, i.e. convergence and approximation properties of the archiver in the limit of infinite (input) samples from a finite space with strictly positive generation probabilities for all points. This paper establishes a number of research questions, and provides the initial framework and analysis for answering them.}, annote = {Revised version available at \url{http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2011-001.pdf}} }  @incollection{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, 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}, pdf = {FonGueLopPaq2011emo.pdf} }  @incollection{MauLopStu2010:cec, year = 2010, address = {Piscataway, NJ}, publisher = {IEEE Press}, booktitle = {Proceedings of the 2010 Congress on Evolutionary Computation (CEC 2010)}, editor = { Ishibuchi, Hisao and others}, 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} }  @incollection{LopStu09ea, 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}, 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, 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, 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, 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, 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, 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{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.} }  @incollection{LopPraPaech05:cec, address = {Piscataway, NJ}, publisher = {IEEE Press}, month = sep, year = 2005, booktitle = {Proceedings of the 2005 Congress on Evolutionary Computation (CEC 2005)}, key = {IEEE CEC}, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter }, title = {Multi-objective Optimisation of the Pump Scheduling Problem using {SPEA2}}, pages = {435--442}, volume = 1, doi = {10.1109/CEC.2005.1554716} }  @incollection{LopPaqStu04:ants, 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} }  @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 Companion, 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}, aurl = {https://dl.acm.org/citation.cfm?id=3319619} }  @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}, aurl = {https://dl.acm.org/citation.cfm?id=3321707} }  @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} }  @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} }  @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}, series = {Lecture Notes in Computer Science}, volume = 9882, doi = {10.1007/978-3-319-44427-7} }  @book{PPSN2016, booktitle = {Proceedings of PPSN XIV, 14th International Conference on Parallel Problem Solving from Nature }, 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}, volume = 9921, year = 2016, doi = {10.1007/978-3-319-45823-6}, isbn = {978-3-319-45822-9} }  @article{RivYanLop2021tweet, title = {Predicting tweet impact using a novel evidential reasoning prediction method}, author = { Rivadeneira, Luc{\'\i}a and Yang, Jian-Bo and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }, year = 2021, journal = {Expert Systems with Applications}, 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{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{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{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, 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 = {http://iridia.ulb.ac.be/supp/IridiaSupp2016-004/}, 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 = {http://iridia.ulb.ac.be/supp/IridiaSupp2014-010/}, 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} }  @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}, supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2013-003/}, alias = {DubLopStu2013cor} }  @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}, pdf = {http://iridia.ulb.ac.be/IridiaTrSeries/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}},
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
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.}
}