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@preamble{{\providecommand{\MaxMinAntSystem}{{$\cal MAX$--$\cal MIN$} {A}nt {S}ystem} } # {\providecommand{\Rpackage}[1]{#1} } # {\providecommand{\SoftwarePackage}[1]{#1} } # {\providecommand{\proglang}[1]{#1} }}

@techreport{LopPerDubStuBir2016iraceguide, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Leslie {P{\'e}rez C{\'a}ceres} 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}, address = {Brussels, Belgium}, 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} }

@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 {K}lee'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 {P}areto 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/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 = {doc/Lopez-Ibanez_MOACO.pdf} }

@misc{BezLopStu2016assessment, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {A Performance Assessment of Tuned Multi- and Many-Objective Evolutionary Algorithms}, year = 2016, note = {Submitted} }

@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, url = {http://lopez-ibanez.eu/eaftools}, annote = {These tools are described in the book chapter ``\emph{Exploratory analysis of stochastic local search algorithms in biobjective optimization}''~\cite{LopPaqStu09emaa}. Please cite the book chapter, not this.} }

@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{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 {P}areto 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{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 = {{GECCO} (Companion)}, 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 ({W}orking {G}roup ``{M}achine {D}ecision-{M}aking'')}, pages = {110--116} }

@incollection{PerLopStu2014ants, volume = 8667, series = {Lecture Notes in Computer Science}, publisher = {Springer}, editor = { Marco Dorigo and others }, year = 2014, booktitle = {Swarm Intelligence, 8th International Conference, ANTS 2014}, author = {Leslie {P{\'e}rez C{\'a}ceres} 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}, author = {Leslie {P{\'e}rez C{\'a}ceres} 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{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 = {doc/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} }

@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}, doi = {10.1007/978-3-319-07644-7_3} }

@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 = { M. 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, aurl = {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 = { E-G. Talbi }, 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: {T}wo-{P}hase and {P}areto 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 = {P. 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}, 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}, year = 2013, 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 M. 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 = {{P}areto 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? ({W}orking {G}roup ``{A}lgorithm {D}esign {M}ethods'')}, 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)}, 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, isbn = {978-1-4503-0557-0}, 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 = {Second}, 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} }

@incollection{MauLopStu2010:cec, year = 2010, address = {Piscataway, NJ}, publisher = {IEEE Press}, booktitle = {Proceedings of the 2010 Congress on Evolutionary Computation (CEC 2010)}, editor = {Ishibuchi, H. 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} }

@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)}, author = { Carlos M. Fonseca and Lu{\'i}s Paquete and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }, title = {An improved dimension\hspace{0pt}-\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)}, 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 F. 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} }

@article{LopDubPerStuBir2016irace, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and Leslie {P{\'e}rez C{\'a}ceres} 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/} }

@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, 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{PerLopStu2015si, author = {Leslie {P{\'e}rez C{\'a}ceres} 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{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 {P}areto 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{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/link/IridiaTr2013-011.pdf} }

@article{LopStu2012swarm, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {An experimental analysis of design choices of multi-objective ant colony optimization algorithms}, journal = {Swarm Intelligence}, year = 2012, number = 3, volume = 6, pages = {207--232}, doi = {10.1007/s11721-012-0070-7}, supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2012-006/} }

@article{LopStu2012tec, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {The Automatic Design of Multi-Objective Ant Colony Optimization Algorithms}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2012, volume = 16, number = 6, pages = {861--875}, doi = {10.1109/TEVC.2011.2182651}, abstract = { Multi-objective optimization problems are problems with several, typically conflicting criteria for evaluating solutions. Without any a priori preference information, the Pareto optimality principle establishes a partial order among solutions, and the output of the algorithm becomes a set of nondominated solutions rather than a single one. Various ant colony optimization (ACO) algorithms have been proposed in recent years for solving such problems. These multi-objective ACO (MOACO) algorithms exhibit different design choices for dealing with the particularities of the multi-objective context. This paper proposes a formulation of algorithmic components that suffices to describe most MOACO algorithms proposed so far. This formulation also shows that existing MOACO algorithms often share equivalent design choices but they are described in different terms. Moreover, this formulation is synthesized into a flexible algorithmic framework, from which not only existing MOACO algorithms may be instantiated, but also combinations of components that were never studied in the literature. In this sense, this paper goes beyond proposing a new MOACO algorithm, but it rather introduces a family of MOACO algorithms. The flexibility of the proposed MOACO framework facilitates the application of automatic algorithm configuration techniques. The experimental results presented in this paper show that the automatically configured MOACO framework outperforms the MOACO algorithms that inspired the framework itself. This paper is also among the first to apply automatic algorithm configuration techniques to multi-objective algorithms.} }

@article{LopPraPae2011ec, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter }, title = {Representations and Evolutionary Operators for the Scheduling of Pump Operations in Water Distribution Networks}, journal = {Evolutionary Computation}, year = 2011, doi = {10.1162/EVCO_a_00035}, volume = 19, number = 3, pages = {429--467}, abstract = {Reducing the energy consumption of water distribution networks has never had more significance. The greatest energy savings can be obtained by carefully scheduling the operations of pumps. Schedules can be defined either implicitly, in terms of other elements of the network such as tank levels, or explicitly by specifying the time during which each pump is on/off. The traditional representation of explicit schedules is a string of binary values with each bit representing pump on/off status during a particular time interval. In this paper, we formally define and analyze two new explicit representations based on time-controlled triggers, where the maximum number of pump switches is established beforehand and the schedule may contain less switches than the maximum. In these representations, a pump schedule is divided into a series of integers with each integer representing the number of hours for which a pump is active/inactive. This reduces the number of potential schedules compared to the binary representation, and allows the algorithm to operate on the feasible region of the search space. We propose evolutionary operators for these two new representations. The new representations and their corresponding operations are compared with the two most-used representations in pump scheduling, namely, binary representation and level-controlled triggers. A detailed statistical analysis of the results indicates which parameters have the greatest effect on the performance of evolutionary algorithms. The empirical results show that an evolutionary algorithm using the proposed representations improves over the results obtained by a recent state-of-the-art Hybrid Genetic Algorithm for pump scheduling using level-controlled triggers.} }

@article{DubLopStu2011amai, author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {Improving the Anytime Behavior of Two-Phase Local Search}, journal = {Annals of Mathematics and Artificial Intelligence}, year = 2011, volume = 61, number = 2, pages = {125--154}, doi = {10.1007/s10472-011-9235-0}, alias = {DubLopStu2010amai}, pdf = {DubLopStu2011amai.pdf} }

@article{DubLopStu2011cor, author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {A Hybrid {TP$+$PLS} Algorithm for Bi-objective Flow-Shop Scheduling Problems}, journal = {Computers \& Operations Research}, year = 2011, volume = 38, number = 8, pages = {1219--1236}, doi = {10.1016/j.cor.2010.10.008}, supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2010-001/}, pdf = {DubLopStu2011cor.pdf} }

@article{LopBlu2010cor, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum }, title = {Beam-{ACO} for the travelling salesman problem with time windows}, journal = {Computers \& Operations Research}, year = 2010, doi = {10.1016/j.cor.2009.11.015}, volume = 37, number = 9, pages = {1570--1583}, keywords = {Ant colony optimization}, keywords = {Travelling salesman problem with time windows}, keywords = {Hybridization}, alias = {LopBlu09tsptw}, abstract = {The travelling salesman problem with time windows is a difficult optimization problem that arises, for example, in logistics. This paper deals with the minimization of the travel-cost. For solving this problem, this paper proposes a Beam-ACO algorithm, which is a hybrid method combining ant colony optimization with beam search. In general, Beam-ACO algorithms heavily rely on accurate and computationally inexpensive bounding information for differentiating between partial solutions. This work uses stochastic sampling as a useful alternative. An extensive experimental evaluation on seven benchmark sets from the literature shows that the proposed Beam-ACO algorithm is currently a state-of-the-art technique for the travelling salesman problem with time windows when travel-cost optimization is concerned.} }

@article{BeuFonLopPaqVah09:tec, author = { Nicola Beume and Carlos M. Fonseca and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Jan Vahrenhold }, title = {On the complexity of computing the hypervolume indicator}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2009, volume = 13, number = 5, pages = {1075--1082}, doi = {10.1109/TEVC.2009.2015575}, abstract = {The goal of multi-objective optimization is to find a set of best compromise solutions for typically conflicting objectives. Due to the complex nature of most real-life problems, only an approximation to such an optimal set can be obtained within reasonable (computing) time. To compare such approximations, and thereby the performance of multi-objective optimizers providing them, unary quality measures are usually applied. Among these, the \emph{hypervolume indicator} (or \emph{S-metric}) is of particular relevance due to its favorable properties. Moreover, this indicator has been successfully integrated into stochastic optimizers, such as evolutionary algorithms, where it serves as a guidance criterion for finding good approximations to the Pareto front. Recent results show that computing the hypervolume indicator can be seen as solving a specialized version of Klee's Measure Problem. In general, Klee's Measure Problem can be solved with $\mathcal{O}(n \log n + n^{d/2}\log n)$ comparisons for an input instance of size $n$ in $d$ dimensions; as of this writing, it is unknown whether a lower bound higher than $\Omega(n \log n)$ can be proven. In this article, we derive a lower bound of $\Omega(n\log n)$ for the complexity of computing the hypervolume indicator in any number of dimensions $d>1$ by reducing the so-called \textsc{UniformGap} problem to it. For the three dimensional case, we also present a matching upper bound of $\mathcal{O}(n\log n)$ comparisons that is obtained by extending an algorithm for finding the maxima of a point set.} }

@article{BluBleLop09-BeamSearch-LCS, author = { Christian Blum and Mar{\'\i}a J. Blesa and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }, title = {Beam search for the longest common subsequence problem}, number = 12, journal = {Computers \& Operations Research}, year = 2009, pages = {3178--3186}, volume = 36, doi = {10.1016/j.cor.2009.02.005}, pdf = {BluBleLop09-BeamSearch-LCS.pdf}, abstract = { The longest common subsequence problem is a classical string problem that concerns finding the common part of a set of strings. It has several important applications, for example, pattern recognition or computational biology. Most research efforts up to now have focused on solving this problem optimally. In comparison, only few works exist dealing with heuristic approaches. In this work we present a deterministic beam search algorithm. The results show that our algorithm outperforms the current state-of-the-art approaches not only in solution quality but often also in computation time.} }

@article{LopPraPae08aco, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter }, title = {Ant Colony Optimisation for the Optimal Control of Pumps in Water Distribution Networks}, journal = {Journal of Water Resources Planning and Management, {ASCE}}, year = 2008, volume = 134, number = 4, pages = {337--346}, publisher = {{ASCE}}, pdf = {LopezPrasadPaechter08-jwrpm.pdf}, aurl = {http://link.aip.org/link/?QWR/134/337/1}, doi = {10.1061/(ASCE)0733-9496(2008)134:4(337)}, abstract = {Reducing energy consumption of water distribution networks has never had more significance than today. The greatest energy savings can be obtained by careful scheduling of operation of pumps. Schedules can be defined either implicitly, in terms of other elements of the network such as tank levels, or explicitly by specifying the time during which each pump is on/off. The traditional representation of explicit schedules is a string of binary values with each bit representing pump on/off status during a particular time interval. In this paper a new explicit representation is presented. It is based on time controlled triggers, where the maximum number of pump switches is specified beforehand. In this representation a pump schedule is divided into a series of integers with each integer representing the number of hours for which a pump is active/inactive. This reduces the number of potential schedules (search space) compared to the binary representation. Ant colony optimization (ACO) is a stochastic meta-heuristic for combinatorial optimization problems that is inspired by the foraging behavior of some species of ants. In this paper, an application of the ACO framework was developed for the optimal scheduling of pumps. The proposed representation was adapted to an ant colony Optimization framework and solved for the optimal pump schedules. Minimization of electrical cost was considered as the objective, while satisfying system constraints. Instead of using a penalty function approach for constraint violations, constraint violations were ordered according to their importance and solutions were ranked based on this order. The proposed approach was tested on a small test network and on a large real-world network. Results are compared with those obtained using a simple genetic algorithm based on binary representation and a hybrid genetic algorithm that uses level-based triggers.} }

@article{LopPaqStu05:jmma, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle }, title = {Hybrid Population-based Algorithms for the Bi-objective Quadratic Assignment Problem}, journal = {Journal of Mathematical Modelling and Algorithms}, year = 2006, volume = 5, number = 1, pages = {111--137}, pdf = {LopPaqStu04-techrepAIDA-04-11.pdf}, doi = {10.1007/s10852-005-9034-x}, alias = {LopPaqStu06:jmma}, abstract = {We present variants of an ant colony optimization (MO-ACO) algorithm and of an evolutionary algorithm (SPEA2) for tackling multi-objective combinatorial optimization problems, hybridized with an iterative improvement algorithm and the robust tabu search algorithm. The performance of the resulting hybrid stochastic local search (SLS) algorithms is experimentally investigated for the bi-objective quadratic assignment problem (bQAP) and compared against repeated applications of the underlying local search algorithms for several scalarizations. The experiments consider structured and unstructured bQAP instances with various degrees of correlation between the flow matrices. We do a systematic experimental analysis of the algorithms using outperformance relations and the attainment functions methodology to asses differences in the performance of the algorithms. The experimental results show the usefulness of the hybrid algorithms if the available computation time is not too limited and identify SPEA2 hybridized with very short tabu search runs as the most promising variant.} }