LopezIbanez.bib
@comment{{This file has been generated by bib2bib 1.99}}
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@preamble{{\providecommand{\MaxMinAntSystem}{{$\cal MAX$--$\cal MIN$} {Ant} {System}} } # {\providecommand{\rpackage}[1]{{#1}} } # {\providecommand{\softwarepackage}[1]{{#1}} } # {\providecommand{\proglang}[1]{{#1}} } # {\providecommand{\BIBdepartment}[1]{{#1}, } }}
@unpublished{CamTriLop2017pseudo,
author = {Felipe Campelo and \'Athila R. Trindade and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Pseudoreplication in Racing Methods for Tuning Metaheuristics},
note = {In preparation},
year = 2017
}
@techreport{IRIDIA-2018-001,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatically Designing State-of-the-Art Multi- and
Many-Objective Evolutionary Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2018,
number = {TR/IRIDIA/2018-001},
month = jan,
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2018-001.pdf},
note = {Published in Evolutionary Computation journal~\cite{BezLopStu2019ec}}
}
@techreport{IRIDIA-2017-005,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {A Large-Scale Experimental Evaluation of High-Performing
Multi- and Many-Objective Evolutionary Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2017,
number = {TR/IRIDIA/2017-005},
month = nov
}
@techreport{IRIDIA-2017-011,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Configuration of Multi-objective Optimizers and
Multi-objective Configuration},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2017,
number = {TR/IRIDIA/2017-011},
month = nov,
alias = {BezLopStu2017:techreport-011},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2017-011.pdf},
note = {Published as a book chapter~\cite{BezLopStu2020chapter}}
}
@techreport{BezLopStu2017:techreport-005,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {A Large-Scale Experimental Evaluation of High-Performing
Multi- and Many-Objective Evolutionary Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2017,
number = {TR/IRIDIA/2017-005},
month = feb
}
@techreport{IRIDIA-2017-012,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Marie-El{\'e}onore Kessaci and Thomas St{\"u}tzle },
title = {Automatic Design of Hybrid Metaheuristics from Algorithmic
Components},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2017,
number = {TR/IRIDIA/2017-012},
month = dec,
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2017-012.pdf}
}
@techreport{LopPerDubStuBir2016iraceguide,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and P{\'e}rez C{\'a}ceres, Leslie and J{\'e}r{\'e}mie Dubois-Lacoste and Thomas St{\"u}tzle and Mauro Birattari },
title = {The {\rpackage{irace}} package: User Guide},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2016,
number = {TR/IRIDIA/2016-004},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2016-004.pdf}
}
@techreport{BezLopStu2014:automoeaTR,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Com\-ponent-Wise Design of Multi-Objective
Evolutionary Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2014,
number = {TR/IRIDIA/2014-012},
month = aug
}
@techreport{IRIDIA-2014-014,
author = { Vito Trianni and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Advantages of Multi-Objective Optimisation in Evolutionary
Robotics: Survey and Case Studies},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2014,
number = {TR/IRIDIA/2014-014},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2014-014.pdf}
}
@techreport{IRIDIA-2014-009,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Arnaud Liefooghe and Verel, S{\'e}bastien },
title = {Local Optimal Sets and Bounded Archiving on
Multi-objective {NK}-Landscapes with Correlated
Objectives},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2014,
number = {TR/IRIDIA/2014-009}
}
@techreport{IRIDIA-2013-015,
author = { Franco Mascia and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and Thomas St{\"u}tzle },
year = 2013,
title = {Grammar-based generation of stochastic local search
heuristics through automatic algorithm configuration
tools},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2013-015}
}
@techreport{IRIDIA-2012-012,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatically Improving the Anytime Behaviour of
Optimisation Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2012,
number = {TR/IRIDIA/2012-012},
month = may,
note = {Published in European Journal of Operational Research~\cite{LopStu2013ejor}}
}
@techreport{IRIDIA-2012-019,
author = { Andreea Radulescu and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatically Improving the Anytime Behaviour of
Multiobjective Evolutionary Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2012,
number = {TR/IRIDIA/2012-019},
note = {Published in the proceedings of EMO 2013~\cite{RadLopStu2013emo}}
}
@techreport{IRIDIA-2011-003,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {The Automatic Design of Multi-Objective Ant Colony
Optimization Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2011,
number = {TR/IRIDIA/2011-003},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2011-003.pdf},
note = {Published in IEEE Transactions on Evolutionary
Computation~\cite{LopStu2012tec}}
}
@techreport{LopDubStu2011irace,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and Thomas St{\"u}tzle and Mauro Birattari },
title = {The {\rpackage{irace}} package, Iterated Race for Automatic
Algorithm Configuration},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2011,
number = {TR/IRIDIA/2011-004},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2011-004.pdf},
note = {Published in Operations Research Perspectives~\cite{LopDubPerStuBir2016irace}}
}
@techreport{IRIDIA-2011-001,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Joshua D. Knowles and Marco Laumanns },
title = {On Sequential Online Archiving of Objective Vectors},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2011,
number = {TR/IRIDIA/2011-001},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2011-001.pdf},
note = {This is a revised version of the paper published in EMO 2011~\cite{LopKnoLau2011emo}}
}
@techreport{IRIDIA-2010-002,
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Paola Pellegrini and Michael Maur and Marco A. {Montes de Oca} and Mauro Birattari and Marco Dorigo },
title = {Parameter Adaptation in Ant Colony Optimization},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2010-002},
year = 2010,
month = jan,
note = {Published as a book chapter~\cite{StuLopPel2011autsea}}
}
@techreport{IRIDIA-2009-026,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Adaptive ``Anytime'' Two-Phase Local Search},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2010,
number = {TR/IRIDIA/2009-026},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2009-026.pdf},
note = {Published in the proceedings of LION 4~\cite{DubLopStu10:lion-bfsp}}
}
@techreport{IRIDIA-2010-019,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {A Hybrid {TP+PLS} Algorithm for Bi-objective
Flow-Shop Scheduling Problems},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2010,
number = {TR/IRIDIA/2010-019},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2010-019.pdf},
note = {Published in Computers \& Operations Research~\cite{DubLopStu2011cor}}
}
@techreport{IRIDIA-2010-022,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Improving the Anytime Behavior of Two-Phase Local
Search},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2010,
number = {TR/IRIDIA/2010-022},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2010-022.pdf},
note = {Published in Annals of Mathematics and Artificial Intelligence~\cite{DubLopStu2011amai}}
}
@techreport{IRIDIA-2009-015,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2009,
number = {TR/IRIDIA/2009-015},
month = may,
note = {Published as a book chapter~\cite{LopPaqStu09emaa}}
}
@techreport{IRIDIA-2009-019,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An Analysis of Algorithmic Components for
Multiobjective Ant Colony Optimization: A Case Study
on the Biobjective {TSP}},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2009-019},
year = 2009,
month = jun,
note = {Published in the proceedings of Evolution Artificielle, 2009~\cite{LopStu09ea}}
}
@techreport{IRIDIA-2009-020,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Effective Hybrid Stochastic Local Search Algorithms
for Biobjective Permutation Flowshop Scheduling},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2009-020},
year = 2009,
month = jun,
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2009-020.pdf},
note = {Published in the proceedings of Hybrid Metaheuristics 2009~\cite{DubLopStu09:hm-bfsp}}
}
@techreport{LopBlu08:tsptw,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum },
title = {Beam-{ACO} Based on Stochastic Sampling: {A} Case Study on
the {TSP} with Time Windows},
institution = {Department LSI, Universitat Polit{\`e}cnica de Catalunya},
year = 2008,
number = {LSI-08-28},
note = {Extended version published in Computers \& Operations Research~\cite{LopBlu2010cor}}
}
@techreport{BluBleLop08:lcs,
author = { Christian Blum and Mar{\'i}a J. Blesa and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Beam Search for the Longest Common Subsequence
Problem},
institution = {Department LSI, Universitat Polit{\`e}cnica de Catalunya},
year = 2008,
number = {LSI-08-29},
note = {Published in Computers \& Operations Research~\cite{BluBleLop09-BeamSearch-LCS}}
}
@techreport{CI-235-07,
author = { Nicola Beume and Carlos M. Fonseca and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Jan Vahrenhold },
title = {On the Complexity of Computing the Hypervolume
Indicator},
institution = {University of Dortmund},
year = 2007,
number = {CI-235/07},
month = dec,
note = {Published in IEEE Transactions on Evolutionary Computation~\cite{BeuFonLopPaqVah09:tec}}
}
@techreport{PaqFonLop06-CSI-klee,
author = { Lu{\'i}s Paquete and Carlos M. Fonseca and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {An optimal algorithm for a special case of {Klee}'s
measure problem in three dimensions},
institution = {CSI, Universidade do Algarve},
year = 2006,
number = {CSI-RT-I-01/2006},
abstract = {The measure of the region dominated by (the maxima
of) a set of $n$ points in the positive $d$-orthant
has been proposed as an indicator of performance in
multiobjective optimization, known as the
hypervolume indicator, and the problem of computing
it efficiently is attracting increasing
attention. In this report, this problem is
formulated as a special case of Klee's measure
problem in $d$ dimensions, which immediately
establishes $O(n^{d/2}\log n)$ as a, possibly
conservative, upper bound on the required
computation time. Then, an $O(n log n)$ algorithm
for the 3-dimensional version of this special case
is constructed, based on an existing dimension-sweep
algorithm for the related maxima problem. Finally,
$O(n log n)$ is shown to remain a lower bound on the
time required by the hypervolume indicator for
$d>1$, which attests the optimality of the algorithm
proposed.},
note = {Superseded by paper in IEEE Transactions on Evolutionary Computation~\cite{BeuFonLopPaqVah09:tec}},
annote = {Proof of Theorem 3.1 is incorrect}
}
@techreport{PaqStuLop-IRIDIA-2005-029,
author = { Lu{\'i}s Paquete and Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {On the design and analysis of {SLS} algorithms for
multiobjective combinatorial optimization problems},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2005,
number = {TR/IRIDIA/2005-029},
abstract = {Effective Stochastic Local Search (SLS) algorithms
can be seen as being composed of several algorithmic
components, each of which plays some specific role
with respect to overall performance. In this
article, we explore the application of experimental
design techniques to analyze the effect of different
choices for these algorithmic components on SLS
algorithms applied to Multiobjective Combinatorial
Optimization Problems that are solved in terms of
{Pareto} optimality. This analysis is done using the
example application of SLS algorithms to the
biobjective Quadratic Assignment Problem and we show
also that the same choices for algorithmic
components can lead to different behavior in
dependence of various instance features, such as the
structure of input data and the correlation between
objectives.},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2005-029.pdf}
}
@techreport{LopPaqStu04:hybrid,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {Hybrid Population-based Algorithms for the Bi-objective
Quadratic Assignment Problem},
institution = {FG Intellektik, FB Informatik, TU Darmstadt},
year = 2004,
number = {AIDA--04--11},
month = dec,
note = {Published in Journal of Mathematical Modelling and Algorithms~\cite{LopPaqStu05:jmma}},
annote = {First use of EAF differences}
}
@phdthesis{LopezIbanezPhD,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Operational Optimisation of Water Distribution
Networks},
school = {School of Engineering and the Built Environment},
year = 2009,
address = {Edinburgh Napier University, UK},
url = {https://researchrepository.napier.ac.uk/id/eprint/3044}
}
@phdthesis{LopezDiploma,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Multi-objective Ant Colony Optimization},
school = {Intellectics Group, Computer Science Department, Technische
Universit{\"a}t Darmstadt, Germany},
year = 2004,
type = {Diploma thesis},
pdf = {Lopez-Ibanez_MOACO.pdf}
}
@misc{DesRitLopPer2020zenodo,
author = { Marcelo {De Souza} and Marcus Ritt and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and P{\'e}rez C{\'a}ceres, Leslie },
title = {{\softwarepackage{ACVIZ}}: Algorithm Configuration
Visualizations for {\rpackage{irace}}: Supplementary
material},
howpublished = {\url{http://doi.org/10.5281/zenodo.4714582}},
month = sep,
year = 2020,
publisher = {Zenodo}
}
@misc{DesRitLop2020acviz,
author = { Marcelo {De Souza} and Marcus Ritt and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and P{\'e}rez C{\'a}ceres, Leslie },
title = {{\softwarepackage{ACVIZ}}: A Tool for the Visual Analysis of
the Configuration of Algorithms with {\rpackage{irace}} --
Source Code},
howpublished = {\url{https://github.com/souzamarcelo/acviz}},
year = 2020
}
@misc{SouRitLop2020capopt,
author = { Marcelo {De Souza} and Marcus Ritt and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {{CAPOPT}: Capping Methods for the Automatic Configuration of Optimization Algorithms},
howpublished = {\url{https://github.com/souzamarcelo/capopt}},
year = 2020
}
@misc{LopPaqStu2010:eaftools,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {{EAF} Graphical Tools},
year = 2010,
howpublished = {\url{http://lopez-ibanez.eu/eaftools}},
note = {These tools are described in the book chapter
``\emph{Exploratory analysis of stochastic local search
algorithms in biobjective
optimization}''~\cite{LopPaqStu09emaa}.},
annote = {Please cite the book chapter, not this.}
}
@inproceedings{HunLop2019turing,
isbn = {978-1-5262-0820-0},
organization = {Alan Turing Institute},
month = nov # { 21--22},
year = 2019,
date = {2019-11-21/2019-11-22},
address = {London, UK},
editor = {Iv{\'a}n Palomares},
booktitle = {International Alan Turing Conference on Decision Support and
Recommender systems},
author = {Maura Hunt and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Modeling a Decision-Maker in Goal Programming by means of
Computational Rationality},
pages = {17--20},
abstract = {This paper extends a simulation of cognitive mechanisms in
the context of multi-criteria decision-making by using ideas
from computational rationality. Specifically, this paper
improves the simulation of a human decision-maker (DM) by
considering how resource constraints impact their evaluation
process in an interactive Goal Programming problem. Our
analysis confirms and emphasizes a previous simulation study
by showing key areas that could be effected by cognitive
mechanisms. While the results are promising, the effects
should be validated by future experiments with human DMs.},
epub = {https://dsrs.blogs.bristol.ac.uk/files/2020/01/DSRS-Turing_19.pdf#page=24}
}
@inproceedings{LopMasMarStu2013mista,
year = 2013,
editor = { Graham Kendall and Vanden Berghe, Greet and Barry McCollum},
address = {Gent, Belgium},
booktitle = {Multidisciplinary International Conference on Scheduling:
Theory and Applications (MISTA 2013)},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Franco Mascia and Marie-El{\'e}onore Marmion and Thomas St{\"u}tzle },
title = {Automatic Design of a Hybrid Iterated Local Search for the
Multi-Mode Resource-Constrained Multi-Project Scheduling
Problem},
pages = {1--6},
epub = {https://hal.inria.fr/hal-01094681},
pdf = {LopMasMarStu2013mista.pdf}
}
@inproceedings{EppLopStuDeS2011:cec,
year = 2011,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2011 Congress on Evolutionary Computation (CEC 2011)},
key = {IEEE CEC},
author = { Stefan Eppe and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle and Yves {De Smet} },
title = {An Experimental Study of Preference Model Integration into
Multi-Objective Optimization Heuristics},
pages = {2751--2758},
doi = {10.1109/CEC.2011.5949963}
}
@inproceedings{MauLopStu2010:cec,
year = 2010,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2010 Congress on Evolutionary Computation (CEC 2010)},
editor = { Ishibuchi, Hisao and others},
key = {IEEE CEC},
author = { Michael Maur and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Pre-scheduled and adaptive parameter variation in
{\MaxMinAntSystem}},
pages = {3823--3830},
doi = {10.1109/CEC.2010.5586332},
pdf = {MauLopStu2010-Parameter Adaptation in Max-Min Ant System.pdf}
}
@inproceedings{LopPraPae08:WDSA,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Parallel Optimisation Of Pump Schedules With A
Thread-Safe Variant Of {EPANET} Toolkit},
booktitle = {Proceedings of the 10th Annual Water Distribution
Systems Analysis Conference (WDSA 2008)},
year = 2008,
editor = { Jakobus E. van Zyl and A. A. Ilemobade and H. E. Jacobs },
month = aug,
pdf = {LopezPrasadPaechter-WDSA2008-official.pdf},
doi = {10.1061/41024(340)40},
publisher = {ASCE}
}
@inproceedings{FonPaqLop06:hypervolume,
address = {Piscataway, NJ},
publisher = {IEEE Press},
month = jul,
year = 2006,
booktitle = {Proceedings of the 2006 Congress on Evolutionary Computation (CEC 2006)},
key = {IEEE CEC},
author = { Carlos M. Fonseca and Lu{\'i}s Paquete and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {An improved dimension-\hspace{0pt}sweep
algorithm for the hypervolume indicator},
pages = {1157--1163},
doi = {10.1109/CEC.2006.1688440},
pdf = {FonPaqLop06-hypervolume.pdf},
abstract = {This paper presents a recursive, dimension-sweep
algorithm for computing the hypervolume indicator of
the quality of a set of $n$ non-dominated points in
$d>2$ dimensions. It improves upon the existing HSO
(Hypervolume by Slicing Objectives) algorithm by
pruning the recursion tree to avoid repeated
dominance checks and the recalculation of partial
hypervolumes. Additionally, it incorporates a recent
result for the three-dimensional special case. The
proposed algorithm achieves $O(n^{d-2} \log n)$ time
and linear space complexity in the worst-case, but
experimental results show that the pruning
techniques used may reduce the time complexity
exponent even further.}
}
@inproceedings{LopPraPaech05:cec,
address = {Piscataway, NJ},
publisher = {IEEE Press},
month = sep,
year = 2005,
booktitle = {Proceedings of the 2005 Congress on Evolutionary Computation (CEC 2005)},
key = {IEEE CEC},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Multi-objective Optimisation of the Pump Scheduling
Problem using {SPEA2}},
pages = {435--442},
volume = 1,
doi = {10.1109/CEC.2005.1554716}
}
@inproceedings{LopPraPaech:ccwi2005,
month = sep,
address = {University of Exeter, UK},
volume = 1,
editor = { Dragan A. Savic and Godfrey A. Walters and Roger King and Soon Thiam-Khu },
year = 2005,
booktitle = {Proceedings of the Eighth International Conference on
Computing and Control for the Water Industry (CCWI 2005)},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Optimal Pump Scheduling: Representation and Multiple
Objectives},
pdf = {LopPraPae05-ccwi.pdf},
pages = {117--122}
}
@inproceedings{PaqStuLop05mic,
address = {Vienna, Austria},
year = 2005,
booktitle = {6th Metaheuristics International Conference (MIC 2005)},
editor = { Karl F. Doerner and Michel Gendreau and Peter Greistorfer and Gutjahr, Walter J. and Richard F. Hartl and Marc Reimann },
author = { Lu{\'i}s Paquete and Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Towards the Empirical Analysis of {SLS} Algorithms
for Multiobjective Combinatorial Optimization
Problems through Experimental Design},
pages = {739--746},
abstract = { Stochastic Local Search (SLS) algorithms for
Multiobjective Combinatorial Optimization Problems
(MCOPs) typically involve the selection and
parameterization of many algorithm components whose
role with respect to their overall performance and
relation to certain instance features is often not
clear. In this abstract, we use a modular approach
for the design of SLS algorithms for MCOPs defined
in terms of {Pareto} optimality and we present an
extensive analysis of SLS algorithms through
experimental design techniques, where each algorithm
component is considered a factor. The experimental
analysis is based on a sound experimental
methodology for analyzing the output of algorithms
for MCOPs. We show that different choices for
algorithm components can lead to different behavior
in dependence of various instance features.},
pdf = {PaqStuLop05mic.pdf}
}
@incollection{PanVerLopBac2024transfer,
location = {Melbourne, Australia},
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2024},
publisher = {ACM Press},
year = 2024,
editor = { Julia Handl and Li, Xiaodong },
author = {Shuaiqun Pan and Diederick Vermetten and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas B{\"a}ck and Wang, Hao },
title = {Transfer Learning of Surrogate Models via Domain Affine
Transformation},
doi = {10.1145/3638529.3654032}
}
@incollection{PriAllLop2024ppsn,
address = { Cham, Switzerland},
series = {Lecture Notes in Computer Science},
volume = 15149,
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XVIII}},
publisher = {Springer},
year = 2024,
editor = {Michael Affenzeller and Stephan M. Winkler and Anna
V. Kononova and Heike Trautmann and Tea Tu{\v s}ar and Penousal Machado and Thomas B{\"a}ck },
author = { Pricopie, Stefan and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Fare,
Clyde and Benatan, Matt and Joshua D. Knowles },
title = {An Adaptive Approach to Bayesian Optimization with Setup
Switching Costs},
pages = {340--355},
abstract = {Black-box optimization methods typically assume that
evaluations of the black-box objective function are equally
costly to evaluate. We investigate here a
resource-constrained setting where changes to certain
decision variables of the search space incur a higher
switching cost, e.g., due to expensive changes to the
experimental setup. In this scenario, there is a trade-off
between fixing the values of those costly variables or
accepting this additional cost to explore more of the search
space. We adapt two process-constrained batch algorithms to
this sequential problem formulation, and propose two new
methods: one one cost-aware and one cost-ignorant. We
validate and compare the algorithms using a set of 7 scalable
test functions with different switching-cost settings. Our
proposed cost-aware parameter-free algorithm yields
comparable results to tuned process-constrained algorithms in
all settings we considered, suggesting some degree of
robustness to varying landscape features and cost
trade-offs. This method starts to outperform the other
algorithms with increasing switching cost. Our work expands
on other recent Bayesian Optimization studies in
resource-constrained settings that consider a batch setting
only. Although the contributions of this work are relevant to
the general class of resource-constrained problems, they are
particularly relevant to problems where adaptability to
varying resource availability is of high importance.},
doi = {10.1007/978-3-031-70068-2_21}
}
@incollection{ShaLopAllKno2023emo,
address = { Cham, Switzerland},
series = {Lecture Notes in Computer Science},
volume = 13970,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2023},
publisher = {Springer International Publishing},
year = 2023,
editor = { Emmerich, Michael T. M. and others},
author = { Shavarani, Seyed Mahdi and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Allmendinger, Richard and Joshua D. Knowles },
title = {An Interactive Decision Tree-Based Evolutionary
Multi-Objective Algorithm: Supplementary material},
pages = {620--634},
doi = {10.1007/978-3-031-27250-9_44}
}
@incollection{AyoAllLop2023gecco,
location = {Lisbon, Portugal},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2023},
annote = {ISBN: 979-8-4007-0120-7},
address = { New York, NY},
year = 2023,
publisher = {ACM Press},
editor = {Silva, Sara and Lu{\'i}s Paquete },
author = { Ayodele, Mayowa and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Parizy, Matthieu and Arnaud Liefooghe },
title = {Applying {Ising} Machines to Multi-Objective {QUBOs}},
pages = {2166--2174},
doi = {10.1145/3583133.3596312},
abstract = {Multi-objective optimisation problems involve finding
solutions with varying trade-offs between multiple and often
conflicting objectives. Ising machines are physical devices
that aim to find the absolute or approximate ground states of
an Ising model. To apply Ising machines to multi-objective
problems, a weighted sum objective function is used to
convert multi-objective into single-objective
problems. However, deriving scalarisation weights that
archives evenly distributed solutions across the Pareto front
is not trivial. Previous work has shown that adaptive weights
based on dichotomic search, and one based on averages of
previously explored weights can explore the Pareto front
quicker than uniformly generated weights. However, these
adaptive methods have only been applied to bi-objective
problems in the past. In this work, we extend the adaptive
method based on averages in two ways: (i) we extend the
adaptive method of deriving scalarisation weights for
problems with two or more objectives, and (ii) we use an
alternative measure of distance to improve performance. We
compare the proposed method with existing ones and show that
it leads to the best performance on multi-objective
Unconstrained Binary Quadratic Programming (mUBQP) instances
with 3 and 4 objectives and that it is competitive with the
best one for instances with 2 objectives.},
numpages = 9,
keywords = {digital annealer, multi-objective, bi-objective QAP, QUBO}
}
@incollection{LieLop2023many,
location = {Lisbon, Portugal},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2023},
annote = {ISBN: 9798400701191},
address = { New York, NY},
year = 2023,
publisher = {ACM Press},
editor = {Silva, Sara and Lu{\'i}s Paquete },
author = { Arnaud Liefooghe and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Many-objective (Combinatorial) Optimization is Easy},
pages = {704--712},
doi = {10.1145/3583131.3590475},
abstract = {It is a common held assumption that problems with many
objectives are harder to optimize than problems with two or
three objectives. In this paper, we challenge this assumption
and provide empirical evidence that increasing the number of
objectives tends to reduce the difficulty of the landscape
being optimized. Of course, increasing the number of
objectives brings about other challenges, such as an increase
in the computational effort of many operations, or the memory
requirements for storing non-dominated solutions. More
precisely, we consider a broad range of multi- and
many-objective combinatorial benchmark problems, and we
measure how the number of objectives impacts the dominance
relation among solutions, the connectedness of the Pareto
set, and the landscape multimodality in terms of local
optimal solutions and sets. Our analysis shows the limit
behavior of various landscape features when adding more
objectives to a problem. Our conclusions do not contradict
previous observations about the inability of
Pareto-optimality to drive search, but we explain these
observations from a different perspective. Our findings have
important implications for the design and analysis of
many-objective optimization algorithms.}
}
@incollection{TraNikCen2022ngopt,
address = { Cham, Switzerland},
series = {Lecture Notes in Computer Science},
volume = 13398,
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XVII}},
publisher = {Springer},
year = 2022,
editor = { G{\"u}nther Rudolph and Anna V. Kononova and Aguirre, Hern\'{a}n E. and Pascal Kerschke and Gabriela Ochoa and Tea Tu{\v s}ar },
author = {Trajanov, Risto and Nikolikj, Ana and Cenikj, Gjorgjina and
Teytaud, Fabien and Videau, Mathurin and Olivier Teytaud and Tome Eftimov and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Carola Doerr },
title = {Improving {Nevergrad}'s Algorithm Selection Wizard {NGOpt}
Through Automated Algorithm Configuration},
pages = {18--31},
doi = {10.1007/978-3-031-14714-2_2},
abstract = {Algorithm selection wizards are effective and versatile tools
that automatically select an optimization algorithm given
high-level information about the problem and available
computational resources, such as number and type of decision
variables, maximal number of evaluations, possibility to
parallelize evaluations, etc. State-of-the-art algorithm
selection wizards are complex and difficult to improve. We
propose in this work the use of automated configuration
methods for improving their performance by finding better
configurations of the algorithms that compose them. In
particular, we use elitist iterated racing (irace) to find
CMA configurations for specific artificial benchmarks that
replace the hand-crafted CMA configurations currently used in
the NGOpt wizard provided by the Nevergrad platform. We
discuss in detail the setup of irace for the purpose of
generating configurations that work well over the diverse set
of problem instances within each benchmark. Our approach
improves the performance of the NGOpt wizard, even on
benchmark suites that were not part of the tuning by irace.}
}
@incollection{DobNebLop2022ants,
volume = 13491,
series = {Lecture Notes in Computer Science},
address = { Cham, Switzerland},
publisher = {Springer},
editor = { Marco Dorigo and Heiko Hamann and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Jos{\'e} Garc{\'i}a-Nieto and Andries Engelbrecht and Carlo Pinciroli and Volker Strobel and Camacho-Villal\'{o}n, Christian Leonardo},
year = 2022,
booktitle = {Swarm Intelligence, 13th International Conference, ANTS 2022},
author = {Doblas, Daniel and Nebro, Antonio J. and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Jos{\'e} Garc{\'i}a-Nieto and Carlos A. {Coello Coello} },
title = {Automatic Design of Multi-objective Particle Swarm
Optimizers},
doi = {10.1007/978-3-031-20176-9_3},
pages = {28--40}
}
@incollection{PriAllLop2022gecco,
location = {Boston, Massachusetts},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2022},
address = { New York, NY},
year = 2022,
publisher = {ACM Press},
editor = { Jonathan E. Fieldsend and Markus Wagner },
author = { Pricopie, Stefan and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Fare,
Clyde and Benatan, Matt and Joshua D. Knowles },
title = {Expensive Optimization with Production-Graph Resource
Constraints: A First Look at a New Problem Class},
doi = {10.1145/3512290.3528741},
abstract = {We consider a new class of expensive, resource-constrained
optimization problems (here arising from molecular discovery)
where costs are associated with the experiments (or
evaluations) to be carried out during the optimization
process. In the molecular discovery problem, candidate
compounds to be optimized must be synthesized in an iterative
process that starts from a set of purchasable items and
builds up to larger molecules. To produce target molecules,
their required resources are either used from
already-synthesized items in storage or produced themselves
on-demand at an additional cost. Any remaining resources from
the production process are stored for reuse for the next
evaluations. We model these resource dependencies with a
directed acyclic production graph describing the development
process from granular purchasable items to evaluable target
compounds. Moreover, we develop several resource-eficient
algorithms to address this problem. In particular, we develop
resource-aware variants of Random Search heuristics and of
Bayesian Optimization and analyze their performance in terms
of anytime behavior. The experimental results were obtained
from a real-world molecular optimization problem. Our results
suggest that algorithms that encourage exploitation by
reusing existing resources achieve satisfactory results while
using fewer resources overall.},
pages = {840--848},
numpages = 9,
keywords = {molecular discovery, resource constraints, expensive
optimization, production costs}
}
@incollection{LopChiGil2022evo,
fulleditor = { Jim{\'e}nez Laredo, Juan Luis and Hidalgo Perez, J. Ignacio and Oluwatoyin Babaagba, Kehinde},
address = {Switzerland},
series = {Lecture Notes in Computer Science},
volume = 13224,
booktitle = {EvoApplications 2022: Applications of Evolutionary Computation},
publisher = {Springer Nature},
year = 2022,
editor = { Jim{\'e}nez Laredo, Juan Luis and others},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Chicano, Francisco and Rodrigo Gil-Merino},
title = {The Asteroid Routing Problem: A Benchmark for Expensive
Black-Box Permutation Optimization},
pages = {124--140},
abstract = {Inspired by the recent 11th Global Trajectory Optimisation
Competition, this paper presents the asteroid routing problem
(ARP) as a realistic benchmark of algorithms for expensive
bound-constrained black-box optimization in permutation
space. Given a set of asteroids' orbits and a departure
epoch, the goal of the ARP is to find the optimal sequence
for visiting the asteroids, starting from Earth's orbit, in
order to minimize both the cost, measured as the sum of the
magnitude of velocity changes required to complete the trip,
and the time, measured as the time elapsed from the departure
epoch until visiting the last asteroid. We provide
open-source code for generating instances of arbitrary sizes
and evaluating solutions to the problem. As a preliminary
analysis, we compare the results of two methods for expensive
black-box optimization in permutation spaces, namely,
Combinatorial Efficient Global Optimization (CEGO), a
Bayesian optimizer based on Gaussian processes, and
Unbalanced Mallows Model (UMM), an estimation-of-distribution
algorithm based on probabilistic Mallows models. We
investigate the best permutation representation for each
algorithm, either rank-based or order-based. Moreover, we
analyze the effect of providing a good initial solution,
generated by a greedy nearest neighbor heuristic, on the
performance of the algorithms. The results suggest directions
for improvements in the algorithms being compared.},
keywords = {Spacecraft Trajectory Optimization, Unbalanced Mallows Model,
Combinatorial Efficient Global Optimization, Estimation of
Distribution Algorithms, Bayesian Optimization},
supplement = {https://doi.org/10.5281/zenodo.5725837},
doi = {10.1007/978-3-031-02462-7_9},
epub = {https://arxiv.org/abs/2203.15708}
}
@incollection{KimAllLop2022easafe,
location = {Boston, Massachusetts},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2022},
address = { New York, NY},
year = 2022,
publisher = {ACM Press},
editor = { Jonathan E. Fieldsend and Markus Wagner },
author = { Kim, Youngmin and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Are Evolutionary Algorithms Safe Optimizers?},
doi = {10.1145/3512290.3528818},
abstract = {We consider a type of constrained optimization problem, where
the violation of a constraint leads to an irrevocable loss,
such as breakage of a valuable experimental resource/platform
or loss of human life. Such problems are referred to as safe
optimization problems (SafeOPs). While SafeOPs have received
attention in the machine learning community in recent years,
there was little interest in the evolutionary computation
(EC) community despite some early attempts between 2009 and
2011. Moreover, there is a lack of acceptable guidelines on
how to benchmark different algorithms for SafeOPs, an area
where the EC community has significant experience in. Driven
by the need for more eficient algorithms and benchmark
guidelines for SafeOPs, the objective of this paper is to
reignite the interest of the EC community in this problem
class. To achieve this we (i) provide a formal definition of
SafeOPs and contrast it to other types of optimization
problems that the EC community is familiar with, (ii)
investigate the impact of key SafeOP parameters on the
performance of selected safe optimization algorithms, (iii)
benchmark EC against state-of-the-art safe optimization
algorithms from the machine learning community, and (iv)
provide an open-source Python framework to replicate and
extend our work.},
pages = {814--822},
numpages = 9,
keywords = {Bayesian optimization, constrained optimization,
benchmarking, safety constraints, safe optimization}
}
@incollection{VerWanLopDoe2022undersampling,
location = {Boston, Massachusetts},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2022},
address = { New York, NY},
year = 2022,
publisher = {ACM Press},
editor = { Jonathan E. Fieldsend and Markus Wagner },
author = { Diederick Vermetten and Wang, Hao and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Carola Doerr and Thomas B{\"a}ck },
title = {Analyzing the Impact of Undersampling on the Benchmarking and
Configuration of Evolutionary Algorithms},
doi = {10.1145/3512290.3528799},
abstract = {The stochastic nature of iterative optimization heuristics
leads to inherently noisy performance measurements. Since
these measurements are often gathered once and then used
repeatedly, the number of collected samples will have a
significant impact on the reliability of algorithm
comparisons. We show that care should be taken when making
decisions based on limited data. Particularly, we show that
the number of runs used in many benchmarking studies, e.g.,
the default value of 15 suggested by the COCO environment,
can be insufficient to reliably rank algorithms on well-known
numerical optimization benchmarks.Additionally, methods for
automated algorithm configuration are sensitive to
insufficient sample sizes. This may result in the
configurator choosing a "lucky" but poor-performing
configuration despite exploring better ones. We show that
relying on mean performance values, as many configurators do,
can require a large number of runs to provide accurate
comparisons between the considered configurations. Common
statistical tests can greatly improve the situation in most
cases but not always. We show examples of performance losses
of more than 20\%, even when using statistical races to
dynamically adjust the number of runs, as done by irace. Our
results underline the importance of appropriately considering
the statistical distribution of performance values.},
pages = {867--875},
numpages = 9,
keywords = {parameter tuning, evolution strategies, algorithm
configuration, performance measures}
}
@incollection{AyoAllLop2022gecco,
location = {Boston, Massachusetts},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2022},
address = { New York, NY},
year = 2022,
publisher = {ACM Press},
editor = { Jonathan E. Fieldsend and Markus Wagner },
author = { Ayodele, Mayowa and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Parizy, Matthieu },
title = {Multi-Objective {QUBO} Solver: Bi-Objective Quadratic
Assignment Problem},
pages = {467--475},
doi = {10.1145/3512290.3528698},
abstract = {Quantum and quantum-inspired optimisation algorithms are
designed to solve problems represented in binary, quadratic
and unconstrained form. Combinatorial optimisation problems
are therefore often formulated as Quadratic Unconstrained
Binary Optimisation Problems (QUBO) to solve them with these
algorithms. Moreover, these QUBO solvers are often
implemented using specialised hardware to achieve enormous
speedups, e.g. Fujitsu's Digital Annealer (DA) and D-Wave's
Quantum Annealer. However, these are single-objective
solvers, while many real-world problems feature multiple
conflicting objectives. Thus, a common practice when using
these QUBO solvers is to scalarise such multi-objective
problems into a sequence of single-objective problems. Due to
design trade-offs of these solvers, formulating each
scalarisation may require more time than finding a local
optimum. We present the first attempt to extend the algorithm
supporting a commercial QUBO solver as a multi-objective
solver that is not based on scalarisation. The proposed
multi-objective DA algorithm is validated on the bi-objective
Quadratic Assignment Problem. We observe that algorithm
performance significantly depends on the archiving strategy
adopted, and that combining DA with non-scalarisation methods
to optimise multiple objectives outperforms the current
scalarised version of the DA in terms of final solution
quality.},
numpages = 9,
keywords = {digital annealer, multi-objective, bi-objective QAP, QUBO}
}
@incollection{AyoAllLop2022or,
booktitle = {Operations Research Proceedings 2022, OR 2022},
address = { Cham, Switzerland},
series = {Lecture Notes in Operations Research},
year = 2022,
publisher = {Springer},
editor = {Oliver Grothe and Stefan Nickel and Steffen Rebennack and
Oliver Stein},
author = { Ayodele, Mayowa and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Parizy, Matthieu },
title = {A Study of Scalarisation Techniques for Multi-objective
{QUBO} Solving},
pages = {393--399},
doi = {10.1007/978-3-031-24907-5_47}
}
@incollection{ChuLop2021gecco,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2021},
address = { New York, NY},
year = 2021,
publisher = {ACM Press},
editor = { Chicano, Francisco and Krzysztof Krawiec },
author = { Tinkle Chugh and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Maximising Hypervolume and Minimising $\epsilon$-Indicators
using Bayesian Optimisation over Sets},
doi = {10.1145/3449726.3463178},
keywords = {multi-objective, surrogate models, epsilon, hypervolume},
supplement = {https://doi.org/10.5281/zenodo.4675569},
pages = {1326--1334}
}
@incollection{ShaLopKno2021gecco,
location = {Lille, France},
address = { New York, NY},
publisher = {ACM Press},
year = 2021,
editor = { Chicano, Francisco and Krzysztof Krawiec },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021},
author = { Shavarani, Seyed Mahdi and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Joshua D. Knowles },
title = {Realistic Utility Functions Prove Difficult for
State-of-the-Art Interactive Multiobjective Optimization
Algorithms},
pages = {457--465},
doi = {10.1145/3449639.3459373},
pdf = {ShaLopKno2021gecco.pdf}
}
@incollection{KimAllLop2020safe,
year = 2021,
volume = 12641,
address = { Cham, Switzerland},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
booktitle = {Trustworthy AI -- Integrating Learning, Optimization and
Reasoning. TAILOR 2020},
editor = {Fredrik Heintz and Michela Milano and O'Sullivan, Barry },
author = { Kim, Youngmin and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Safe Learning and Optimization Techniques: Towards a Survey
of the State of the Art},
pages = {123--139},
doi = {10.1007/978-3-030-73959-1_12},
abstract = {Safe learning and optimization deals with learning and
optimization problems that avoid, as much as possible, the
evaluation of non-safe input points, which are solutions,
policies, or strategies that cause an irrecoverable loss
(e.g., breakage of a machine or equipment, or life
threat). Although a comprehensive survey of safe
reinforcement learning algorithms was published in 2015, a
number of new algorithms have been proposed thereafter, and
related works in active learning and in optimization were not
considered. This paper reviews those algorithms from a number
of domains including reinforcement learning, Gaussian process
regression and classification, evolutionary computing, and
active learning. We provide the fundamental concepts on which
the reviewed algorithms are based and a characterization of
the individual algorithms. We conclude by explaining how the
algorithms are connected and suggestions for future
research.}
}
@incollection{IruLop2021gecco,
location = {Lille, France},
address = { New York, NY},
publisher = {ACM Press},
year = 2021,
editor = { Chicano, Francisco and Krzysztof Krawiec },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021},
author = { Irurozki, Ekhine and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Unbalanced Mallows Models for Optimizing Expensive Black-Box
Permutation Problems},
pages = {225--233},
doi = {10.1145/3449639.3459366},
supplement = {https://doi.org/10.5281/zenodo.4500974},
abstract = {Expensive black-box combinatorial optimization problems arise
in practice when the objective function is evaluated by means
of a simulator or a real-world experiment. Since each fitness
evaluation is expensive in terms of time or resources, only a
limited number of evaluations is possible, typically several
orders of magnitude smaller than in non-expensive
problems. In this scenario, classical optimization methods
such as mixed-integer programming and local search are not
useful. In the continuous case, Bayesian optimization, in
particular using Gaussian processes, has proven very
effective under these conditions. Much less research is
available in the combinatorial case. In this paper, we
propose and analyze UMM, an estimation-of-distribution (EDA)
algorithm based on a Mallows probabilistic model and
unbalanced rank aggregation (uBorda). Experimental results on
black-box versions of LOP and PFSP show that UMM is able to
match, and sometimes surpass, the solutions obtained by CEGO,
a Bayesian optimization algorithm for combinatorial
optimization. Moreover, the computational complexity of UMM
increases linearly with both the number of function
evaluations and the permutation size.},
keywords = {UMM, Permutation, Expensive, Black-box}
}
@incollection{CinFerLopAl2021evocop,
address = { Cham, Switzerland},
publisher = {Springer},
volume = 12692,
series = {Lecture Notes in Computer Science},
year = 2021,
booktitle = {Proceedings of EvoCOP 2021 -- 21th European Conference on Evolutionary Computation in Combinatorial Optimization },
editor = { Christine Zarges and Verel, S{\'e}bastien },
author = { Christian Cintrano and Javier Ferrer and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Alba, Enrique },
title = {Hybridization of Racing Methods with Evolutionary Operators
for Simulation Optimization of Traffic Lights Programs},
abstract = {In many real-world optimization problems, like the traffic
light scheduling problem tackled here, the evaluation of
candidate solutions requires the simulation of a process
under various scenarios. Thus, good solutions should not only
achieve good objective function values, but they must be
robust (low variance) across all different scenarios.
Previous work has revealed the effectiveness of IRACE for
this task. However, the operators used by IRACE to generate
new solutions were designed for configuring algorithmic
parameters, that have various data types (categorical,
numerical, etc.). Meanwhile, evolutionary algorithms have
powerful operators for numerical optimization, which could
help to sample new solutions from the best ones found in the
search. Therefore, in this work, we propose a hybridization
of the elitist iterated racing mechanism of IRACE with
evolutionary operators from differential evo- lution and
genetic algorithms. We consider a realistic case study
derived from the traffic network of Malaga (Spain) with 275
traffic lights that should be scheduled optimally. After a
meticulous study, we discovered that the hybrid algorithm
comprising IRACE plus differential evolution offers
statistically better results than conventional algorithms and
also improves travel times and reduces pollution.},
keywords = {Hybrid algorithms, Evolutionary algorithms, Simulation
optimization, Uncertainty, Traffic light planning},
pages = {17--33},
doi = {10.1007/978-3-030-72904-2_2},
annote = {Extended version published in Evolutionary Computation journal~\cite{CinFerLopAlb2022irace}.}
}
@incollection{AvrAllLop2021evo,
volume = {12694},
series = {Lecture Notes in Computer Science},
address = { Cham, Switzerland},
publisher = {Springer},
booktitle = {Applications of Evolutionary Computation},
year = 2021,
editor = {Pedro Castillo and Jim{\'e}nez Laredo, Juan Luis },
author = { Andreea Avramescu and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {A Multi-Objective Multi-Type Facility Location Problem for
the Delivery of Personalised Medicine},
pages = {388--403},
doi = {10.1007/978-3-030-72699-7_25},
abstract = {Advances in personalised medicine targeting specific
sub-populations and individuals pose a challenge to the
traditional pharmaceutical industry. With a higher level of
personalisation, an already critical supply chain is facing
additional demands added by the very sensitive nature of its
products. Nevertheless, studies concerned with the efficient
development and delivery of these products are scarce. Thus,
this paper presents the case of personalised medicine and the
challenges imposed by its mass delivery. We propose a
multi-objective mathematical model for the
location-allocation problem with two interdependent facility
types in the case of personalised medicine products. We show
its practical application through a cell and gene therapy
case study. A multi-objective genetic algorithm with a novel
population initialisation procedure is used as solution
method.},
supplement = {https://doi.org/10.5281/zenodo.4495162},
keywords = {Personalised medicine, Biopharmaceuticals Supply chain,
Facility location-allocation, Evolutionary multi-objective
optimisation}
}
@incollection{BezLopStu2020chapter,
address = { Cham, Switzerland},
publisher = {Springer International Publishing},
editor = { Thomas Bartz-Beielstein and Bogdan Filipi{\v c} and P. Koro{\v s}ec and Talbi, El-Ghazali },
year = 2020,
booktitle = {High-Performance Simulation-Based Optimization},
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Configuration of Multi-objective Optimizers and
Multi-objective Configuration},
pages = {69--92},
doi = {10.1007/978-3-030-18764-4_4},
abstract = {Heuristic optimizers are an important tool in academia and industry, and their performance-optimizing configuration requires a significant amount of expertise. As the proper configuration of algorithms is a crucial aspect in the engineering of heuristic algorithms, a significant research effort has been dedicated over the last years towards moving this step to the computer and, thus, make it automatic. These research efforts go way beyond tuning only numerical parameters of already fully defined algorithms, but exploit automatic configuration as a means for automatic algorithm design. In this chapter, we review two main aspects where the research on automatic configuration and multi-objective optimization intersect. The first is the automatic configuration of multi-objective optimizers, where we discuss means and specific approaches. In addition, we detail a case study that shows how these approaches can be used to design new, high-performing multi-objective evolutionary algorithms. The second aspect is the research on multi-objective configuration, that is, the possibility of using multiple performance metrics for the evaluation of algorithm configurations. We highlight some few examples in this direction.}
}
@incollection{StuLop2019hb,
publisher = {Springer},
series = {International Series in Operations Research \& Management
Science},
volume = 272,
booktitle = {Handbook of Metaheuristics},
year = 2019,
editor = { Michel Gendreau and Jean-Yves Potvin },
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Automated Design of Metaheuristic Algorithms},
pages = {541--579},
doi = {10.1007/978-3-319-91086-4_17},
keywords = {automatic design, automatic configuration}
}
@incollection{SaiLopMie2019gecco,
isbn = {978-1-4503-6748-6},
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
author = { Saini, Bhupinder Singh and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Kaisa Miettinen },
title = {Automatic Surrogate Modelling Technique Selection based on
Features of Optimization Problems},
doi = {10.1145/3319619.3326890},
pdf = {SaiLopMie2019gecco.pdf},
pages = {1765--1772},
abstract = {A typical scenario when solving industrial single or
multiobjective optimization problems is that no explicit
formulation of the problem is available. Instead, a dataset
containing vectors of decision variables together with their
objective function value(s) is given and a surrogate model
(or metamodel) is build from the data and used for
optimization and decision-making. This data-driven
optimization process strongly depends on the ability of the
surrogate model to predict the objective value of decision
variables not present in the original dataset. Therefore, the
choice of surrogate modelling technique is crucial. While
many surrogate modelling techniques have been discussed in
the literature, there is no standard procedure that will
select the best technique for a given problem. In this work,
we propose the automatic selection of a surrogate modelling
technique based on exploratory landscape features of the
optimization problem that underlies the given dataset. The
overall idea is to learn offline from a large pool of
benchmark problems, on which we can evaluate a large number
of surrogate modelling techniques. When given a new dataset,
features are used to select the most appropriate surrogate
modelling technique. The preliminary experiments reported
here suggest that the proposed automatic selector is able to
identify high-accuracy surrogate models as long as an
appropriate classifier is used for selection.}
}
@incollection{NebLopBarGar2019gecco,
isbn = {978-1-4503-6748-6},
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
author = { Nebro, Antonio J. and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Barba-Gonz{\'a}lez, Crist{\'o}bal and Jos{\'e} Garc{\'i}a-Nieto },
title = {Automatic Configuration of {NSGA-II} with {jMetal} and irace},
pages = {1374--1381},
doi = {10.1145/3319619.3326832},
pdf = {NebLopBarGar2019gecco.pdf}
}
@incollection{ShaKomLopKaz2019gecco,
isbn = {978-1-4503-6111-8},
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
author = { Mudita Sharma and Alexandros Komninos and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Dimitar Kazakov },
title = {Deep Reinforcement Learning-Based Parameter Control in
Differential Evolution},
pages = {709--717},
supplement = {https://dx.doi.org/10.5281/zenodo.2628228},
doi = {10.1145/3321707.3321813},
pdf = {ShaKomLopKaz2019gecco.pdf},
keywords = {DE-DDQN}
}
@incollection{BezLopStu2019gecco,
isbn = {978-1-4503-6111-8},
address = { New York, NY},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Archiver Effects on the Performance of State-of-the-art
Multi- and Many-objective Evolutionary Algorithms},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2019-004/},
doi = {10.1145/3321707.3321789},
pdf = {BezLopStu2019gecco.pdf}
}
@incollection{MazChuMietLop2019emo,
isbn = {978-3-030-12597-4},
year = 2019,
address = { Cham, Switzerland},
publisher = {Springer International Publishing},
volume = 11411,
series = {Lecture Notes in Computer Science},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2019},
editor = { Kalyanmoy Deb and Erik D. Goodman and Carlos A. {Coello Coello} and Kathrin
Klamroth and Kaisa Miettinen and Sanaz Mostaghim and Patrick
Reed},
author = { Atanu Mazumdar and Tinkle Chugh and Kaisa Miettinen and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {On Dealing with Uncertainties from Kriging Models in Offline
Data-Driven Evolutionary Multiobjective Optimization},
pages = {463--474},
doi = {10.1007/978-3-030-12598-1_37}
}
@incollection{ShaLopKaz2018ppsn,
volume = 11102,
year = 2018,
address = { Cham, Switzerland},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Anne Auger and Carlos M. Fonseca and Louren{\c c}o, N. and Penousal Machado and Lu{\'i}s Paquete and Darrell Whitley },
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XV}},
author = { Mudita Sharma and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Dimitar Kazakov },
title = {Performance Assessment of Recursive Probability Matching for
Adaptive Operator Selection in Differential Evolution},
supplement = {https://github.com/mudita11/AOS-comparisons},
doi = {10.1007/978-3-319-99259-4_26},
pages = {321--333},
keywords = {Rec-PM}
}
@incollection{LopStuDor2017aco,
isbn = {978-3-319-07125-1},
publisher = {Springer International Publishing},
year = 2018,
booktitle = {Handbook of Heuristics},
editor = { Rafael Mart{\'i} and Panos M. Pardalos and Mauricio G. C. Resende },
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle and Marco Dorigo },
title = {Ant Colony Optimization: A Component-Wise Overview},
pages = {371--407},
annote = {Proposed ACOTSPQAP software},
doi = {10.1007/978-3-319-07124-4_21},
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,
address = { Cham, Switzerland},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Anne Auger and Carlos M. Fonseca and Louren{\c c}o, N. and Penousal Machado and Lu{\'i}s Paquete and Darrell Whitley },
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XV}},
author = { Aymeric Blot and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Marie-El{\'e}onore Kessaci-Marmion and Laetitia Jourdan },
title = {New Initialisation Techniques for Multi-Objective Local
Search: Application to the Bi-objective Permutation Flowshop},
doi = {10.1007/978-3-319-99253-2_26},
pages = {323--334}
}
@incollection{LieLopPaqVer2018gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2018,
editor = { Aguirre, Hern\'{a}n E. and Keiki Takadama},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018},
author = { Arnaud Liefooghe and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Verel, S{\'e}bastien },
title = {Dominance, Epsilon, and Hypervolume Local Optimal Sets in
Multi-objective Optimization, and How to Tell the Difference},
pages = {324--331},
doi = {10.1145/3205455.3205572},
pdf = {LieLopPaqVer2018gecco.pdf}
}
@incollection{LieDerVerLop2018ppsn,
volume = 11102,
year = 2018,
address = { Cham, Switzerland},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Anne Auger and Carlos M. Fonseca and Louren{\c c}o, N. and Penousal Machado and Lu{\'i}s Paquete and Darrell Whitley },
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XV}},
author = { Arnaud Liefooghe and Bilel Derbel and Verel, S{\'e}bastien and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Aguirre, Hern\'{a}n E. and Tanaka, Kiyoshi },
title = {On {Pareto} Local Optimal Solutions Networks},
pages = {232--244},
doi = {10.1007/978-3-319-99259-4_19}
}
@incollection{StuLop2017gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2017,
editor = { Peter A. N. Bosman },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2017},
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Automated Offline Design of Algorithms},
pages = {1038--1065},
doi = {10.1145/3067695.3067722}
}
@incollection{BezLopStu2017emo,
editor = {Heike Trautmann and G{\"{u}}nter Rudolph and Kathrin Klamroth
and Oliver Sch{\"{u}}tze and Margaret M. Wiecek and Yaochu
Jin and Christian Grimme},
year = 2017,
volume = 10173,
series = {Lecture Notes in Computer Science},
address = { Cham, Switzerland},
publisher = {Springer International Publishing},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2017},
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An Empirical Assessment of the Properties of Inverted
Generational Distance Indicators on Multi- and Many-objective
Optimization},
pages = {31--45},
doi = {10.1007/978-3-319-54157-0_3}
}
@incollection{PerLopHooStu2017:lion,
address = { Cham, Switzerland},
series = {Lecture Notes in Computer Science},
volume = 10556,
booktitle = {Learning and Intelligent Optimization, 11th International Conference, LION 11},
publisher = {Springer},
year = 2017,
editor = { Roberto Battiti and Dmitri E. Kvasov and Yaroslav D. Sergeyev},
author = { P{\'e}rez C{\'a}ceres, Leslie and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Holger H. Hoos and Thomas St{\"u}tzle },
title = {An Experimental Study of Adaptive Capping in {\rpackage{irace}}},
pages = {235--250},
pdf = {PerLopHooStu2017lion.pdf},
doi = {10.1007/978-3-319-69404-7_17},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2016-007/}
}
@incollection{StuLop2015gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2015,
editor = { Jim{\'e}nez Laredo, Juan Luis and Sara Silva and Anna I. Esparcia{-}Alc{\'{a}}zar },
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2015},
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Automatic (Offline) Configuration of Algorithms},
pages = {681--702},
doi = {10.1145/2739482.2756581}
}
@incollection{LopKno2015emo,
editor = { Ant{\'o}nio Gaspar{-}Cunha and Carlos Henggeler Antunes and Carlos A. {Coello Coello} },
volume = 9019,
year = 2015,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2015 Part {II}},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Joshua D. Knowles },
title = {Machine Decision Makers as a Laboratory for Interactive {EMO}},
pages = {295--309},
abstract = {A key challenge, perhaps the central challenge, of
multi-objective optimization is how to deal with candidate
solutions that are ultimately evaluated by the hidden or
unknown preferences of a human decision maker (DM) who
understands and cares about the optimization problem.
Alternative ways of addressing this challenge exist but
perhaps the favoured one currently is the interactive
approach (proposed in various forms). Here, an evolutionary
multi-objective optimization algorithm (EMOA) is controlled
by a series of interactions with the DM so that preferences
can be elicited and the direction of search controlled. MCDM
has a key role to play in designing and evaluating these
approaches, particularly in testing them with real DMs, but
so far quantitative assessment of interactive EMOAs has been
limited. In this paper, we propose a conceptual framework
for this problem of quantitative assessment, based on the
definition of machine decision makers (machine DMs), made
somewhat realistic by the incorporation of various
non-idealities. The machine DM proposed here draws from
earlier models of DM biases and inconsistencies in the MCDM
literature. As a practical illustration of our approach, we
use the proposed machine DM to study the performance of an
interactive EMOA, and discuss how this framework could help
in the evaluation and development of better interactive
EMOAs.},
doi = {10.1007/978-3-319-15892-1_20},
pdf = {LopKno2015emo.pdf}
}
@incollection{BezLopStu2015emode,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2015 Part {I}},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 9018,
year = 2015,
publisher = {Springer},
editor = { Ant{\'o}nio Gaspar{-}Cunha and Carlos Henggeler Antunes and Carlos A. {Coello Coello} },
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {To {DE} or Not to {DE}? {Multi}-objective Differential
Evolution Revisited from a Component-Wise Perspective},
pages = {48--63},
doi = {10.1007/978-3-319-15934-8_4}
}
@incollection{BezLopStu2015moead,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2015 Part {I}},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 9018,
year = 2015,
publisher = {Springer},
editor = { Ant{\'o}nio Gaspar{-}Cunha and Carlos Henggeler Antunes and Carlos A. {Coello Coello} },
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Comparing De\-com\-po\-sition-Based and Automatically
Component-Wise Designed Multi-Objective Evolutionary
Algorithms},
pages = {396--410},
doi = {10.1007/978-3-319-15934-8_27}
}
@incollection{BraCorGre2015dagstuhl,
keywords = {multiple criteria decision making, evolutionary
multiobjective optimization},
doi = {10.4230/DagRep.5.1.96},
volume = {5(1)},
year = 2015,
series = {Dagstuhl Reports},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik, Germany},
booktitle = {Understanding Complexity in Multiobjective Optimization
(Dagstuhl Seminar 15031)},
editor = { Salvatore Greco and Kathrin Klamroth and Joshua D. Knowles and G{\"u}nther Rudolph },
author = { J{\"u}rgen Branke and Salvatore Corrente and Salvatore Greco and Kadzi{\'n}ski, Mi{\l}osz and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Vincent Mousseau and Mauro Munerato and Roman S{\l}owi{\'n}ski },
title = {Behavior-Realistic Artificial Decision-Makers to Test
Preference-Based Multi-objective Optimization Method
({Working} {Group} ``{Machine} {Decision}-{Making}'')},
pages = {110--116}
}
@incollection{LopLieVer2014ppsn,
year = 2014,
volume = 8672,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Thomas Bartz-Beielstein and J{\"u}rgen Branke and Bogdan Filipi{\v c} and Jim Smith},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XIII}},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Arnaud Liefooghe and Verel, S{\'e}bastien },
doi = {10.1007/978-3-319-10762-2_61},
title = {Local Optimal Sets and Bounded Archiving on Multi-objective
{NK}-Landscapes with Correlated Objectives},
pages = {621--630},
pdf = {LopLieVer2014ppsn.pdf},
abstract = {The properties of local optimal solutions in multi-objective
combinatorial optimization problems are crucial for the
effectiveness of local search algorithms, particularly when
these algorithms are based on Pareto dominance. Such local
search algorithms typically return a set of mutually
nondominated Pareto local optimal (PLO) solutions, that is, a
PLO-set. This paper investigates two aspects of PLO-sets by
means of experiments with Pareto local search (PLS). First,
we examine the impact of several problem characteristics on
the properties of PLO-sets for multi-objective NK-landscapes
with correlated objectives. In particular, we report that
either increasing the number of objectives or decreasing the
correlation between objectives leads to an exponential
increment on the size of PLO-sets, whereas the variable
correlation has only a minor effect. Second, we study the
running time and the quality reached when using bounding
archiving methods to limit the size of the archive handled by
PLS, and thus, the maximum size of the PLO-set found. We
argue that there is a clear relationship between the running
time of PLS and the difficulty of a problem instance.}
}
@incollection{BezLopStu2014:lion,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 8426,
booktitle = {Learning and Intelligent Optimization, 8th International Conference, LION 8},
publisher = {Springer},
year = 2014,
editor = { Panos M. Pardalos and Mauricio G. C. Resende and Chrysafis Vogiatzis and Jose
L. Walteros},
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Deconstructing Multi-Objective Evolutionary Algorithms: An
Iterative Analysis on the Permutation Flowshop},
pages = {57--172},
doi = {10.1007/978-3-319-09584-4_16},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2013-010/}
}
@incollection{BezLopStu2014:ppsn,
year = 2014,
volume = 8672,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Thomas Bartz-Beielstein and J{\"u}rgen Branke and Bogdan Filipi{\v c} and Jim Smith},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XIII}},
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Design of Evolutionary Algorithms for
Multi-Objective Combinatorial Optimization},
doi = {10.1007/978-3-319-10762-2_50},
pages = {508--517}
}
@incollection{HutLopFaw2014lion,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 8426,
booktitle = {Learning and Intelligent Optimization, 8th International Conference, LION 8},
publisher = {Springer},
year = 2014,
editor = { Panos M. Pardalos and Mauricio G. C. Resende and Chrysafis Vogiatzis and Jose
L. Walteros},
author = { Frank Hutter and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Chris Fawcett and Marius Thomas Lindauer and Holger H. Hoos and Kevin Leyton-Brown and Thomas St{\"u}tzle },
title = {{AClib}: A Benchmark Library for Algorithm Configuration},
pages = {36--40},
doi = {10.1007/978-3-319-09584-4_4},
pdf = {HutLopFaw2014lion.pdf}
}
@incollection{MasLopDub2014hm,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 8457,
isbn = {978-3-319-07643-0},
editor = { Mar{\'i}a J. Blesa and Christian Blum and Stefan Vo{\ss} },
year = 2014,
booktitle = {Hybrid Metaheuristics},
author = { Franco Mascia and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and Marie-El{\'e}onore Marmion and Thomas St{\"u}tzle },
title = {Algorithm Comparison by Automatically Configurable Stochastic
Local Search Frameworks: A Case Study Using Flow-Shop
Scheduling Problems},
pages = {30--44},
pdf = {MasLopDu2014hm.pdf},
doi = {10.1007/978-3-319-07644-7_3}
}
@incollection{PerLopStu2014ants,
volume = 8667,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Marco Dorigo and others},
year = 2014,
booktitle = {Swarm Intelligence, 9th International Conference, ANTS 2014},
author = { P{\'e}rez C{\'a}ceres, Leslie and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Ant Colony Optimization on a Budget of 1000},
doi = {10.1007/978-3-319-09952-1_5},
pages = {50--61}
}
@incollection{PerLopStu2014evocop,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 8600,
series = {Lecture Notes in Computer Science},
year = 2014,
booktitle = {Proceedings of EvoCOP 2014 -- 14th European Conference on Evolutionary Computation in Combinatorial Optimization },
editor = { Christian Blum and Gabriela Ochoa },
author = { P{\'e}rez C{\'a}ceres, Leslie and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An Analysis of Parameters of irace},
doi = {10.1007/978-3-662-44320-0_4},
pages = {37--48}
}
@incollection{MarMasLop2013hm,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 7919,
series = {Lecture Notes in Computer Science},
editor = { Mar{\'i}a J. Blesa and Christian Blum and Paola Festa and Andrea Roli and M. Sampels },
isbn = {978-3-642-38515-5},
year = 2013,
booktitle = {Hybrid Metaheuristics},
author = { Marie-El{\'e}onore Marmion and Franco Mascia and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Design of Hybrid Stochastic Local Search
Algorithms},
pages = {144--158},
doi = {10.1007/978-3-642-38516-2_12},
pdf = {MarMasLopStu2013hm.pdf}
}
@incollection{BezLopStu2013evocop,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
year = 2013,
volume = 7832,
booktitle = {Proceedings of EvoCOP 2013 -- 13th European Conference on Evolutionary Computation in Combinatorial Optimization },
editor = { Martin Middendorf and Christian Blum },
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An Analysis of Local Search for the Bi-objective
Bidimensional Knapsack Problem},
pages = {85--96},
doi = {10.1007/978-3-642-37198-1_8}
}
@incollection{DubLopStu2013hm,
url = {http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-30670-9},
year = 2013,
volume = 434,
series = {Studies in Computational Intelligence},
editor = { Talbi, El-Ghazali },
publisher = {Springer Verlag},
booktitle = {Hybrid Metaheuristics},
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Combining Two Search Paradigms for Multi-objective
Optimization: {Two}-{Phase} and {Pareto} Local Search},
pages = {97--117},
doi = {10.1007/978-3-642-30671-6_3},
alias = {DubLopStu2012hm},
pdf = {DubLopStu2013hm.pdf}
}
@incollection{MasLopDubStu2013lion,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 7997,
booktitle = {Learning and Intelligent Optimization, 7th International Conference, LION 7},
publisher = {Springer},
year = 2013,
editor = { Panos M. Pardalos and G. Nicosia},
author = { Franco Mascia and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and Thomas St{\"u}tzle },
title = {From Grammars to Parameters: Automatic Iterated Greedy Design
for the Permutation Flow-shop Problem with Weighted
Tardiness},
pages = {321--334},
pdf = {MasLopDubStu2013lion.pdf},
doi = {10.1007/978-3-642-44973-4_36}
}
@incollection{MasLopStu2013hm,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 7919,
series = {Lecture Notes in Computer Science},
editor = { Mar{\'i}a J. Blesa and Christian Blum and Paola Festa and Andrea Roli and M. Sampels },
isbn = {978-3-642-38515-5},
year = 2013,
booktitle = {Hybrid Metaheuristics},
author = { Florence Massen and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle and Yves Deville },
title = {Experimental Analysis of Pheromone-Based Heuristic
Column Generation Using irace},
pages = {92--106},
doi = {10.1007/978-3-642-38516-2_8},
pdf = {MasLopStu2013hm.pdf}
}
@incollection{RadLopStu2013emo,
isbn = {978-3-642-37139-4},
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2013},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 7811,
year = 2013,
publisher = {Springer},
editor = { Robin C. Purshouse and Peter J. Fleming and Carlos M. Fonseca and Salvatore Greco and Jane Shaw},
author = { Andreea Radulescu and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatically Improving the Anytime Behaviour of
Multiobjective Evolutionary Algorithms},
pages = {825--840},
doi = {10.1007/978-3-642-37140-0_61}
}
@incollection{StuLopPel2011autsea,
year = 2012,
address = { Berlin, Germany},
publisher = {Springer},
booktitle = {Autonomous Search},
editor = { Youssef Hamadi and E. Monfroy and F. Saubion},
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Paola Pellegrini and Michael Maur and Marco A. {Montes de Oca} and Mauro Birattari and Marco Dorigo },
title = {Parameter Adaptation in Ant Colony Optimization},
doi = {10.1007/978-3-642-21434-9_8},
pages = {191--215}
}
@incollection{LopLiaStu2012ppsn,
volume = 7491,
year = 2012,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
fulleditor = { Carlos A. {Coello Coello} and Vincenzo Cutello and Kalyanmoy Deb and Stephanie
Forrest and Giuseppe Nicosia and Mario Pavone},
editor = { Carlos A. {Coello Coello} and others},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XII}, Part {I}},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Liao, Tianjun and Thomas St{\"u}tzle },
title = {On the anytime behavior of {IPOP-CMA-ES}},
pages = {357--366},
doi = {10.1007/978-3-642-32937-1_36}
}
@incollection{BezLopStu2012:ants,
volume = 7461,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
editor = { Marco Dorigo and others},
year = 2012,
booktitle = {Swarm Intelligence, 8th International Conference, ANTS 2012},
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Generation of Multi-Objective {ACO}
Algorithms for the Biobjective Knapsack},
pages = {37--48},
doi = {10.1007/978-3-642-32650-9_4},
pdf = {BezLopStu2012ants.pdf},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2012-008/}
}
@incollection{DubLopStu2012evocop,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 7245,
year = 2012,
editor = { Jin-Kao Hao and Martin Middendorf },
booktitle = {Proceedings of EvoCOP 2012 -- 12th European Conference on Evolutionary Computation in Combinatorial Optimization },
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {{Pareto} Local Search Algorithms for Anytime
Bi-objective Optimization},
pages = {206--217},
doi = {10.1007/978-3-642-29124-1_18},
alias = {DubLopStu12:evocop}
}
@incollection{BroLopNau2012ppsn,
volume = 7491,
year = 2012,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
fulleditor = { Carlos A. {Coello Coello} and Vincenzo Cutello and Kalyanmoy Deb and Stephanie
Forrest and Giuseppe Nicosia and Mario Pavone},
editor = { Carlos A. {Coello Coello} and others},
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XII}, Part {I}},
author = { Dimo Brockhoff and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Boris Naujoks and G{\"u}nther Rudolph },
title = {Runtime Analysis of Simple Interactive Evolutionary
Biobjective Optimization Algorithms},
pages = {123--132},
doi = {10.1007/978-3-642-32937-1_13},
abstract = {Development and deployment of interactive evolutionary
multiobjective optimization algorithms (EMOAs) have recently
gained broad interest. In this study, first steps towards a
theory of interactive EMOAs are made by deriving bounds on
the expected number of function evaluations and queries to a
decision maker. We analyze randomized local search and the
(1+1)-EA on the biobjective problems LOTZ and COCZ under the
scenario that the decision maker interacts with these
algorithms by providing a subjective preference whenever
solutions are incomparable. It is assumed that this decision
is based on the decision maker's internal utility
function. We show that the performance of the interactive
EMOAs may dramatically worsen if the utility function is
non-linear instead of linear.}
}
@incollection{AugBroLop2012dagstuhl,
doi = {10.4230/DagRep.2.1.50},
series = {Dagstuhl Reports},
volume = {2(1)},
year = 2012,
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik, Germany},
booktitle = {Learning in Multiobjective Optimization (Dagstuhl Seminar
12041)},
editor = { Salvatore Greco and Joshua D. Knowles and Kaisa Miettinen and Eckart Zitzler },
author = { Anne Auger and Dimo Brockhoff and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Kaisa Miettinen and Boris Naujoks and G{\"u}nther Rudolph },
title = {Which questions should be asked to find the most appropriate
method for decision making and problem solving? ({Working}
{Group} ``{Algorithm} {Design} {Methods}'')},
pages = {92--93}
}
@incollection{StuLopDor2011eorms,
year = 2011,
publisher = {John Wiley \& Sons},
editor = {J. J. Cochran},
booktitle = {Wiley Encyclopedia of Operations Research and
Management Science},
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Marco Dorigo },
title = {A Concise Overview of Applications of Ant Colony
Optimization},
pages = {896--911},
volume = 2,
doi = {10.1002/9780470400531.eorms0001}
}
@incollection{LopKnoLau2011emo,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2011},
address = {Berlin\slash Heidelberg},
series = {Lecture Notes in Computer Science},
volume = 6576,
year = 2011,
publisher = {Springer},
editor = { Takahashi, R. H. C. and Kalyanmoy Deb and Wanner, Elizabeth F. and Salvatore Greco },
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Joshua D. Knowles and Marco Laumanns },
title = {On Sequential Online Archiving of Objective Vectors},
pages = {46--60},
doi = {10.1007/978-3-642-19893-9_4},
abstract = {In this paper, we examine the problem of maintaining
an approximation of the set of nondominated points
visited during a multiobjective optimization, a
problem commonly known as archiving. Most of the
currently available archiving algorithms are
reviewed, and what is known about their convergence
and approximation properties is summarized. The main
scenario considered is the restricted case where the
archive must be updated online as points are
generated one by one, and at most a fixed number of
points are to be stored in the archive at any one
time. In this scenario, the better-monotonicity of
an archiving algorithm is proposed as a weaker, but
more practical, property than negative efficiency
preservation. This paper shows that
hypervolume-based archivers and a recently proposed
multi-level grid archiver have this property. On the
other hand, the archiving methods used by SPEA2 and
NSGA-II do not, and they may better-deteriorate with
time. The better-monotonicity property has meaning
on any input sequence of points. We also classify
archivers according to limit properties,
i.e. convergence and approximation properties of the
archiver in the limit of infinite (input) samples
from a finite space with strictly positive
generation probabilities for all points. This paper
establishes a number of research questions, and
provides the initial framework and analysis for
answering them.},
annote = {Revised version available at \url{http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2011-001.pdf}}
}
@incollection{DubLopStu2011gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2011,
editor = {Natalio Krasnogor and Pier Luca Lanzi},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011},
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Configuration of State-of-the-art Multi-Objective
Optimizers Using the {TP+PLS} Framework},
pages = {2019--2026},
doi = {10.1145/2001576.2001847}
}
@incollection{BluLop2011ieh,
author = { Christian Blum and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
booktitle = {The Industrial Electronics Handbook: Intelligent Systems},
title = {Ant Colony Optimization},
publisher = {CRC Press},
year = 2011,
edition = {2nd},
isbn = 9781439802830,
url = {http://www.crcpress.com/product/isbn/9781439802830},
annnote = {http://www.eng.auburn.edu/~wilambm/ieh/}
}
@incollection{FonGueLopPaq2011emo,
booktitle = { Evolutionary Multi-criterion Optimization, EMO 2011},
address = {Berlin\slash Heidelberg},
series = {Lecture Notes in Computer Science},
volume = 6576,
year = 2011,
publisher = {Springer},
editor = { Takahashi, R. H. C. and Kalyanmoy Deb and Wanner, Elizabeth F. and Salvatore Greco },
author = { Carlos M. Fonseca and Andreia P. Guerreiro and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete },
title = {On the Computation of the Empirical Attainment Function},
doi = {10.1007/978-3-642-19893-9_8},
pages = {106--120},
abstract = {The attainment function provides a description of the
location of the distribution of a random non-dominated point
set. This function can be estimated from experimental data
via its empirical counterpart, the empirical attainment
function (EAF). However, computation of the EAF in more than
two dimensions is a non-trivial task. In this article, the
problem of computing the empirical attainment function is
formalised, and upper and lower bounds on the corresponding
number of output points are presented. In addition, efficient
algorithms for the two and three-dimensional cases are
proposed, and their time complexities are related to lower
bounds derived for each case.},
pdf = {FonGueLopPaq2011emo.pdf}
}
@incollection{LopStu09ea,
address = { Heidelberg, Germany},
publisher = {Springer},
editor = {Pierre Collet and Nicolas Monmarch{\'e} and Pierrick
Legrand and Marc Schoenauer and Evelyne Lutton},
shorteditor = {Pierre Collet and others},
volume = 5975,
series = {Lecture Notes in Computer Science},
year = 2010,
booktitle = {Artificial Evolution: 9th International Conference, Evolution Artificielle, EA, 2009},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An Analysis of Algorithmic Components for
Multiobjective Ant Colony Optimization: {A} Case
Study on the Biobjective {TSP}},
pages = {134--145},
doi = {10.1007/978-3-642-14156-0_12}
}
@incollection{LopStu2010:ants,
volume = 6234,
series = {Lecture Notes in Computer Science},
address = { Heidelberg, Germany},
publisher = {Springer},
fulleditor = { Marco Dorigo and Mauro Birattari and Gianni A. {Di Caro} and Doursat, R. and Engelbrecht, A. P. and Floreano,
D. and Gambardella, L. M. and Gro\ss, R. and Sahin,
E. and Thomas St{\"u}tzle and Sayama, H.},
editor = { Marco Dorigo and others},
year = 2010,
booktitle = {Swarm Intelligence, 7th International Conference, ANTS 2010},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Configuration of Multi-Objective {ACO}
Algorithms},
pages = {95--106},
doi = {10.1007/978-3-642-15461-4_9},
abstract = {In the last few years a significant number of ant
colony optimization (ACO) algorithms have been
proposed for tackling multi-objective optimization
problems. In this paper, we propose a software
framework that allows to instantiate the most
prominent multi-objective ACO (MOACO)
algorithms. More importantly, the flexibility of
this MOACO framework allows the application of
automatic algorithm configuration techniques. The
experimental results presented in this paper show
that such an automatic configuration of MOACO
algorithms is highly desirable, given that our
automatically configured algorithms clearly
outperform the best performing MOACO algorithms that
have been proposed in the literature. As far as we
are aware, this paper is also the first to apply
automatic algorithm configuration techniques to
multi-objective stochastic local search algorithms.}
}
@incollection{LopStu2010:gecco,
address = { New York, NY},
publisher = {ACM Press},
year = 2010,
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2010},
editor = {Martin Pelikan and J{\"u}rgen Branke },
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {The impact of design choices of multi-objective ant
colony optimization algorithms on performance: An
experimental study on the biobjective {TSP}},
doi = {10.1145/1830483.1830494},
pages = {71--78},
abstract = {Over the last few years, there have been a number of
proposals of ant colony optimization (ACO)
algorithms for tackling multiobjective combinatorial
optimization problems. These proposals adapt ACO
concepts in various ways, for example, some use
multiple pheromone matrices and multiple heuristic
matrices and others use multiple ant colonies.\\ In
this article, we carefully examine several of the
most prominent of these proposals. In particular, we
identify commonalities among the approaches by
recasting the original formulation of the algorithms
in different terms. For example, several proposals
described in terms of multiple colonies can be cast
equivalently using a single ant colony, where ants
use different weights for aggregating the pheromone
and/or the heuristic information. We study
algorithmic choices for the various proposals and we
identify previously undetected trade-offs in their
performance.}
}
@incollection{LopPaqStu09emaa,
editor = { Thomas Bartz-Beielstein and Marco Chiarandini and Lu{\'i}s Paquete and Mike Preuss },
year = 2010,
address = {Berlin\slash Heidelberg},
publisher = {Springer},
booktitle = {Experimental Methods for the Analysis of
Optimization Algorithms},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {Exploratory Analysis of Stochastic Local Search
Algorithms in Biobjective Optimization},
pages = {209--222},
doi = {10.1007/978-3-642-02538-9_9},
abstract = {This chapter introduces two Perl programs that
implement graphical tools for exploring the
performance of stochastic local search algorithms
for biobjective optimization problems. These tools
are based on the concept of the empirical attainment
function (EAF), which describes the probabilistic
distribution of the outcomes obtained by a
stochastic algorithm in the objective space. In
particular, we consider the visualization of
attainment surfaces and differences between the
first-order EAFs of the outcomes of two
algorithms. This visualization allows us to identify
certain algorithmic behaviors in a graphical way.
We explain the use of these visualization tools and
illustrate them with examples arising from
practice.}
}
@incollection{DubLopStu10:lion-bfsp,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 6073,
booktitle = {Learning and Intelligent Optimization, 4th International Conference, LION 4},
publisher = {Springer},
year = 2010,
editor = { Christian Blum and Roberto Battiti },
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Adaptive ``Anytime'' Two-Phase Local Search},
pages = {52--67},
doi = {10.1007/978-3-642-13800-3_5}
}
@incollection{LopBlu09:evocop,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 5482,
year = 2009,
editor = { Carlos Cotta and P. Cowling},
booktitle = {Proceedings of EvoCOP 2009 -- 9th European Conference on Evolutionary Computation in Combinatorial Optimization },
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum and Dhananjay Thiruvady and Andreas T. Ernst and Bernd Meyer },
title = {Beam-{ACO} based on stochastic sampling for makespan
optimization concerning the {TSP} with time windows},
pages = {97--108},
pdf = {LopBlu09-Beam-ACO-TSPTW-evocop.pdf},
doi = {10.1007/978-3-642-01009-5_9},
alias = {Lop++09}
}
@incollection{LopBlu09:lion,
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 5851,
booktitle = {Learning and Intelligent Optimization, Third International Conference, LION 3},
publisher = {Springer},
year = 2009,
editor = { Thomas St{\"u}tzle },
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum },
title = {Beam-{ACO} Based on Stochastic Sampling: {A} Case
Study on the {TSP} with Time Windows},
pages = {59--73},
doi = {10.1007/978-3-642-11169-3_5}
}
@incollection{DubLopStu09:hm-bfsp,
address = { Heidelberg, Germany},
publisher = {Springer},
volume = 5818,
series = {Lecture Notes in Computer Science},
editor = { Mar{\'i}a J. Blesa and Christian Blum and Luca {Di Gaspero} and Andrea Roli and M. Sampels and Andrea Schaerf},
year = 2009,
booktitle = {Hybrid Metaheuristics},
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Effective Hybrid Stochastic Local Search Algorithms for
Biobjective Permutation Flowshop Scheduling},
pages = {100--114},
pdf = {DubLopStu09hm-bfsp.pdf},
doi = {10.1007/978-3-642-04918-7_8},
alias = {DuboisHM09}
}
@incollection{LopPraPae:gecco07,
address = { New York, NY},
publisher = {ACM Press},
year = 2007,
editor = {Dirk Thierens and others},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Solving Optimal Pump Control Problem using
{\MaxMinAntSystem}},
volume = 1,
pages = 176,
doi = {10.1145/1276958.1276990},
pdf = {pap212s1-lopezibanez.pdf}
}
@incollection{PaqStuLop07metaheuristics,
author = { Lu{\'i}s Paquete and Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Using experimental design to analyze stochastic local search
algorithms for multiobjective problems},
booktitle = {Metaheuristics: Progress in Complex Systems Optimization},
pages = {325--344},
year = 2007,
doi = {10.1007/978-0-387-71921-4_17},
volume = 39,
series = {Operations Research / Computer Science Interfaces},
publisher = {Springer},
address = { New York, NY},
annote = {Post-Conference Proceedings of the 6th Metaheuristics
International Conference (MIC 2005)},
editor = {Karl F. Doerner and Michel Gendreau and Peter Greistorfer and Gutjahr, Walter J. and Richard F. Hartl and Marc Reimann },
abstract = {Stochastic Local Search (SLS) algorithms can be seen as being
composed of several algorithmic components, each playing some
specific role with respect to overall performance. This
article explores the application of experimental design
techniques to analyze the effect of components of SLS
algorithms for Multiobjective Combinatorial Optimization
problems, in particular for the Biobjective Quadratic
Assignment Problem. The analysis shows that there exists a
strong dependence between the choices for these components
and various instance features, such as the structure of the
input data and the correlation between the objectives.}
}
@incollection{LopPaqStu04:ants,
address = { Heidelberg, Germany},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 3172,
editor = { Marco Dorigo and others},
fulleditor = { Marco Dorigo and L. M. Gambardella and Francesco Mondada and Thomas St{\"u}tzle and Mauro Birattari and Christian Blum },
year = 2004,
booktitle = {Ant Colony Optimization and Swarm Intelligence, 4th
International Workshop, ANTS 2004 },
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {On the Design of {ACO} for the Biobjective Quadratic
Assignment Problem},
pages = {214--225},
doi = {10.1007/978-3-540-28646-2_19},
pdf = {LopPaqStu2004antsbQAP.pdf}
}
@book{ANTS2022,
title = {Swarm Intelligence, 13th International Conference, ANTS 2022,
M\'alaga, Spain, November 2-4, 2022, Proceedings},
booktitle = {Swarm Intelligence, 13th International Conference, ANTS 2022},
year = 2022,
editor = { Marco Dorigo and Heiko Hamann and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Jos{\'e} Garc{\'i}a-Nieto and Andries Engelbrecht and Carlo Pinciroli and Volker Strobel and Camacho-Villal\'{o}n, Christian Leonardo},
publisher = {Springer},
address = { Cham, Switzerland},
series = {Lecture Notes in Computer Science},
volume = 13491,
doi = {10.1007/978-3-031-20176-9}
}
@book{GECCO2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
title = {Proceedings of the Genetic and Evolutionary Computation
Conference, {GECCO} 2019, Prague, Czech Republic, July 13-17,
2019},
year = 2019,
publisher = {ACM Press},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019},
address = { New York, NY},
isbn = {978-1-4503-6111-8},
doi = {10.1145/3321707}
}
@book{GECCO2019c,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
title = {Genetic and Evolutionary Computation Conference Companion,
{GECCO} 2019, Prague, Czech Republic, July 13-17, 2019},
year = 2019,
publisher = {ACM Press},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019},
address = { New York, NY},
isbn = {978-1-4503-6748-6},
doi = {10.1145/3319619}
}
@book{EVOCOP2018,
editor = { Arnaud Liefooghe and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Evolutionary Computation in Combinatorial Optimization --
18th European Conference, EvoCOP 2018, Parma, Italy, April
4-6, 2018, Proceedings},
booktitle = {Proceedings of EvoCOP 2018 -- 18th European Conference on Evolutionary Computation in Combinatorial Optimization },
year = 2018,
series = {Lecture Notes in Computer Science},
volume = 10782,
doi = {10.1007/978-3-319-77449-7},
publisher = {Springer},
address = { Heidelberg, Germany}
}
@book{EVOCOP2017,
editor = { Bin Hu and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Evolutionary Computation in Combinatorial Optimization -- 17th
European Conference, EvoCOP 2017, Amsterdam, The Netherlands,
April 19-21, 2017, Proceedings},
booktitle = {Proceedings of EvoCOP 2017 -- 17th European Conference on Evolutionary Computation in Combinatorial Optimization },
year = 2017,
series = {Lecture Notes in Computer Science},
volume = 10197,
doi = {10.1007/978-3-319-55453-2},
publisher = {Springer},
address = { Heidelberg, Germany}
}
@book{ANTS2016,
title = {Swarm Intelligence, 10th International Conference, ANTS 2016,
Brussels, Belgium, September 7-9, 2016, Proceedings},
booktitle = {Swarm Intelligence, 10th International Conference, ANTS 2016},
year = 2016,
editor = { Marco Dorigo and Mauro Birattari and Li, Xiaodong and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Kazuhiro Ohkura and Carlo Pinciroli and Thomas St{\"u}tzle },
publisher = {Springer},
address = { Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 9882,
doi = {10.1007/978-3-319-44427-7}
}
@book{PPSN2016,
booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XIV}},
title = {Parallel Problem Solving from Nature - PPSN XIV 14th
International Conference, Edinburgh, UK, September 17-21,
2016, Proceedings},
editor = { Julia Handl and Emma Hart and Lewis, P. R. and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Gabriela Ochoa and Ben Paechter },
series = {Lecture Notes in Computer Science},
publisher = {Springer},
address = { Heidelberg, Germany},
volume = 9921,
year = 2016,
doi = {10.1007/978-3-319-45823-6},
isbn = {978-3-319-45822-9}
}
@article{LopVerDreDoe2025,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Diederick Vermetten and Johann Dreo and Carola Doerr },
title = {Using the Empirical Attainment Function for Analyzing
Single-objective Black-box Optimization Algorithms},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2025,
annote = {Pre-print: \url{https://doi.org/10.48550/arXiv.2404.02031}},
doi = {10.1109/TEVC.2024.3462758},
abstract = {A widely accepted way to assess the performance of iterative
black-box optimizers is to analyze their empirical cumulative
distribution function (ECDF) of pre-defined quality targets
achieved not later than a given runtime. In this work, we
consider an alternative approach, based on the empirical
attainment function (EAF) and we show that the target-based
ECDF is an approximation of the EAF. We argue that the EAF
has several advantages over the target-based ECDF. In
particular, it does not require defining a priori quality
targets per function, captures performance differences more
precisely, and enables the use of additional summary
statistics that enrich the analysis. We also show that the
average area over the convergence curves is a
simpler-to-calculate, but equivalent, measure of anytime
performance. To facilitate the accessibility of the EAF, we
integrate a module to compute it into the IOHanalyzer
platform. Finally, we illustrate the use of the EAF via
synthetic examples and via the data available for the BBOB
suite.}
}
@article{MarLopStuCol2024auto,
author = { Raul Mart{\'i}n-Santamar{\'i}a and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle and Colmenar, J. Manuel },
title = {On the automatic generation of metaheuristic algorithms for
combinatorial optimization problems},
journal = {European Journal of Operational Research},
year = 2024,
doi = {10.1016/j.ejor.2024.06.001},
abstract = {Metaheuristic algorithms have become one of the preferred
approaches for solving optimization problems. Finding the
best metaheuristic for a given problem is often difficult due
to the large number of available approaches and possible
algorithmic designs. Moreover, high-performing metaheuristics
often combine general-purpose and problem-specific
algorithmic components. We propose here an approach for
automatically designing metaheuristics using a flexible
framework of algorithmic components, from which algorithms
are instantiated and evaluated by an automatic configuration
method. The rules for composing algorithmic components are
defined implicitly by the properties of each algorithmic
component, in contrast to previous proposals, which require a
handwritten algorithmic template or grammar. As a result,
extending our framework with additional components, even
problem-specific or user-defined ones, automatically updates
the design space. Furthermore, since the generated algorithms
are made up of components, they can be easily interpreted. We
provide an implementation of our proposal and demonstrate its
benefits by outperforming previous research in three distinct
problems from completely different families: a facility
layout problem, a vehicle routing problem and a clustering
problem.},
keywords = {irace}
}
@article{BliCosRefZha2023aitsp,
title = {The First {AI4TSP} Competition: Learning to Solve Stochastic
Routing Problems},
journal = {Artificial Intelligence},
pages = 103918,
volume = 319,
year = 2023,
issn = {0004-3702},
doi = {10.1016/j.artint.2023.103918},
author = {Laurens Bliek and Paulo {da Costa} and Reza {Refaei Afshar}
and Robbert Reijnen and Yingqian Zhang and Tom Catshoek and
Dani{\"e}l Vos and Sicco Verwer and Fynn Schmitt-Ulms and
Andr{\'e} Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf
and Federico Bobbio and Justine Pepin and Warley Almeida
Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Irurozki, Ekhine },
keywords = {AI for TSP competition, Travelling salesman problem, Routing
problem, Stochastic combinatorial optimization,
Surrogate-based optimization, Deep reinforcement learning},
abstract = {This paper reports on the first international competition on
AI for the traveling salesman problem (TSP) at the
International Joint Conference on Artificial Intelligence
2021 (IJCAI-21). The TSP is one of the classical
combinatorial optimization problems, with many variants
inspired by real-world applications. This first competition
asked the participants to develop algorithms to solve an
orienteering problem with stochastic weights and time windows
(OPSWTW). It focused on two learning approaches:
surrogate-based optimization and deep reinforcement
learning. In this paper, we describe the problem, the
competition setup, and the winning methods, and give an
overview of the results. The winning methods described in
this work have advanced the state-of-the-art in using AI for
stochastic routing problems. Overall, by organizing this
competition we have introduced routing problems as an
interesting problem setting for AI researchers. The simulator
of the problem has been made open-source and can be used by
other researchers as a benchmark for new learning-based
methods. The instances and code for the competition are
available at
\url{https://github.com/paulorocosta/ai-for-tsp-competition}.}
}
@article{ShaLopKno2023bench,
author = { Shavarani, Seyed Mahdi and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Joshua D. Knowles },
title = {On Benchmarking Interactive Evolutionary Multi-Objective
Algorithms},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2023,
volume = 28,
number = 4,
pages = {1084--1098},
doi = {10.1109/TEVC.2023.3289872},
abstract = {We carry out a detailed performance assessment of two
interactive evolutionary multi-objective algorithms (EMOAs)
using a machine decision maker that enables us to repeat
experiments and study specific behaviours modeled after human
decision makers (DMs). Using the same set of benchmark test
problems as in the original papers on these interactive EMOAs
(in up to 10 objectives), we bring to light interesting
effects when we use a machine DM based on sigmoidal utility
functions that have support from the psychology literature
(replacing the simpler utility functions used in the original
papers). Our machine DM enables us to go further and simulate
human biases and inconsistencies as well. Our results from
this study, which is the most comprehensive assessment of
multiple interactive EMOAs so far conducted, suggest that
current well-known algorithms have shortcomings that need
addressing. These results further demonstrate the value of
improving the benchmarking of interactive EMOAs}
}
@article{ShaLopAlm2023hidden,
author = { Shavarani, Seyed Mahdi and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Allmendinger, Richard },
title = {Detecting Hidden and Irrelevant Objectives in Interactive
Multi-Objective Optimization},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2023,
volume = 28,
number = 2,
pages = {544--557},
doi = {10.1109/TEVC.2023.3269348},
abstract = {Evolutionary multi-objective optimization algorithms (EMOAs)
typically assume that all objectives that are relevant to the
decision-maker (DM) are optimized by the EMOA. In some
scenarios, however, there are irrelevant objectives that are
optimized by the EMOA but ignored by the DM, as well as,
hidden objectives that the DM considers when judging the
utility of solutions but are not optimized. This discrepancy
between the EMOA and the DM's preferences may impede the
search for the most-preferred solution and waste resources
evaluating irrelevant objectives. Research on objective
reduction has focused so far on the structure of the problem
and correlations between objectives and neglected the role of
the DM. We formally define here the concepts of irrelevant
and hidden objectives and propose methods for detecting them,
based on uni-variate feature selection and recursive feature
elimination, that use the preferences already elicited when a
DM interacts with a ranking-based interactive EMOA
(iEMOA). We incorporate the detection methods into an iEMOA
capable of dynamically switching the objectives being
optimized. Our experiments show that this approach can
efficiently identify which objectives are relevant to the DM
and reduce the number of objectives being optimized, while
keeping and often improving the utility, according to the DM,
of the best solution found.}
}
@article{NebLopGarCoe2023automopso,
author = { Nebro, Antonio J. and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Jos{\'e} Garc{\'i}a-Nieto and Carlos A. {Coello Coello} },
title = {On the automatic design of multi-objective particle swarm
optimizers: experimentation and analysis},
journal = {Swarm Intelligence},
year = 2023,
doi = {10.1007/s11721-023-00227-2},
abstract = {Research in multi-objective particle swarm optimizers
(MOPSOs) progresses by proposing one new MOPSO at a time. In
spite of the commonalities among different MOPSOs, it is
often unclear which algorithmic components are crucial for
explaining the performance of a particular MOPSO
design. Moreover, it is expected that different designs may
perform best on different problem families and identifying a
best overall MOPSO is a challenging task. We tackle this
challenge here by: (1) proposing AutoMOPSO, a flexible
algorithmic template for designing MOPSOs with a design space
that can instantiate thousands of potential MOPSOs; and (2)
searching for good-performing MOPSO designs given a family of
training problems by means of an automatic configuration tool
(irace). We apply this automatic design methodology to
generate a MOPSO that significantly outperforms two
state-of-the-art MOPSOs on four well-known bi-objective
problem families. We also identify the key design choices and
parameters of the winning MOPSO by means of
ablation. AutoMOPSO is publicly available as part of the
jMetal framework.}
}
@article{LiLopYao2023archiving,
author = { Li, Miqing and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Xin Yao },
title = {Multi-Objective Archiving},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2023,
volume = 28,
number = 3,
pages = {696--717},
doi = {10.1109/TEVC.2023.3314152},
abstract = {Most multi-objective optimisation algorithms maintain an
archive explicitly or implicitly during their search. Such an
archive can be solely used to store high-quality solutions
presented to the decision maker, but in many cases may
participate in the search process (e.g., as the population in
evolutionary computation). Over the last two decades,
archiving, the process of comparing new solutions with
previous ones and deciding how to update the
archive/population, stands as an important issue in
evolutionary multi-objective optimisation (EMO). This is
evidenced by constant efforts from the community on
developing various effective archiving methods, ranging from
conventional Pareto-based methods to more recent
indicator-based and decomposition-based ones. However, the
focus of these efforts is on empirical performance comparison
in terms of specific quality indicators; there is lack of
systematic study of archiving methods from a general
theoretical perspective. In this paper, we attempt to conduct
a systematic overview of multi-objective archiving, in the
hope of paving the way to understand archiving algorithms
from a holistic perspective of theory and practice, and more
importantly providing a guidance on how to design
theoretically desirable and practically useful archiving
algorithms. In doing so, we also present that archiving
algorithms based on weakly Pareto compliant indicators (e.g.,
$\epsilon$-indicator), as long as designed properly, can
achieve the same theoretical desirables as archivers based on
Pareto compliant indicators (e.g., hypervolume
indicator). Such desirables include the property
limit-optimal, the limit form of the possible optimal
property that a bounded archiving algorithm can have with
respect to the most general form of superiority between
solution sets.}
}
@article{CinFerLopAlb2022irace,
author = { Christian Cintrano and Javier Ferrer and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Alba, Enrique },
title = {Hybridization of Evolutionary Operators with Elitist Iterated
Racing for the Simulation Optimization of Traffic Lights
Programs},
journal = {Evolutionary Computation},
year = 2023,
volume = 31,
number = 1,
pages = {31--51},
doi = {10.1162/evco_a_00314},
abstract = {In the traffic light scheduling problem the evaluation of
candidate solutions requires the simulation of a process
under various (traffic) scenarios. Thus, good solutions
should not only achieve good objective function values, but
they must be robust (low variance) across all different
scenarios. Previous work has shown that combining IRACE with
evolutionary operators is effective for this task due to the
power of evolutionary operators in numerical optimization. In
this paper, we further explore the hybridization of
evolutionary operators and the elitist iterated racing of
IRACE for the simulation-optimization of traffic light
programs. We review previous works from the literature to
find the evolutionary operators performing the best when
facing this problem to propose new hybrid algorithms. We
evaluate our approach over a realistic case study derived
from the traffic network of Málaga (Spain) with 275 traffic
lights that should be scheduled optimally. The experimental
analysis reveals that the hybrid algorithm comprising IRACE
plus differential evolution offers statistically better
results than the other algorithms when the budget of
simulations is low. In contrast, IRACE performs better than
the hybrids for high simulations budget, although the
optimization time is much longer.},
keywords = {irace, Simulation optimization, Uncertainty, Traffic light
planning}
}
@article{MazLopChuMie2023tgp,
author = { Atanu Mazumdar and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Tinkle Chugh and Kaisa Miettinen },
title = {Treed {Gaussian} Process Regression for Solving Offline
Data-Driven Continuous Multiobjective Optimization Problems},
journal = {Evolutionary Computation},
year = 2023,
volume = 31,
number = 4,
pages = {375--399},
doi = {10.1162/evco_a_00329},
abstract = {For offline data-driven multiobjective optimization problems
(MOPs), no new data is available during the optimization
process. Approximation models (or surrogates) are first built
using the provided offline data and an optimizer, e.g. a
multiobjective evolutionary algorithm, can then be utilized
to find Pareto optimal solutions to the problem with
surrogates as objective functions. In contrast to online
data-driven MOPs, these surrogates cannot be updated with new
data and, hence, the approximation accuracy cannot be
improved by considering new data during the optimization
process. Gaussian process regression (GPR) models are widely
used as surrogates because of their ability to provide
uncertainty information. However, building GPRs becomes
computationally expensive when the size of the dataset is
large. Using sparse GPRs reduces the computational cost of
building the surrogates. However, sparse GPRs are not
tailored to solve offline data-driven MOPs, where good
accuracy of the surrogates is needed near Pareto optimal
solutions. Treed GPR (TGPR-MO) surrogates for offline
data-driven MOPs with continuous decision variables are
proposed in this paper. The proposed surrogates first split
the decision space into subregions using regression trees and
build GPRs sequentially in regions close to Pareto optimal
solutions in the decision space to accurately approximate
tradeoffs between the objective functions. TGPR-MO surrogates
are computationally inexpensive because GPRs are built only
in a smaller region of the decision space utilizing a subset
of the data. The TGPR-MO surrogates were tested on
distance-based visualizable problems with various data sizes,
sampling strategies, numbers of objective functions, and
decision variables. Experimental results showed that the
TGPR-MO surrogates are computationally cheaper and can handle
datasets of large size. Furthermore, TGPR-MO surrogates
produced solutions closer to Pareto optimal solutions
compared to full GPRs and sparse GPRs.},
keywords = {Gaussian processes, Kriging, Regression trees, Metamodelling,
Surrogate, Pareto optimality}
}
@article{SouRitLop2021cap,
author = { Marcelo {De Souza} and Marcus Ritt and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Capping Methods for the Automatic Configuration of
Optimization Algorithms},
journal = {Computers \& Operations Research},
doi = {10.1016/j.cor.2021.105615},
year = 2022,
volume = 139,
pages = 105615,
supplement = {https://github.com/souzamarcelo/supp-cor-capopt},
abstract = {Automatic configuration techniques are widely and
successfully used to find good parameter settings for
optimization algorithms. Configuration is costly, because it
is necessary to evaluate many configurations on different
instances. For decision problems, when the objective is to
minimize the running time of the algorithm, many
configurators implement capping methods to discard poor
configurations early. Such methods are not directly
applicable to optimization problems, when the objective is to
optimize the cost of the best solution found, given a
predefined running time limit. We propose new capping methods
for the automatic configuration of optimization
algorithms. They use the previous executions to determine a
performance envelope, which is used to evaluate new
executions and cap those that do not satisfy the envelope
conditions. We integrate the capping methods into the irace
configurator and evaluate them on different optimization
scenarios. Our results show that the proposed methods can
save from about 5\% to 78\% of the configuration effort,
while finding configurations of the same quality. Based on
the computational analysis, we identify two conservative and
two aggressive methods, that save an average of about 20\%
and 45\% of the configuration effort, respectively. We also
provide evidence that capping can help to better use the
available budget in scenarios with a configuration time
limit.}
}
@article{AyoAllLopPar2022scalarisation,
author = { Ayodele, Mayowa and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Parizy, Matthieu },
title = {A Study of Scalarisation Techniques for Multi-Objective
{QUBO} Solving},
journal = {Arxiv preprint arXiv:2210.11321},
year = 2022,
doi = {10.48550/arXiv.2210.11321}
}
@article{RivYanLop2021tweet,
author = { Rivadeneira, Luc{\'i}a and Yang, Jian-Bo and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Predicting tweet impact using a novel evidential reasoning
prediction method},
journal = {Expert Systems with Applications},
year = 2021,
volume = 169,
pages = 114400,
month = may,
doi = {10.1016/j.eswa.2020.114400},
abstract = {This study presents a novel evidential reasoning (ER)
prediction model called MAKER-RIMER to examine how different
features embedded in Twitter posts (tweets) can predict the
number of retweets achieved during an electoral campaign. The
tweets posted by the two most voted candidates during the
official campaign for the 2017 Ecuadorian Presidential
election were used for this research. For each tweet, five
features including type of tweet, emotion, URL, hashtag, and
date are identified and coded to predict if tweets are of
either high or low impact. The main contributions of the new
proposed model include its suitability to analyse tweet
datasets based on likelihood analysis of data. The model is
interpretable, and the prediction process relies only on the
use of available data. The experimental results show that
MAKER-RIMER performed better, in terms of misclassification
error, when compared against other predictive machine
learning approaches. In addition, the model allows observing
which features of the candidates' tweets are linked to high
and low impact. Tweets containing allusions to the contender
candidate, either with positive or negative connotations,
without hashtags, and written towards the end of the
campaign, were persistently those with the highest
impact. URLs, on the other hand, is the only variable that
performs differently for the two candidates in terms of
achieving high impact. MAKER-RIMER can provide campaigners of
political parties or candidates with a tool to measure how
features of tweets are predictors of their impact, which can
be useful to tailor Twitter content during electoral
campaigns.},
keywords = {Evidential reasoning rule,Belief rule-based inference,Maximum
likelihood data analysis,Twitter,Retweet,Prediction}
}
@article{ShaLopMie2021visual,
title = {Visualizations for Decision Support in Scenario-based
Multiobjective Optimization},
author = { Shavazipour, Babooshka and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Kaisa Miettinen },
journal = {Information Sciences},
volume = 578,
pages = {1--21},
year = 2021,
abstract = {We address challenges of decision problems when managers need
to optimize several conflicting objectives simultaneously
under uncertainty. We propose visualization tools to support
the solution of such scenario-based multiobjective
optimization problems. Suitable graphical visualizations are
necessary to support managers in understanding, evaluating,
and comparing the performances of management decisions
according to all objectives in all plausible scenarios. To
date, no appropriate visualization has been suggested. This
paper fills this gap by proposing two visualization methods:
a novel extension of empirical attainment functions for
scenarios and an adapted version of heatmaps. They help a
decision-maker in gaining insight into realizations of
trade-offs and comparisons between objective functions in
different scenarios. Some fundamental questions that a
decision-maker may wish to answer with the help of
visualizations are also identified. Several examples are
utilized to illustrate how the proposed visualizations
support a decision-maker in evaluating and comparing
solutions to be able to make a robust decision by answering
the questions. Finally, we validate the usefulness of the
proposed visualizations in a real-world problem with a real
decision-maker. We conclude with guidelines regarding which
of the proposed visualizations are best suited for different
problem classes.},
doi = {10.1016/j.ins.2021.07.025},
supplement = {https://doi.org/10.5281/zenodo.5040421}
}
@article{DesRitLopPer2021acviz,
author = { Marcelo {De Souza} and Marcus Ritt and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and P{\'e}rez C{\'a}ceres, Leslie },
title = {{\softwarepackage{ACVIZ}}: A Tool for the Visual Analysis of
the Configuration of Algorithms with {\rpackage{irace}}},
journal = {Operations Research Perspectives},
year = 2021,
doi = {10.1016/j.orp.2021.100186},
supplement = {https://zenodo.org/record/4714582},
abstract = {This paper introduces acviz, a tool that helps to analyze the
automatic configuration of algorithms with irace. It provides
a visual representation of the configuration process,
allowing users to extract useful information, e.g. how the
configurations evolve over time. When test data is available,
acviz also shows the performance of each configuration on the
test instances. Using this visualization, users can analyze
and compare the quality of the resulting configurations and
observe the performance differences on training and test
instances.},
volume = 8,
pages = 100186
}
@article{LopBraPaq2021arxiv,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\"u}rgen Branke and Lu{\'i}s Paquete },
title = {Reproducibility in Evolutionary Computation},
journal = {Arxiv preprint arXiv:20102.03380 [cs.AI]},
year = 2021,
url = {https://arxiv.org/abs/2102.03380},
abstract = {Experimental studies are prevalent in Evolutionary
Computation (EC), and concerns about the reproducibility and
replicability of such studies have increased in recent times,
reflecting similar concerns in other scientific fields. In
this article, we suggest a classification of different types
of reproducibility that refines the badge system of the
Association of Computing Machinery (ACM) adopted by TELO. We
discuss, within the context of EC, the different types of
reproducibility as well as the concepts of artifact and
measurement, which are crucial for claiming
reproducibility. We identify cultural and technical obstacles
to reproducibility in the EC field. Finally, we provide
guidelines and suggest tools that may help to overcome some
of these reproducibility obstacles.},
keywords = {Evolutionary Computation, Reproducibility, Empirical study,
Benchmarking}
}
@article{LopBraPaq2021telo,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\"u}rgen Branke and Lu{\'i}s Paquete },
title = {Reproducibility in Evolutionary Computation},
journal = {ACM Transactions on Evolutionary Learning and Optimization},
year = 2021,
volume = 1,
number = 4,
pages = {1--21},
doi = {10.1145/3466624},
epub = {https://arxiv.org/abs/2102.03380},
abstract = {Experimental studies are prevalent in Evolutionary
Computation (EC), and concerns about the reproducibility and
replicability of such studies have increased in recent times,
reflecting similar concerns in other scientific fields. In
this article, we suggest a classification of different types
of reproducibility that refines the badge system of the
Association of Computing Machinery (ACM) adopted by TELO. We
discuss, within the context of EC, the different types of
reproducibility as well as the concepts of artifact and
measurement, which are crucial for claiming
reproducibility. We identify cultural and technical obstacles
to reproducibility in the EC field. Finally, we provide
guidelines and suggest tools that may help to overcome some
of these reproducibility obstacles.},
keywords = {Evolutionary Computation, Reproducibility, Empirical study,
Benchmarking}
}
@article{DiaLop2020ejor,
author = { Juan Esteban Diaz and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Incorporating Decision-Maker's Preferences into the Automatic
Configuration of Bi-Objective Optimisation Algorithms},
journal = {European Journal of Operational Research},
year = 2021,
volume = 289,
number = 3,
pages = {1209--1222},
doi = {10.1016/j.ejor.2020.07.059},
abstract = {Automatic configuration (AC) methods are increasingly used to
tune and design optimisation algorithms for problems with
multiple objectives. Most AC methods use unary quality
indicators, which assign a single scalar value to an
approximation to the Pareto front, to compare the performance
of different optimisers. These quality indicators, however,
imply preferences beyond Pareto-optimality that may differ
from those of the decision maker (DM). Although it is
possible to incorporate DM's preferences into quality
indicators, e.g., by means of the weighted hypervolume
indicator (HV$^w$), expressing preferences in terms of weight
function is not always intuitive nor an easy task for a DM,
in particular, when comparing the stochastic outcomes of
several algorithm configurations. A more visual approach to
compare such outcomes is the visualisation of their empirical
attainment functions (EAFs) differences. This paper proposes
using such visualisations as a way of eliciting information
about regions of the objective space that are preferred by
the DM. We present a method to convert the information about
EAF differences into a HV$^w$ that will assign higher quality
values to approximation fronts that result in EAF differences
preferred by the DM. We show that the resulting HV$^w$ may be
used by an AC method to guide the configuration of
multi-objective optimisers according to the preferences of
the DM. We evaluate the proposed approach on a well-known
benchmark problem. Finally, we apply our approach to
re-configuring, according to different DM's preferences, a
multi-objective optimiser tackling a real-world production
planning problem arising in the manufacturing industry.},
supplement = {https://doi.org/10.5281/zenodo.3749288}
}
@article{AvrAllLop2021arxiv,
author = { Andreea Avramescu and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Managing Manufacturing and Delivery of Personalised Medicine:
Current and Future Models},
year = 2021,
journal = {Arxiv preprint arXiv:2105.12699 [econ.GN]},
url = {https://arxiv.org/abs/2105.12699}
}
@article{LopPerStu2020ifors,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and P{\'e}rez C{\'a}ceres, Leslie and Thomas St{\"u}tzle },
title = {{irace}: A Tool for the Automatic Configuration of
Algorithms},
journal = {International Federation of Operational Research Societies
(IFORS) News},
year = 2020,
volume = 14,
number = 2,
pages = {30--32},
month = jun,
url = {https://www.ifors.org/newsletter/ifors-news-june2020.pdf}
}
@article{BarDoeBer2020benchmarking,
title = {Benchmarking in Optimization: Best Practice and Open Issues},
author = { Thomas Bartz-Beielstein and Carola Doerr and Daan van den Berg and Jakob Bossek and Sowmya Chandrasekaran and Tome Eftimov and Andreas Fischbach and Pascal Kerschke and William {La
Cava} and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Katherine M. Malan and Jason H. Moore and Boris Naujoks and Patryk Orzechowski and Vanessa Volz and Markus Wagner and Thomas Weise},
year = 2020,
journal = {Arxiv preprint arXiv:2007.03488 [cs.NE]},
url = {https://arxiv.org/abs/2007.03488}
}
@article{StrLopBroLee2020,
title = {General Northern English: Exploring regional variation in the
North of England with machine learning},
author = { Strycharczuk, Patrycja and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Brown, Georgina and Adrian Leemann },
journal = { Frontiers in Artificial Intelligence },
year = 2020,
volume = 3,
number = 48,
keywords = {vowels, accent features, dialect leveling, Random forest
(bagging), Feature selecion},
doi = {10.3389/frai.2020.00048},
abstract = {In this paper, we present a novel computational approach to
the analysis of accent variation. The case study is dialect
leveling in the North of England, manifested as reduction of
accent variation across the North and emergence of General
Northern English (GNE), a pan-regional standard accent
associated with middle-class speakers. We investigated this
instance of dialect leveling using random forest
classification, with audio data from a crowd-sourced corpus
of 105 urban, mostly highly-educated speakers from five
northern UK cities: Leeds, Liverpool, Manchester, Newcastle
upon Tyne, and Sheffield. We trained random forest models to
identify individual northern cities from a sample of other
northern accents, based on first two formant measurements of
full vowel systems. We tested the models using unseen
data. We relied on undersampling, bagging (bootstrap
aggregation) and leave-one-out cross-validation to address
some challenges associated with the data set, such as
unbalanced data and relatively small sample size. The
accuracy of classification provides us with a measure of
relative similarity between different pairs of cities, while
calculating conditional feature importance allows us to
identify which input features (which vowels and which
formants) have the largest influence in the prediction. We do
find a considerable degree of leveling, especially between
Manchester, Leeds and Sheffield, although some differences
persist. The features that contribute to these differences
most systematically are typically not the ones discussed in
previous dialect descriptions. We propose that the most
systematic regional features are also not salient, and as
such, they serve as sociolinguistic regional indicators. We
supplement the random forest results with a more traditional
variationist description of by-city vowel systems, and we use
both sources of evidence to inform a description of the
vowels of General Northern English.}
}
@article{BezLopStu2019ec,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatically Designing State-of-the-Art Multi- and
Many-Objective Evolutionary Algorithms},
journal = {Evolutionary Computation},
year = 2020,
volume = 28,
number = 2,
pages = {195--226},
doi = {10.1162/evco_a_00263},
supplement = {https://github.com/iridia-ulb/automoea-ecj-2020},
pdf = {BezLopStu2019ec.pdf},
abstract = {A recent comparison of well-established multiobjective
evolutionary algorithms (MOEAs) has helped better identify
the current state-of-the-art by considering (i) parameter
tuning through automatic configuration, (ii) a wide range of
different setups, and (iii) various performance
metrics. Here, we automatically devise MOEAs with verified
state-of-the-art performance for multi- and many-objective
continuous optimization. Our work is based on two main
considerations. The first is that high-performing algorithms
can be obtained from a configurable algorithmic framework in
an automated way. The second is that multiple performance
metrics may be required to guide this automatic design
process. In the first part of this work, we extend our
previously proposed algorithmic framework, increasing the
number of MOEAs, underlying evolutionary algorithms, and
search paradigms that it comprises. These components can be
combined following a general MOEA template, and an automatic
configuration method is used to instantiate high-performing
MOEA designs that optimize a given performance metric and
present state-of-the-art performance. In the second part, we
propose a multiobjective formulation for the automatic MOEA
design, which proves critical for the context of
many-objective optimization due to the disagreement of
established performance metrics. Our proposed formulation
leads to an automatically designed MOEA that presents
state-of-the-art performance according to a set of metrics,
rather than a single one.}
}
@article{KimAllLop2020arxiv,
author = { Kim, Youngmin and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Safe Learning and Optimization Techniques: Towards a Survey
of the State of the Art},
journal = {Arxiv preprint arXiv:2101.09505 [cs.LG]},
year = 2020,
url = {https://arxiv.org/abs/2101.09505},
abstract = {Safe learning and optimization deals with learning and
optimization problems that avoid, as much as possible, the
evaluation of non-safe input points, which are solutions,
policies, or strategies that cause an irrecoverable loss
(e.g., breakage of a machine or equipment, or life
threat). Although a comprehensive survey of safe
reinforcement learning algorithms was published in 2015, a
number of new algorithms have been proposed thereafter, and
related works in active learning and in optimization were not
considered. This paper reviews those algorithms from a number
of domains including reinforcement learning, Gaussian process
regression and classification, evolutionary algorithms, and
active learning. We provide the fundamental concepts on which
the reviewed algorithms are based and a characterization of
the individual algorithms. We conclude by explaining how the
algorithms are connected and suggestions for future
research. }
}
@article{BeaShaSmiLop2018review,
author = {Bealt, Jennifer and Shaw, Duncan and Smith, Chris M. and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
year = 2019,
title = {Peer Reviews for Making Cities Resilient: A Systematic
Literature Review},
journal = {International Journal of Emergency Management},
volume = 15,
number = 4,
pages = {334--359},
doi = {10.1504/IJEM.2019.104201},
abstract = {Peer reviews are a unique governance tool that use expertise
from one city or country to assess and strengthen the
capabilities of another. Peer review tools are gaining
momentum in disaster management and remain an important but
understudied topic in risk governance. Methodologies to
conduct a peer review are still in their infancy. To enhance
these, a systematic literature review (SLR) of academic and
non-academic literature was conducted on city resilience peer
reviews. Thirty-three attributes of resilience are
identified, which provides useful insights into how research
and practice can inform risk governance, and utilise peer
reviews, to drive meaningful change. Moreover, it situates
the challenges associated with resilience building tools
within risk governance to support the development of
interdisciplinary perspectives for integrated city resilience
frameworks. Results of this research have been used to
develop a peer review methodology and an international
standard on conducting peer reviews for disaster risk
reduction.},
keywords = {city resilience, city peer review, disaster risk governance}
}
@article{FerLopAlb2019asoc,
author = { Javier Ferrer and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Alba, Enrique },
title = {Reliable Simulation-Optimization of Traffic Lights in a
Real-World City},
journal = {Applied Soft Computing},
year = 2019,
volume = 78,
pages = {697--711},
doi = {10.1016/j.asoc.2019.03.016},
pdf = {FerLopAlb2019asoc.pdf},
supplement = {https://github.com/MLopez-Ibanez/irace-sumo}
}
@article{WesLop2018ecj,
title = {Latin Hypercube Designs with Branching and Nested Factors for
Initialization of Automatic Algorithm Configuration},
author = { Simon Wessing and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
doi = {10.1162/evco_a_00241},
journal = {Evolutionary Computation},
year = 2018,
pdf = {WesLop2018ecj.pdf},
volume = 27,
number = 1,
pages = {129--145}
}
@article{BezLopStu2017assessment,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {A Large-Scale Experimental Evaluation of High-Performing
Multi- and Many-Objective Evolutionary Algorithms},
year = 2018,
journal = {Evolutionary Computation},
doi = {10.1162/evco_a_00217},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2015-007/},
pdf = {BezLopStu2017assessment.pdf},
volume = 26,
number = 4,
pages = {621--656},
alias = {BezLopStu2016assessment},
abstract = {Research on multi-objective evolutionary algorithms (MOEAs)
has produced over the past decades a large number of
algorithms and a rich literature on performance assessment
tools to evaluate and compare them. Yet, newly proposed MOEAs
are typically compared against very few, often a decade older
MOEAs. One reason for this apparent contradiction is the lack
of a common baseline for comparison, with each subsequent
study often devising its own experimental scenario, slightly
different from other studies. As a result, the state of the
art in MOEAs is a disputed topic. This article reports a
systematic, comprehensive evaluation of a large number of
MOEAs that covers a wide range of experimental scenarios. A
novelty of this study is the separation between the
higher-level algorithmic components related to
multi-objective optimization (MO), which characterize each
particular MOEA, and the underlying parameters-such as
evolutionary operators, population size, etc.-whose
configuration may be tuned for each scenario. Instead of
relying on a common or "default" parameter configuration that
may be low-performing for particular MOEAs or scenarios and
unintentionally biased, we tune the parameters of each MOEA
for each scenario using automatic algorithm configuration
methods. Our results confirm some of the assumed knowledge in
the field, while at the same time they provide new insights
on the relative performance of MOEAs for many-objective
problems. For example, under certain conditions,
indicator-based MOEAs are more competitive for such problems
than previously assumed. We also analyze problem-specific
features affecting performance, the agreement between
performance metrics, and the improvement of tuned
configurations over the default configurations used in the
literature. Finally, the data produced is made publicly
available to motivate further analysis and a baseline for
future comparisons.}
}
@article{KabColKorLop2017jacryst,
author = { Kabova, Elena A. and Cole, Jason C. and Oliver Korb and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Williams, Adrian C. and Shankland, Kenneth },
title = {Improved performance of crystal structure solution from
powder diffraction data through parameter tuning of a
simulated annealing algorithm},
journal = {Journal of Applied Crystallography},
year = 2017,
volume = 50,
number = 5,
pages = {1411--1420},
month = oct,
doi = {10.1107/S1600576717012602},
abstract = {Significant gains in the performance of the simulated
annealing algorithm in the {\it DASH} software package have
been realized by using the {\it irace} automatic
configuration tool to optimize the values of three key
simulated annealing parameters. Specifically, the success
rate in finding the global minimum in intensity $\chi^2$
space is improved by up to an order of magnitude. The general
applicability of these revised simulated annealing parameters
is demonstrated using the crystal structure determinations of
over 100 powder diffraction datasets.},
keywords = {crystal structure determination, powder diffraction,
simulated annealing, parameter tuning, irace}
}
@article{DorBirLiLop2017si,
author = { Marco Dorigo and Mauro Birattari and Li, Xiaodong and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Kazuhiro Ohkura and Carlo Pinciroli and Thomas St{\"u}tzle },
title = {{ANTS} 2016 Special Issue: Editorial},
journal = {Swarm Intelligence},
year = 2017,
month = nov,
doi = {10.1007/s11721-017-0146-5}
}
@article{LopKesStu2017:cim,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Marie-El{\'e}onore Kessaci and Thomas St{\"u}tzle },
title = {Automatic Design of Hybrid Metaheuristics from Algorithmic Components},
journal = {Submitted},
year = 2017,
optvolume = {},
optnumber = {},
optpages = {}
}
@article{LopDubPerStuBir2016irace,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and P{\'e}rez C{\'a}ceres, Leslie and Thomas St{\"u}tzle and Mauro Birattari },
title = {The {\rpackage{irace}} Package: Iterated Racing for Automatic
Algorithm Configuration},
journal = {Operations Research Perspectives},
year = 2016,
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2016-003/},
doi = {10.1016/j.orp.2016.09.002},
volume = 3,
pages = {43--58}
}
@article{BezLopStu2015tec,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Component-Wise Design of Multi-Objective
Evolutionary Algorithms},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2016,
volume = 20,
number = 3,
pages = {403--417},
doi = {10.1109/TEVC.2015.2474158},
supplement = {https://github.com/iridia-ulb/automoea-tevc-2016},
pdf = {BezLopStu2015tec.pdf}
}
@article{BluPinLopLoz2015cor,
author = { Christian Blum and Pedro Pinacho and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Jos{\'e} A. Lozano },
title = {Construct, Merge, Solve \& Adapt: A New General Algorithm for
Combinatorial Optimization},
journal = {Computers \& Operations Research},
year = 2016,
volume = 68,
pages = {75--88},
doi = {10.1016/j.cor.2015.10.014},
keywords = {irace, CMSA}
}
@article{TriLop2015plos,
author = { Vito Trianni and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Advantages of Task-Specific Multi-Objective Optimisation in
Evolutionary Robotics},
journal = {PLoS One},
year = 2015,
volume = 10,
number = 8,
pages = {e0136406},
doi = {10.1371/journal.pone.0136406},
abstract = {The application of multi-objective optimisation to
evolutionary robotics is receiving increasing attention. A
survey of the literature reveals the different possibilities
it offers to improve the automatic design of efficient and
adaptive robotic systems, and points to the successful
demonstrations available for both task-specific and
task-agnostic approaches (i.e., with or without reference to
the specific design problem to be tackled). However, the
advantages of multi-objective approaches over
single-objective ones have not been clearly spelled out and
experimentally demonstrated. This paper fills this gap for
task-specific approaches: starting from well-known results in
multi-objective optimisation, we discuss how to tackle
commonly recognised problems in evolutionary robotics. In
particular, we show that multi-objective optimisation (i)
allows evolving a more varied set of behaviours by exploring
multiple trade-offs of the objectives to optimise, (ii)
supports the evolution of the desired behaviour through the
introduction of objectives as proxies, (iii) avoids the
premature convergence to local optima possibly introduced by
multi-component fitness functions, and (iv) solves the
bootstrap problem exploiting ancillary objectives to guide
evolution in the early phases. We present an experimental
demonstration of these benefits in three different case
studies: maze navigation in a single robot domain, flocking
in a swarm robotics context, and a strictly collaborative
task in collective robotics.}
}
@article{DubLopStu2015ejor,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Anytime {Pareto} Local Search},
journal = {European Journal of Operational Research},
year = 2015,
volume = 243,
number = 2,
pages = {369--385},
doi = {10.1016/j.ejor.2014.10.062},
pdf = {DubLopStu2015ejor.pdf},
alias = {DubLopStu2013cor},
keywords = {Pareto local search}
}
@article{PerLopStu2015si,
author = { P{\'e}rez C{\'a}ceres, Leslie and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Ant colony optimization on a limited budget of evaluations},
journal = {Swarm Intelligence},
year = 2015,
doi = {10.1007/s11721-015-0106-x},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2015-004},
pdf = {PerLopStu2015si.pdf},
volume = 9,
number = {2-3},
pages = {103--124}
}
@article{LopStu2013ejor,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatically Improving the Anytime Behaviour of Optimisation
Algorithms},
journal = {European Journal of Operational Research},
year = 2014,
volume = 235,
number = 3,
pages = {569--582},
doi = {10.1016/j.ejor.2013.10.043},
pdf = {LopStu2014ejor.pdf},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2012-011/},
abstract = {Optimisation algorithms with good anytime behaviour try to
return as high-quality solutions as possible independently of
the computation time allowed. Designing algorithms with good
anytime behaviour is a difficult task, because performance is
often evaluated subjectively, by plotting the trade-off curve
between computation time and solution quality. Yet, the
trade-off curve may be modelled also as a set of mutually
nondominated, bi-objective points. Using this model, we
propose to combine an automatic configuration tool and the
hypervolume measure, which assigns a single quality measure
to a nondominated set. This allows us to improve the anytime
behaviour of optimisation algorithms by means of
automatically finding algorithmic configurations that produce
the best nondominated sets. Moreover, the recently proposed
weighted hypervolume measure is used here to incorporate the
decision-maker's preferences into the automatic tuning
procedure. We report on the improvements reached when
applying the proposed method to two relevant scenarios: (i)
the design of parameter variation strategies for MAX-MIN Ant
System and (ii) the tuning of the anytime behaviour of SCIP,
an open-source mixed integer programming solver with more
than 200 parameters.}
}
@article{MasLopDubStu2014cor,
author = { Franco Mascia and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and Thomas St{\"u}tzle },
title = {Grammar-Based Generation of Stochastic Local Search
Heuristics through Automatic Algorithm Configuration Tools},
journal = {Computers \& Operations Research},
year = 2014,
doi = {10.1016/j.cor.2014.05.020},
pdf = {MasLopDubStu2014cor.pdf},
volume = 51,
pages = {190--199}
}
@article{LopBlu2013asoc,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum and Jeffrey W. Ohlmann and Barrett W. Thomas },
title = {The Travelling Salesman Problem with Time Windows:
Adapting Algorithms from Travel-time to Makespan
Optimization},
journal = {Applied Soft Computing},
year = 2013,
volume = 13,
number = 9,
pages = {3806--3815},
doi = {10.1016/j.asoc.2013.05.009},
epub = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2013-011.pdf}
}
@article{LopStu2012swarm,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An experimental analysis of design choices of multi-objective ant colony optimization algorithms},
journal = {Swarm Intelligence},
year = 2012,
number = 3,
volume = 6,
pages = {207--232},
doi = {10.1007/s11721-012-0070-7},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2012-006/}
}
@article{LopStu2012tec,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {The Automatic Design of Multi-Objective Ant Colony
Optimization Algorithms},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2012,
volume = 16,
number = 6,
pages = {861--875},
doi = {10.1109/TEVC.2011.2182651},
abstract = {
Multi-objective optimization problems are problems with several,
typically conflicting criteria for evaluating solutions. Without
any a priori preference information, the Pareto optimality
principle establishes a partial order among solutions, and the
output of the algorithm becomes a set of nondominated solutions
rather than a single one. Various ant colony optimization (ACO)
algorithms have been proposed in recent years for solving such
problems. These multi-objective ACO (MOACO) algorithms exhibit
different design choices for dealing with the particularities of
the multi-objective context. This paper proposes a formulation of
algorithmic components that suffices to describe most MOACO
algorithms proposed so far. This formulation also shows that
existing MOACO algorithms often share equivalent design choices
but they are described in different terms. Moreover, this
formulation is synthesized into a flexible algorithmic framework,
from which not only existing MOACO algorithms may be
instantiated, but also combinations of components that were never
studied in the literature. In this sense, this paper goes beyond
proposing a new MOACO algorithm, but it rather introduces a
family of MOACO algorithms. The flexibility of the proposed MOACO
framework facilitates the application of automatic algorithm
configuration techniques. The experimental results presented in
this paper show that the automatically configured MOACO framework
outperforms the MOACO algorithms that inspired the framework
itself. This paper is also among the first to apply automatic
algorithm configuration techniques to multi-objective algorithms.}
}
@article{LopPraPae2011ec,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Representations and Evolutionary Operators for the
Scheduling of Pump Operations in Water Distribution
Networks},
journal = {Evolutionary Computation},
year = 2011,
doi = {10.1162/EVCO_a_00035},
volume = 19,
number = 3,
pages = {429--467},
abstract = {Reducing the energy consumption of water
distribution networks has never had more
significance. The greatest energy savings can be
obtained by carefully scheduling the operations of
pumps. Schedules can be defined either implicitly,
in terms of other elements of the network such as
tank levels, or explicitly by specifying the time
during which each pump is on/off. The traditional
representation of explicit schedules is a string of
binary values with each bit representing pump on/off
status during a particular time interval. In this
paper, we formally define and analyze two new
explicit representations based on time-controlled
triggers, where the maximum number of pump switches
is established beforehand and the schedule may
contain less switches than the maximum. In these
representations, a pump schedule is divided into a
series of integers with each integer representing
the number of hours for which a pump is
active/inactive. This reduces the number of
potential schedules compared to the binary
representation, and allows the algorithm to operate
on the feasible region of the search space. We
propose evolutionary operators for these two new
representations. The new representations and their
corresponding operations are compared with the two
most-used representations in pump scheduling,
namely, binary representation and level-controlled
triggers. A detailed statistical analysis of the
results indicates which parameters have the greatest
effect on the performance of evolutionary
algorithms. The empirical results show that an
evolutionary algorithm using the proposed
representations improves over the results obtained
by a recent state-of-the-art Hybrid Genetic
Algorithm for pump scheduling using level-controlled
triggers.}
}
@article{DubLopStu2011amai,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Improving the Anytime Behavior of Two-Phase Local
Search},
journal = {Annals of Mathematics and Artificial Intelligence},
year = 2011,
volume = 61,
number = 2,
pages = {125--154},
doi = {10.1007/s10472-011-9235-0},
alias = {DubLopStu2010amai},
pdf = {DubLopStu2011amai.pdf}
}
@article{DubLopStu2011cor,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {A Hybrid {TP$+$PLS} Algorithm for Bi-objective
Flow-Shop Scheduling Problems},
journal = {Computers \& Operations Research},
year = 2011,
volume = 38,
number = 8,
pages = {1219--1236},
doi = {10.1016/j.cor.2010.10.008},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2010-001/},
pdf = {DubLopStu2011cor.pdf}
}
@article{LopBlu2010cor,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum },
title = {Beam-{ACO} for the travelling salesman problem with
time windows},
journal = {Computers \& Operations Research},
year = 2010,
doi = {10.1016/j.cor.2009.11.015},
volume = 37,
number = 9,
pages = {1570--1583},
keywords = {Ant colony optimization, Travelling salesman problem with
time windows, Hybridization},
alias = {LopBlu09tsptw},
abstract = {The travelling salesman problem with time windows is
a difficult optimization problem that arises, for
example, in logistics. This paper deals with the
minimization of the travel-cost. For solving this
problem, this paper proposes a Beam-ACO algorithm,
which is a hybrid method combining ant colony
optimization with beam search. In general, Beam-ACO
algorithms heavily rely on accurate and
computationally inexpensive bounding information for
differentiating between partial solutions. This work
uses stochastic sampling as a useful alternative. An
extensive experimental evaluation on seven benchmark
sets from the literature shows that the proposed
Beam-ACO algorithm is currently a state-of-the-art
technique for the travelling salesman problem with
time windows when travel-cost optimization is
concerned.}
}
@article{BeuFonLopPaqVah09:tec,
author = { Nicola Beume and Carlos M. Fonseca and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Jan Vahrenhold },
title = {On the complexity of computing the hypervolume
indicator},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2009,
volume = 13,
number = 5,
pages = {1075--1082},
doi = {10.1109/TEVC.2009.2015575},
abstract = {The goal of multi-objective optimization is to find
a set of best compromise solutions for typically
conflicting objectives. Due to the complex nature of
most real-life problems, only an approximation to
such an optimal set can be obtained within
reasonable (computing) time. To compare such
approximations, and thereby the performance of
multi-objective optimizers providing them, unary
quality measures are usually applied. Among these,
the \emph{hypervolume indicator} (or
\emph{S-metric}) is of particular relevance due to
its favorable properties. Moreover, this indicator
has been successfully integrated into stochastic
optimizers, such as evolutionary algorithms, where
it serves as a guidance criterion for finding good
approximations to the Pareto front. Recent results
show that computing the hypervolume indicator can be
seen as solving a specialized version of Klee's
Measure Problem. In general, Klee's Measure Problem
can be solved with $\mathcal{O}(n \log n +
n^{d/2}\log n)$ comparisons for an input instance of
size $n$ in $d$ dimensions; as of this writing, it
is unknown whether a lower bound higher than
$\Omega(n \log n)$ can be proven. In this article,
we derive a lower bound of $\Omega(n\log n)$ for the
complexity of computing the hypervolume indicator in
any number of dimensions $d>1$ by reducing the
so-called \textsc{UniformGap} problem to it. For
the three dimensional case, we also present a
matching upper bound of $\mathcal{O}(n\log n)$
comparisons that is obtained by extending an
algorithm for finding the maxima of a point set.}
}
@article{BluBleLop09-BeamSearch-LCS,
author = { Christian Blum and Mar{\'i}a J. Blesa and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Beam search for the longest common subsequence
problem},
number = 12,
journal = {Computers \& Operations Research},
year = 2009,
pages = {3178--3186},
volume = 36,
doi = {10.1016/j.cor.2009.02.005},
pdf = {BluBleLop09-BeamSearch-LCS.pdf},
abstract = {The longest common subsequence problem is a classical string
problem that concerns finding the common part of a set of
strings. It has several important applications, for example,
pattern recognition or computational biology. Most research
efforts up to now have focused on solving this problem
optimally. In comparison, only few works exist dealing with
heuristic approaches. In this work we present a deterministic
beam search algorithm. The results show that our algorithm
outperforms the current state-of-the-art approaches not only
in solution quality but often also in computation time.}
}
@article{LopPraPae08aco,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Ant Colony Optimisation for the Optimal Control of
Pumps in Water Distribution Networks},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
year = 2008,
volume = 134,
number = 4,
pages = {337--346},
publisher = {{ASCE}},
pdf = {LopezPrasadPaechter08-jwrpm.pdf},
epub = {http://link.aip.org/link/?QWR/134/337/1},
doi = {10.1061/(ASCE)0733-9496(2008)134:4(337)},
abstract = {Reducing energy consumption of water distribution
networks has never had more significance than today. The greatest
energy savings can be obtained by careful scheduling of operation of
pumps. Schedules can be defined either implicitly, in terms of other
elements of the network such as tank levels, or explicitly by
specifying the time during which each pump is on/off. The
traditional representation of explicit schedules is a string of
binary values with each bit representing pump on/off status during a
particular time interval. In this paper a new explicit
representation is presented. It is based on time controlled
triggers, where the maximum number of pump switches is specified
beforehand. In this representation a pump schedule is divided into a
series of integers with each integer representing the number of
hours for which a pump is active/inactive. This reduces the number
of potential schedules (search space) compared to the binary
representation. Ant colony optimization (ACO) is a stochastic
meta-heuristic for combinatorial optimization problems that is
inspired by the foraging behavior of some species of ants. In this
paper, an application of the ACO framework was developed for the
optimal scheduling of pumps. The proposed representation was adapted
to an ant colony Optimization framework and solved for the optimal
pump schedules. Minimization of electrical cost was considered as
the objective, while satisfying system constraints. Instead of using
a penalty function approach for constraint violations, constraint
violations were ordered according to their importance and solutions
were ranked based on this order. The proposed approach was tested on
a small test network and on a large real-world network. Results are
compared with those obtained using a simple genetic algorithm based
on binary representation and a hybrid genetic algorithm that uses
level-based triggers.}
}
@article{LopPaqStu05:jmma,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {Hybrid Population-based Algorithms for the
Bi-objective Quadratic Assignment Problem},
journal = {Journal of Mathematical Modelling and Algorithms},
year = 2006,
volume = 5,
number = 1,
pages = {111--137},
pdf = {LopPaqStu04-techrepAIDA-04-11.pdf},
doi = {10.1007/s10852-005-9034-x},
alias = {LopPaqStu06:jmma},
abstract = {We present variants of an ant colony optimization
(MO-ACO) algorithm and of an evolutionary algorithm
(SPEA2) for tackling multi-objective combinatorial
optimization problems, hybridized with an iterative
improvement algorithm and the robust tabu search
algorithm. The performance of the resulting hybrid
stochastic local search (SLS) algorithms is
experimentally investigated for the bi-objective
quadratic assignment problem (bQAP) and compared
against repeated applications of the underlying
local search algorithms for several
scalarizations. The experiments consider structured
and unstructured bQAP instances with various degrees
of correlation between the flow matrices. We do a
systematic experimental analysis of the algorithms
using outperformance relations and the attainment
functions methodology to asses differences in the
performance of the algorithms. The experimental
results show the usefulness of the hybrid algorithms
if the available computation time is not too limited
and identify SPEA2 hybridized with very short tabu
search runs as the most promising variant.}
}