LopezIbanez.bib
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@unpublished{CamTriLop2017pseudo,
author = {Felipe Campelo and \'Athila R. Trindade and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Pseudoreplication in Racing Methods for Tuning Metaheuristics},
note = {In preparation},
year = 2017
}
@techreport{IRIDIA-2018-001,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatically Designing State-of-the-Art Multi- and
Many-Objective Evolutionary Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2018,
number = {TR/IRIDIA/2018-001},
month = jan,
url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2018-001.pdf},
note = {Published as \cite{BezLopStu2019ec}}
}
@techreport{IRIDIA-2017-005,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {A Large-Scale Experimental Evaluation of High-Performing
Multi- and Many-Objective Evolutionary Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2017,
number = {TR/IRIDIA/2017-005},
month = nov
}
@techreport{IRIDIA-2017-011,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Configuration of Multi-objective Optimizers and
Multi-objective Configuration},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2017,
number = {TR/IRIDIA/2017-011},
month = nov,
alias = {BezLopStu2017:techreport-011},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2017-011.pdf},
note = {Published as \cite{BezLopStu2020chapter}}
}
@techreport{BezLopStu2017:techreport-005,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {A Large-Scale Experimental Evaluation of High-Performing
Multi- and Many-Objective Evolutionary Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2017,
number = {TR/IRIDIA/2017-005},
month = feb
}
@techreport{IRIDIA-2017-012,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Marie-El{\'e}onore Kessaci and Thomas St{\"u}tzle },
title = {Automatic Design of Hybrid Metaheuristics from Algorithmic
Components},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2017,
number = {TR/IRIDIA/2017-012},
month = dec,
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2017-012.pdf}
}
@techreport{LopPerDubStuBir2016iraceguide,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and P{\'e}rez C{\'a}ceres, Leslie and J{\'e}r{\'e}mie Dubois-Lacoste and Thomas St{\"u}tzle and Mauro Birattari },
title = {The {\Rpackage{irace}} package: User Guide},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2016,
number = {TR/IRIDIA/2016-004},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2016-004.pdf}
}
@techreport{BezLopStu2014:automoeaTR,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Com\-ponent-Wise Design of Multi-Objective
Evolutionary Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2014,
number = {TR/IRIDIA/2014-012},
month = aug
}
@techreport{IRIDIA-2014-014,
author = { Vito Trianni and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Advantages of Multi-Objective Optimisation in Evolutionary
Robotics: Survey and Case Studies},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2014,
number = {TR/IRIDIA/2014-014},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2014-014.pdf}
}
@techreport{IRIDIA-2014-009,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Arnaud Liefooghe and Verel, S{\'e}bastien },
title = {Local Optimal Sets and Bounded Archiving on
Multi-objective {NK}-Landscapes with Correlated
Objectives},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2014,
number = {TR/IRIDIA/2014-009}
}
@techreport{IRIDIA-2013-015,
author = { Franco Mascia and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and Thomas St{\"u}tzle },
year = 2013,
title = {Grammar-based generation of stochastic local search
heuristics through automatic algorithm configuration
tools},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2013-015}
}
@techreport{IRIDIA-2012-012,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatically Improving the Anytime Behaviour of
Optimisation Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2012,
number = {TR/IRIDIA/2012-012},
month = may,
note = {Published in European Journal of Operations Research~\cite{LopStu2013ejor}}
}
@techreport{IRIDIA-2012-019,
author = { Andreea Radulescu and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatically Improving the Anytime Behaviour of
Multiobjective Evolutionary Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2012,
number = {TR/IRIDIA/2012-019},
note = {Published in the proceedings of EMO 2013~\cite{RadLopStu2013emo}}
}
@techreport{IRIDIA-2011-003,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {The Automatic Design of Multi-Objective Ant Colony
Optimization Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2011,
number = {TR/IRIDIA/2011-003},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2011-003.pdf},
note = {Published in IEEE Transactions on Evolutionary
Computation~\cite{LopStu2012tec}}
}
@techreport{LopDubStu2011irace,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and Thomas St{\"u}tzle and Mauro Birattari },
title = {The {\Rpackage{irace}} package, Iterated Race for Automatic
Algorithm Configuration},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2011,
number = {TR/IRIDIA/2011-004},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2011-004.pdf},
note = {Published in Operations Research Perspectives~\cite{LopDubPerStuBir2016irace}}
}
@techreport{IRIDIA-2011-001,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Joshua D. Knowles and Marco Laumanns },
title = {On Sequential Online Archiving of Objective Vectors},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2011,
number = {TR/IRIDIA/2011-001},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2011-001.pdf},
note = {This is a revised version of the paper published in EMO 2011~\cite{LopKnoLau2011emo}}
}
@techreport{IRIDIA-2010-002,
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Paola Pellegrini and Michael Maur and Marco A. {Montes de Oca} and Mauro Birattari and Marco Dorigo },
title = {Parameter Adaptation in Ant Colony Optimization},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2010-002},
year = 2010,
month = jan,
note = {Published as a book chapter~\cite{StuLopPel2011autsea}}
}
@techreport{IRIDIA-2009-026,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Adaptive ``Anytime'' Two-Phase Local Search},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2010,
number = {TR/IRIDIA/2009-026},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2009-026r001.pdf},
note = {Published in the proceedings of LION 4~\cite{DubLopStu10:lion-bfsp}}
}
@techreport{IRIDIA-2010-019,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {A Hybrid {TP+PLS} Algorithm for Bi-objective
Flow-Shop Scheduling Problems},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2010,
number = {TR/IRIDIA/2010-019},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2010-019r001.pdf},
note = {Published in Computers \& Operations Research~\cite{DubLopStu2011cor}}
}
@techreport{IRIDIA-2010-022,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Improving the Anytime Behavior of Two-Phase Local
Search},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2010,
number = {TR/IRIDIA/2010-022},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2010-022r001.pdf},
note = {Published in Annals of Mathematics and Artificial Intelligence~\cite{DubLopStu2011amai}}
}
@techreport{IRIDIA-2009-015,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'\i}s Paquete and Thomas St{\"u}tzle },
title = {Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2009,
number = {TR/IRIDIA/2009-015},
month = may,
note = {Published as a book chapter~\cite{LopPaqStu09emaa}}
}
@techreport{IRIDIA-2009-019,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An Analysis of Algorithmic Components for
Multiobjective Ant Colony Optimization: A Case Study
on the Biobjective {TSP}},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2009-019},
year = 2009,
month = jun,
note = {Published in the proceedings of Evolution Artificielle, 2009~\cite{LopStu09ea}}
}
@techreport{IRIDIA-2009-020,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Effective Hybrid Stochastic Local Search Algorithms
for Biobjective Permutation Flowshop Scheduling},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2009-020},
year = 2009,
month = jun,
url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2009-020r001.pdf},
note = {Published in the proceedings of Hybrid Metaheuristics 2009~\cite{DubLopStu09:hm-bfsp}}
}
@techreport{LopBlu08:tsptw,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum },
title = {Beam-{ACO} Based on Stochastic Sampling: {A} Case
Study on the {TSP} with Time Windows},
institution = {Department LSI, Universitat Polit{\`e}cnica de Catalunya},
year = 2008,
number = {LSI-08-28},
note = {Extended version published in Computers \& Operations Research~\cite{LopBlu2010cor}}
}
@techreport{BluBleLop08:lcs,
author = { Christian Blum and Mar{\'\i}a J. Blesa and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Beam Search for the Longest Common Subsequence
Problem},
institution = {Department LSI, Universitat Polit{\`e}cnica de Catalunya},
year = 2008,
number = {LSI-08-29},
note = {Published in Computers \& Operations Research~\cite{BluBleLop09-BeamSearch-LCS}}
}
@techreport{CI-235-07,
author = { Nicola Beume and Carlos M. Fonseca and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'\i}s Paquete and Jan Vahrenhold },
title = {On the Complexity of Computing the Hypervolume
Indicator},
institution = {University of Dortmund},
year = 2007,
number = {CI-235/07},
month = dec,
note = {Published in IEEE Transactions on Evolutionary Computation~\cite{BeuFonLopPaqVah09:tec}}
}
@techreport{PaqFonLop06-CSI-klee,
author = { Lu{\'\i}s Paquete and Carlos M. Fonseca and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {An optimal algorithm for a special case of {K}lee's
measure problem in three dimensions},
institution = {CSI, Universidade do Algarve},
year = 2006,
number = {CSI-RT-I-01/2006},
abstract = {The measure of the region dominated by (the maxima
of) a set of $n$ points in the positive $d$-orthant
has been proposed as an indicator of performance in
multiobjective optimization, known as the
hypervolume indicator, and the problem of computing
it efficiently is attracting increasing
attention. In this report, this problem is
formulated as a special case of Klee's measure
problem in $d$ dimensions, which immediately
establishes $O(n^{d/2}\log n)$ as a, possibly
conservative, upper bound on the required
computation time. Then, an $O(n log n)$ algorithm
for the 3-dimensional version of this special case
is constructed, based on an existing dimension-sweep
algorithm for the related maxima problem. Finally,
$O(n log n)$ is shown to remain a lower bound on the
time required by the hypervolume indicator for
$d>1$, which attests the optimality of the algorithm
proposed.},
note = {Superseded by paper in IEEE Transactions on Evolutionary Computation~\cite{BeuFonLopPaqVah09:tec}},
annote = {Proof of Theorem 3.1 is incorrect}
}
@techreport{PaqStuLop-IRIDIA-2005-029,
author = { Lu{\'\i}s Paquete and Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {On the design and analysis of {SLS} algorithms for
multiobjective combinatorial optimization problems},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2005,
number = {TR/IRIDIA/2005-029},
abstract = {Effective Stochastic Local Search (SLS) algorithms
can be seen as being composed of several algorithmic
components, each of which plays some specific role
with respect to overall performance. In this
article, we explore the application of experimental
design techniques to analyze the effect of different
choices for these algorithmic components on SLS
algorithms applied to Multiobjective Combinatorial
Optimization Problems that are solved in terms of
{P}areto optimality. This analysis is done using the
example application of SLS algorithms to the
biobjective Quadratic Assignment Problem and we show
also that the same choices for algorithmic
components can lead to different behavior in
dependence of various instance features, such as the
structure of input data and the correlation between
objectives.},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2005-029r001.pdf}
}
@techreport{LopPaqStu04:hybrid,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'\i}s Paquete and Thomas St{\"u}tzle },
title = {Hybrid Population-based Algorithms for the
Bi-objective Quadratic Assignment Problem},
institution = {FG Intellektik, FB Informatik, TU Darmstadt},
year = 2004,
number = {AIDA--04--11},
month = dec,
note = {Published in Journal of Mathematical Modelling and Algorithms~\cite{LopPaqStu05:jmma}}
}
@phdthesis{LopezIbanezPhD,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Operational Optimisation of Water Distribution
Networks},
school = {School of Engineering and the Built Environment},
year = 2009,
address = {Edinburgh Napier University, UK},
url = {http://researchrepository.napier.ac.uk/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{BezLopStu2016automoea2,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatically designing and understanding evolutionary
algorithms for multi- and many-objective optimization},
year = 2016,
note = {To be submitted}
}
@misc{LopPaqStu2010:eaftools,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'\i}s Paquete and Thomas St{\"u}tzle },
title = {{EAF} Graphical Tools},
year = 2010,
howpublished = {\url{http://lopez-ibanez.eu/eaftools}},
note = {These tools are described in the book chapter
``\emph{Exploratory analysis of stochastic local search
algorithms in biobjective
optimization}''~\cite{LopPaqStu09emaa}.},
annote = {Please cite the book chapter, not this.}
}
@inproceedings{HunLop2019turing,
isbn = {978-1-5262-0820-0},
organization = {Alan Turing Institute},
month = nov # {, 21--22},
address = {London, UK},
editor = {Iván Palomares},
booktitle = {International Alan Turing Conference on Decision Support and
Recommender systems},
year = 2019,
author = {Maura Hunt and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Modeling a Decision-Maker in Goal Programming by means of
Computational Rationality},
pages = {17--20},
abstract = {This paper extends a simulation of cognitive mechanisms in
the context of multi-criteria decision-making by using ideas
from computational rationality. Specifically, this paper
improves the simulation of a human decision-maker (DM) by
considering how resource constraints impact their evaluation
process in an interactive Goal Programming problem. Our
analysis confirms and emphasizes a previous simulation study
by showing key areas that could be effected by cognitive
mechanisms. While the results are promising, the effects
should be validated by future experiments with human DMs. }
}
@inproceedings{BezLopStu2015moead,
editor = { Ant{\'o}nio Gaspar{-}Cunha and Carlos Henggeler Antunes and Carlos A. {Coello Coello} },
volume = 9018,
year = 2015,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
booktitle = {Evolutionary Multi-criterion Optimization, EMO 2015 Part {I}},
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Comparing De\-com\-po\-sition-Based and Automatically
Component-Wise Designed Multi-Objective Evolutionary
Algorithms},
pages = {396--410},
doi = {10.1007/978-3-319-15934-8_27}
}
@inproceedings{LopMasMarStu2013mista,
year = 2013,
editor = { Graham Kendall and Greet Vanden Berghe and Barry McCollum},
address = {Gent, Belgium},
booktitle = {Multidisciplinary International Conference on Scheduling:
Theory and Applications (MISTA 2013)},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Franco Mascia and Marie-El{\'e}onore Marmion and Thomas St{\"u}tzle },
title = {Automatic Design of a Hybrid Iterated Local Search for the
Multi-Mode Resource-Constrained Multi-Project Scheduling
Problem},
pages = {1--6},
annote = {\url{https://hal.inria.fr/hal-01094681}},
pdf = {LopMasMarStu2013mista.pdf}
}
@inproceedings{LopPraPae08:WDSA,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Parallel Optimisation Of Pump Schedules With A
Thread-Safe Variant Of {EPANET} Toolkit},
booktitle = {Proceedings of the 10th Annual Water Distribution
Systems Analysis Conference (WDSA 2008)},
year = 2008,
editor = { Jakobus E. van Zyl and A. A. Ilemobade and H. E. Jacobs },
month = aug,
pdf = {LopezPrasadPaechter-WDSA2008-official.pdf},
doi = {10.1061/41024(340)40},
publisher = {ASCE}
}
@inproceedings{LopPraPaech:ccwi2005,
month = sep,
address = {University of Exeter, UK},
volume = 1,
editor = { Dragan A. Savic and Godfrey A. Walters and Roger King and Soon Thiam-Khu },
year = 2005,
booktitle = {Proceedings of the Eighth International Conference on
Computing and Control for the Water Industry (CCWI 2005)},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Optimal Pump Scheduling: Representation and Multiple
Objectives},
pdf = {LopPraPae05-ccwi.pdf},
pages = {117--122}
}
@inproceedings{PaqStuLop05mic,
address = {Vienna, Austria},
year = 2005,
booktitle = {6th Metaheuristics International Conference (MIC 2005)},
editor = { Karl F. Doerner and Michel Gendreau and Peter Greistorfer and Gutjahr, Walter J. and Richard F. Hartl and Marc Reimann },
author = { Lu{\'\i}s Paquete and Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Towards the Empirical Analysis of {SLS} Algorithms
for Multiobjective Combinatorial Optimization
Problems through Experimental Design},
pages = {739--746},
abstract = { Stochastic Local Search (SLS) algorithms for
Multiobjective Combinatorial Optimization Problems
(MCOPs) typically involve the selection and
parameterization of many algorithm components whose
role with respect to their overall performance and
relation to certain instance features is often not
clear. In this abstract, we use a modular approach
for the design of SLS algorithms for MCOPs defined
in terms of {P}areto optimality and we present an
extensive analysis of SLS algorithms through
experimental design techniques, where each algorithm
component is considered a factor. The experimental
analysis is based on a sound experimental
methodology for analyzing the output of algorithms
for MCOPs. We show that different choices for
algorithm components can lead to different behavior
in dependence of various instance features.},
pdf = {PaqStuLop05mic.pdf}
}
@incollection{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{NebLopBarGar2019gecco,
aurl = {https://dl.acm.org/citation.cfm?id=3319619},
isbn = {978-1-4503-6748-6},
address = {New York, NY},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
booktitle = {GECCO'19 Companion},
title = {Automatic Configuration of {NSGA-II} with {jMetal} and irace},
author = {Antonio J. Nebro and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Barba-González, Cristóbal and Jos{\'{e}} Garc\'{\i}a-Nieto },
doi = {10.1145/3319619.3326832},
pdf = {NebLopBarGar2019gecco.pdf}
}
@incollection{StuLop2019hb,
publisher = {Springer},
series = {International Series in Operations Research \& Management
Science},
volume = 272,
booktitle = {Handbook of Metaheuristics},
year = 2019,
editor = { Michel Gendreau and Jean-Yves Potvin },
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Automated Design of Metaheuristic Algorithms},
pages = {541--579},
doi = {10.1007/978-3-319-91086-4_17}
}
@incollection{SaiLopMie2019gecco,
aurl = {https://dl.acm.org/citation.cfm?id=3319619},
isbn = {978-1-4503-6748-6},
address = {New York, NY},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
booktitle = {GECCO'19 Companion},
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}
}
@incollection{ShaKomLopKaz2019gecco,
aurl = {https://dl.acm.org/citation.cfm?id=3321707},
isbn = {978-1-4503-6111-8},
address = {New York, NY},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2019},
title = {Deep Reinforcement Learning-Based Parameter Control in
Differential Evolution},
author = { Mudita Sharma and Alexandros Komninos and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Dimitar Kazakov },
supplement = {https://dx.doi.org/10.5281/zenodo.2628228},
doi = {10.1145/3321707.3321813},
pdf = {ShaKomLopKaz2019gecco.pdf},
keywords = {DE-DDQN}
}
@incollection{BezLopStu2019gecco,
aurl = {https://dl.acm.org/citation.cfm?id=3321707},
isbn = {978-1-4503-6111-8},
address = {New York, NY},
publisher = {ACM Press},
year = 2019,
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2019},
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Archiver Effects on the Performance of State-of-the-art
Multi- and Many-objective Evolutionary Algorithms:
Supplementary material},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2019-004/},
doi = {10.1145/3321707.3321789},
pdf = {BezLopStu2019gecco.pdf}
}
@incollection{MazChuMietLop2019emo,
isbn = {978-3-030-12597-4},
year = 2019,
address = {Cham, Switzerland},
publisher = {Springer International Publishing},
volume = 11411,
series = {Lecture Notes in Computer Science},
booktitle = {Evolutionary Multi-criterion Optimization, EMO 2019},
editor = { Kalyanmoy Deb and Erik D. Goodman and Carlos A. {Coello Coello} and Kathrin
Klamroth and Kaisa Miettinen and Sanaz Mostaghim and Patrick
Reed},
author = { Atanu Mazumdar and Tinkle Chugh and Kaisa Miettinen and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {On Dealing with Uncertainties from Kriging Models in Offline
Data-Driven Evolutionary Multiobjective Optimization},
pages = {463--474},
doi = {10.1007/978-3-030-12598-1_37}
}
@incollection{ShaLopKaz2018ppsn,
volume = {11102},
year = 2018,
publisher = {Springer, Cham},
series = {Lecture Notes in Computer Science},
editor = { Anne Auger and Carlos M. Fonseca and Louren{\c c}o, N. and Machado,
P. and Lu{\'\i}s Paquete and Darrell Whitley },
booktitle = {Parallel Problem Solving from Nature - PPSN XV},
author = { Mudita Sharma and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Dimitar Kazakov },
title = {Performance Assessment of Recursive Probability Matching for
Adaptive Operator Selection in Differential Evolution},
supplement = {https://github.com/mudita11/AOS-comparisons},
doi = {10.1007/978-3-319-99259-4_26},
pages = {321--333},
keywords = {Rec-PM}
}
@incollection{LopStuDor2017aco,
isbn = {978-3-319-07125-1},
publisher = {Springer International Publishing},
year = 2018,
booktitle = {Handbook of Heuristics},
editor = {Rafael Mart{\'\i} and Panos M. Pardalos and Mauricio G. C. Resende },
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle and Marco Dorigo },
title = {Ant Colony Optimization: A Component-Wise Overview},
doi = {10.1007/978-3-319-07124-4_21},
pages = {371--407},
supplement = {http://iridia.ulb.ac.be/aco-tsp-qap/}
}
@incollection{BroCalLop2018dagstuhl,
keywords = {multiple criteria decision making, evolutionary
multiobjective optimization},
doi = {10.4230/DagRep.8.1.33},
volume = {8(1)},
year = 2018,
series = {Dagstuhl Reports},
publisher = {Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik,
Germany},
booktitle = {Personalized Multiobjective Optimization: An Analytics
Perspective (Dagstuhl Seminar 18031)},
editor = { Kathrin Klamroth and Joshua D. Knowles and G{\"u}nther Rudolph and Margaret M. Wiecek },
author = { Dimo Brockhoff and Roberto Calandra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Frank Neumann and Selvakumar Ulaganathan},
title = {Meta-modeling for (interactive) multi-objective optimization
(WG5)},
pages = {85--94}
}
@incollection{BloLopKesJou2018ppsn,
volume = {11101},
year = 2018,
publisher = {Springer, Cham},
series = {Lecture Notes in Computer Science},
editor = { Anne Auger and Carlos M. Fonseca and Louren{\c c}o, N. and Machado,
P. and Lu{\'\i}s Paquete and Darrell Whitley },
booktitle = {Parallel Problem Solving from Nature - PPSN XV},
author = { Aymeric Blot and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Marie-El{\'e}onore Kessaci-Marmion and Laetitia Jourdan },
title = {New Initialisation Techniques for Multi-Objective Local
Search: Application to the Bi-objective Permutation Flowshop},
doi = {10.1007/978-3-319-99253-2_26},
pages = {323--334}
}
@incollection{LieLopPaqVer2018gecco,
address = {New York, NY},
publisher = {ACM Press},
year = 2018,
editor = { Aguirre, Hern\'{a}n E. and Keiki Takadama},
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2018},
author = { Arnaud Liefooghe and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'\i}s Paquete and Verel, S{\'e}bastien },
title = {Dominance, Epsilon, and Hypervolume Local Optimal Sets in
Multi-objective Optimization, and How to Tell the Difference},
pages = {324--331},
doi = {10.1145/3205455.3205572},
pdf = {LieLopPaqVer2018gecco.pdf}
}
@incollection{LieDerVerLop2018ppsn,
volume = {11102},
year = 2018,
publisher = {Springer, Cham},
series = {Lecture Notes in Computer Science},
editor = { Anne Auger and Carlos M. Fonseca and Louren{\c c}o, N. and Machado,
P. and Lu{\'\i}s Paquete and Darrell Whitley },
booktitle = {Parallel Problem Solving from Nature - PPSN XV},
author = { Arnaud Liefooghe and Bilel Derbel and Verel, S{\'e}bastien and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Aguirre, Hern\'{a}n E. and Tanaka, Kiyoshi },
title = {On {P}areto 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 = {GECCO'17 Companion},
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Automated Offline Design of Algorithms},
pages = {1038--1065},
doi = {10.1145/3067695.3067722}
}
@incollection{BezLopStu2017emo,
editor = {Heike Trautmann and G{\"{u}}nter Rudolph and Kathrin Klamroth
and Oliver Sch{\"{u}}tze and Margaret M. Wiecek and Yaochu
Jin and Christian Grimme},
year = 2017,
series = {Lecture Notes in Computer Science},
address = {Cham, Switzerland},
publisher = {Springer International Publishing},
booktitle = {Evolutionary Multi-criterion Optimization, EMO 2017},
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An Empirical Assessment of the Properties of Inverted
Generational Distance Indicators on Multi- and Many-objective
Optimization},
pages = {31--45},
doi = {10.1007/978-3-319-54157-0_3}
}
@incollection{PerLopHooStu2017:lion,
address = {Cham, Switzerland},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 10556,
editor = { Roberto Battiti and Dmitri E. Kvasov and Yaroslav D. Sergeyev},
year = 2017,
booktitle = {Learning and Intelligent Optimization, 11th International
Conference, LION 11},
author = { P{\'e}rez C{\'a}ceres, Leslie and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Holger H. Hoos and Thomas St{\"u}tzle },
title = {An experimental study of adaptive capping in irace},
pages = {235--250},
pdf = {PerLopHooStu2017lion.pdf},
doi = {10.1007/978-3-319-69404-7_17},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2016-007/}
}
@incollection{StuLop2015gecco,
address = {New York, NY},
publisher = {ACM Press},
year = 2015,
editor = {Juan Luis Jim{\'{e}}nez Laredo and Sara Silva and Anna I. Esparcia{-}Alc{\'{a}}zar },
booktitle = {{GECCO} (Companion)},
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Automatic (Offline) Configuration of Algorithms},
pages = {681--702},
doi = {10.1145/2739482.2756581}
}
@incollection{LopKno2015emo,
editor = { Ant{\'o}nio Gaspar{-}Cunha and Carlos Henggeler Antunes and Carlos A. {Coello Coello} },
volume = 9019,
year = 2015,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
booktitle = {Evolutionary Multi-criterion Optimization, EMO 2015 Part {II}},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Joshua D. Knowles },
title = {Machine Decision Makers as a Laboratory for Interactive EMO},
pages = {295--309},
abstract = {A key challenge, perhaps the central challenge, of
multi-objective optimization is how to deal with candidate
solutions that are ultimately evaluated by the hidden or
unknown preferences of a human decision maker (DM) who
understands and cares about the optimization problem.
Alternative ways of addressing this challenge exist but
perhaps the favoured one currently is the interactive
approach (proposed in various forms). Here, an evolutionary
multi-objective optimization algorithm (EMOA) is controlled
by a series of interactions with the DM so that preferences
can be elicited and the direction of search controlled. MCDM
has a key role to play in designing and evaluating these
approaches, particularly in testing them with real DMs, but
so far quantitative assessment of interactive EMOAs has been
limited. In this paper, we propose a conceptual framework
for this problem of quantitative assessment, based on the
definition of machine decision makers (machine DMs), made
somewhat realistic by the incorporation of various
non-idealities. The machine DM proposed here draws from
earlier models of DM biases and inconsistencies in the MCDM
literature. As a practical illustration of our approach, we
use the proposed machine DM to study the performance of an
interactive EMOA, and discuss how this framework could help
in the evaluation and development of better interactive
EMOAs.},
doi = {10.1007/978-3-319-15892-1_20},
pdf = {LopKno2015emo.pdf}
}
@incollection{BezLopStu2015emode,
editor = { Ant{\'o}nio Gaspar{-}Cunha and Carlos Henggeler Antunes and Carlos A. {Coello Coello} },
volume = 9018,
year = 2015,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
booktitle = {Evolutionary Multi-criterion Optimization, EMO 2015 Part {I}},
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {To {DE} or Not to {DE}? {M}ulti-objective Differential
Evolution Revisited from a Component-Wise Perspective},
pages = {48--63},
doi = {10.1007/978-3-319-15934-8_4}
}
@incollection{BraCorGre2015dagstuhl,
keywords = {multiple criteria decision making, evolutionary
multiobjective optimization},
doi = {10.4230/DagRep.5.1.96},
volume = {5(1)},
year = 2015,
series = {Dagstuhl Reports},
publisher = {Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik,
Germany},
booktitle = {Understanding Complexity in Multiobjective Optimization
(Dagstuhl Seminar 15031)},
editor = { Salvatore Greco and Kathrin Klamroth and Joshua D. Knowles and G{\"u}nther Rudolph },
author = { J{\"u}rgen Branke and Salvatore Corrente and Salvatore Greco and Milosz Kadzinski and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Vincent Mousseau and Mauro Munerato and Roman S{\l}owi{\'n}ski },
title = {Behavior-Realistic Artificial Decision-Makers to Test
Preference-Based Multi-objective Optimization Method
({W}orking {G}roup ``{M}achine {D}ecision-{M}aking'')},
pages = {110--116}
}
@incollection{LopLieVer2014ppsn,
year = 2014,
volume = 8672,
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = {Thomas Bartz-Beielstein and J{\"u}rgen Branke and Bogdan Filipi{\v c} and Jim Smith},
booktitle = {PPSN 2014},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Arnaud Liefooghe and Verel, S{\'e}bastien },
doi = {10.1007/978-3-319-10762-2_61},
title = {Local Optimal Sets and Bounded Archiving on Multi-objective
{NK}-Landscapes with Correlated Objectives},
pages = {621--630},
pdf = {LopLieVer2014ppsn.pdf}
}
@incollection{BezLopStu2014:lion,
publisher = {Springer},
volume = 8426,
editor = { Panos M. Pardalos and Mauricio G. C. Resende and Chrysafis Vogiatzis and Jose
L. Walteros},
series = {Lecture Notes in Computer Science},
year = 2014,
booktitle = {Learning and Intelligent Optimization, 8th International Conference, LION 8},
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Deconstructing Multi-Objective Evolutionary Algorithms: An
Iterative Analysis on the Permutation Flowshop},
pages = {57--172},
doi = {10.1007/978-3-319-09584-4_16},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2013-010/}
}
@incollection{BezLopStu2014:ppsn,
year = 2014,
volume = 8672,
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = {Thomas Bartz-Beielstein and J{\"u}rgen Branke and Bogdan Filipi{\v c} and Jim Smith},
booktitle = {PPSN 2014},
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Design of Evolutionary Algorithms for
Multi-Objective Combinatorial Optimization},
doi = {10.1007/978-3-319-10762-2_50},
pages = {508--517}
}
@incollection{HutLopFaw2014lion,
publisher = {Springer},
volume = 8426,
editor = { Panos M. Pardalos and Mauricio G. C. Resende and Chrysafis Vogiatzis and Jose
L. Walteros},
series = {Lecture Notes in Computer Science},
year = 2014,
booktitle = {Learning and Intelligent Optimization, 8th International Conference, LION 8},
author = { Frank Hutter and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Chris Fawcett and Marius Thomas Lindauer and Holger H. Hoos and Kevin Leyton-Brown and Thomas St{\"u}tzle },
title = {{AClib}: a Benchmark Library for Algorithm Configuration},
pages = {36--40},
doi = {10.1007/978-3-319-09584-4_4},
pdf = {HutLopFaw2014lion.pdf}
}
@incollection{MasLopDub2014hm,
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 8457,
isbn = {978-3-319-07643-0},
editor = { Mar{\'\i}a J. Blesa and Christian Blum and Stefan Vo{\ss} },
year = 2014,
booktitle = {Hybrid Metaheuristics},
author = { Franco Mascia and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and Marie-El{\'e}onore Marmion and Thomas St{\"u}tzle },
title = {Algorithm Comparison by Automatically Configurable Stochastic
Local Search Frameworks: A Case Study Using Flow-Shop
Scheduling Problems},
pages = {30--44},
pdf = {MasLopDu2014hm.pdf},
doi = {10.1007/978-3-319-07644-7_3}
}
@incollection{PerLopStu2014ants,
volume = 8667,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
editor = { Marco Dorigo and others },
year = 2014,
booktitle = {Swarm Intelligence, 9th International Conference, ANTS 2014},
author = { P{\'e}rez C{\'a}ceres, Leslie and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Ant Colony Optimization on a Budget of 1000},
doi = {10.1007/978-3-319-09952-1_5},
pages = {50--61}
}
@incollection{PerLopStu2014evocop,
publisher = {Springer},
volume = 8600,
series = {Lecture Notes in Computer Science},
year = 2014,
booktitle = {Proceedings of EvoCOP 2014 -- 14th European Conference on Evolutionary Computation in Combinatorial Optimization},
editor = { Christian Blum and Gabriela Ochoa },
author = { P{\'e}rez C{\'a}ceres, Leslie and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An Analysis of Parameters of irace},
doi = {10.1007/978-3-662-44320-0_4},
pages = {37--48}
}
@incollection{MarMasLop2013hm,
publisher = {Springer},
volume = 7919,
series = {Lecture Notes in Computer Science},
editor = { Mar{\'\i}a J. Blesa and Christian Blum and Paola Festa and Andrea Roli and M. Sampels },
isbn = {978-3-642-38515-5},
year = 2013,
booktitle = {Hybrid Metaheuristics},
author = { Marie-El{\'e}onore Marmion and Franco Mascia and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Design of Hybrid Stochastic Local Search
Algorithms},
pages = {144--158},
doi = {10.1007/978-3-642-38516-2_12},
pdf = {MarMasLopStu2013hm.pdf}
}
@incollection{BezLopStu2013evocop,
publisher = {Springer},
series = {Lecture Notes in Computer Science},
year = 2013,
volume = 7832,
booktitle = {Proceedings of EvoCOP 2013 -- 13th European Conference on Evolutionary Computation in Combinatorial Optimization},
editor = { Martin Middendorf and Christian Blum },
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An Analysis of Local Search for the Bi-objective
Bidimensional Knapsack Problem},
pages = {85--96},
doi = {10.1007/978-3-642-37198-1_8}
}
@incollection{DubLopStu2013hm,
url = {http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-30670-9},
year = 2013,
volume = 434,
series = {Studies in Computational Intelligence},
editor = { Talbi, El-Ghazali },
publisher = {Springer Verlag},
booktitle = {Hybrid Metaheuristics},
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Combining Two Search Paradigms for Multi-objective
Optimization: {T}wo-{P}hase and {P}areto Local Search},
pages = {97--117},
doi = {10.1007/978-3-642-30671-6_3},
alias = {DubLopStu2012hm},
pdf = {DubLopStu2013hm.pdf}
}
@incollection{MasLopDubStu2013lion,
publisher = {Springer},
volume = 7997,
editor = { Panos M. Pardalos and G. Nicosia},
series = {Lecture Notes in Computer Science},
year = 2013,
booktitle = {Learning and Intelligent Optimization, 7th
International Conference, LION 7},
author = { Franco Mascia and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and Thomas St{\"u}tzle },
title = {From Grammars to Parameters: Automatic Iterated
Greedy Design for the Permutation Flow-shop Problem
with Weighted Tardiness},
pages = {321--334},
pdf = {MasLopDubStu2013lion.pdf},
doi = {10.1007/978-3-642-44973-4_36}
}
@incollection{MasLopStu2013hm,
publisher = {Springer},
volume = 7919,
series = {Lecture Notes in Computer Science},
editor = { Mar{\'\i}a J. Blesa and Christian Blum and Paola Festa and Andrea Roli and M. Sampels },
isbn = {978-3-642-38515-5},
year = 2013,
booktitle = {Hybrid Metaheuristics},
author = { Florence Massen and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle and Yves Deville },
title = {Experimental Analysis of Pheromone-Based Heuristic
Column Generation Using irace},
pages = {92-106},
doi = {10.1007/978-3-642-38516-2_8},
pdf = {MasLopStu2013hm.pdf}
}
@incollection{RadLopStu2013emo,
isbn = {978-3-642-37139-4},
year = 2013,
volume = 7811,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
booktitle = {Evolutionary Multi-criterion Optimization, EMO 2013},
editor = { Robin C. Purshouse and Peter J. Fleming and Carlos M. Fonseca and Salvatore Greco and Jane Shaw},
author = { Andreea Radulescu and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatically Improving the Anytime Behaviour of
Multiobjective Evolutionary Algorithms},
pages = {825--840},
doi = {10.1007/978-3-642-37140-0_61}
}
@incollection{StuLopPel2011autsea,
year = 2012,
address = {Berlin, Germany},
publisher = {Springer},
booktitle = {Autonomous Search},
editor = {Y. Hamadi and E. Monfroy and F. Saubion},
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Paola Pellegrini and Michael Maur and Marco A. {Montes de Oca} and Mauro Birattari and Marco Dorigo },
title = {Parameter Adaptation in Ant Colony Optimization},
doi = {10.1007/978-3-642-21434-9_8},
pages = {191--215}
}
@incollection{LopLiaStu2012ppsn,
volume = 7491,
year = 2012,
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Carlos A. {Coello Coello} and others},
booktitle = {Parallel Problem Solving from Nature, PPSN XII},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Liao, Tianjun and Thomas St{\"u}tzle },
title = {On the anytime behavior of {IPOP-CMA-ES}},
pages = {357--366},
doi = {10.1007/978-3-642-32937-1_36}
}
@incollection{BezLopStu2012:ants,
volume = 7461,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
editor = { Marco Dorigo and others },
year = 2012,
booktitle = {Swarm Intelligence, 8th International Conference, ANTS 2012},
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Generation of Multi-objective {ACO}
Algorithms for the Biobjective Knapsack},
pages = {37--48},
doi = {10.1007/978-3-642-32650-9_4},
pdf = {BezLopStu2012ants.pdf},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2012-008/}
}
@incollection{DubLopStu2012evocop,
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 7245,
year = 2012,
editor = { Jin-Kao Hao and Martin Middendorf },
booktitle = {Proceedings of EvoCOP 2012 -- 12th European Conference on Evolutionary Computation in Combinatorial Optimization},
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {{P}areto Local Search Algorithms for Anytime
Bi-objective Optimization},
pages = {206--217},
doi = {10.1007/978-3-642-29124-1_18},
alias = {DubLopStu12:evocop}
}
@incollection{BroLopNau2012ppsn,
volume = 7491,
year = 2012,
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = { Carlos A. {Coello Coello} and others},
booktitle = {Parallel Problem Solving from Nature, PPSN XII},
author = { Dimo Brockhoff and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Boris Naujoks and G{\"u}nther Rudolph },
title = {Runtime Analysis of Simple Interactive Evolutionary
Biobjective Optimization Algorithms},
pages = {123--132},
doi = {10.1007/978-3-642-32937-1_13},
abstract = {Development and deployment of interactive evolutionary
multiobjective optimization algorithms (EMOAs) have recently
gained broad interest. In this study, first steps towards a
theory of interactive EMOAs are made by deriving bounds on
the expected number of function evaluations and queries to a
decision maker. We analyze randomized local search and the
(1+1)-EA on the biobjective problems LOTZ and COCZ under the
scenario that the decision maker interacts with these
algorithms by providing a subjective preference whenever
solutions are incomparable. It is assumed that this decision
is based on the decision maker's internal utility
function. We show that the performance of the interactive
EMOAs may dramatically worsen if the utility function is
non-linear instead of linear.}
}
@incollection{AugBroLop2012dagstuhl,
doi = {10.4230/DagRep.2.1.50},
series = {Dagstuhl Reports},
volume = {2(1)},
year = 2012,
publisher = {Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik,
Germany},
booktitle = {Learning in Multiobjective Optimization (Dagstuhl Seminar
12041)},
editor = { Salvatore Greco and Joshua D. Knowles and Kaisa Miettinen and Eckart Zitzler },
author = { Anne Auger and Dimo Brockhoff and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Kaisa Miettinen and Boris Naujoks and G{\"u}nther Rudolph },
title = {Which questions should be asked to find the most appropriate
method for decision making and problem solving? ({W}orking
{G}roup ``{A}lgorithm {D}esign {M}ethods'')},
pages = {92--93}
}
@incollection{StuLopDor2011eorms,
year = 2011,
publisher = {John Wiley \& Sons},
editor = {J. J. Cochran},
booktitle = {Wiley Encyclopedia of Operations Research and
Management Science},
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Marco Dorigo },
title = {A Concise Overview of Applications of Ant Colony
Optimization},
pages = {896--911},
volume = 2,
doi = {10.1002/9780470400531.eorms0001}
}
@incollection{EppLopStuDeS2011:cec,
year = 2011,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2011 Congress on Evolutionary
Computation (CEC 2011)},
key = {IEEE CEC},
author = { Stefan Eppe and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle and Yves {De Smet} },
title = {An Experimental Study of Preference Model Integration into
Multi-Objective Optimization Heuristics},
pages = {2751--2758},
doi = {10.1109/CEC.2011.5949963}
}
@incollection{LopKnoLau2011emo,
publisher = {Springer},
year = 2011,
series = {Lecture Notes in Computer Science},
volume = 6576,
editor = { Takahashi, R. H. C. and others},
booktitle = {Evolutionary Multi-criterion Optimization, EMO 2011},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Joshua D. Knowles and Marco Laumanns },
title = {On Sequential Online Archiving of Objective Vectors},
pages = {46--60},
doi = {10.1007/978-3-642-19893-9_4},
abstract = {In this paper, we examine the problem of maintaining
an approximation of the set of nondominated points
visited during a multiobjective optimization, a
problem commonly known as archiving. Most of the
currently available archiving algorithms are
reviewed, and what is known about their convergence
and approximation properties is summarized. The main
scenario considered is the restricted case where the
archive must be updated online as points are
generated one by one, and at most a fixed number of
points are to be stored in the archive at any one
time. In this scenario, the better-monotonicity of
an archiving algorithm is proposed as a weaker, but
more practical, property than negative efficiency
preservation. This paper shows that
hypervolume-based archivers and a recently proposed
multi-level grid archiver have this property. On the
other hand, the archiving methods used by SPEA2 and
NSGA-II do not, and they may better-deteriorate with
time. The better-monotonicity property has meaning
on any input sequence of points. We also classify
archivers according to limit properties,
i.e. convergence and approximation properties of the
archiver in the limit of infinite (input) samples
from a finite space with strictly positive
generation probabilities for all points. This paper
establishes a number of research questions, and
provides the initial framework and analysis for
answering them.},
annote = {Revised version available at \url{http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2011-001.pdf}}
}
@incollection{DubLopStu2011gecco,
address = {New York, NY},
publisher = {ACM Press},
year = 2011,
editor = {Natalio Krasnogor and Pier Luca Lanzi},
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2011},
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Configuration of State-of-the-art Multi-objective
Optimizers Using the {TP+PLS} Framework},
pages = {2019--2026},
doi = {10.1145/2001576.2001847}
}
@incollection{BluLop2011ieh,
author = { Christian Blum and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
booktitle = {The Industrial Electronics Handbook: Intelligent Systems},
title = {Ant Colony Optimization},
publisher = {CRC Press},
year = 2011,
edition = {Second},
isbn = 9781439802830,
url = {http://www.crcpress.com/product/isbn/9781439802830},
annnote = {http://www.eng.auburn.edu/~wilambm/ieh/}
}
@incollection{FonGueLopPaq2011emo,
publisher = {Springer},
year = 2011,
series = {Lecture Notes in Computer Science},
volume = 6576,
editor = { Takahashi, R. H. C. and others},
booktitle = {Evolutionary Multi-criterion Optimization, EMO 2011},
author = { Carlos M. Fonseca and Andreia P. Guerreiro and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'\i}s Paquete },
title = {On the Computation of the Empirical Attainment Function},
doi = {10.1007/978-3-642-19893-9_8},
pages = {106--120},
pdf = {FonGueLopPaq2011emo.pdf}
}
@incollection{MauLopStu2010:cec,
year = 2010,
address = {Piscataway, NJ},
publisher = {IEEE Press},
booktitle = {Proceedings of the 2010 Congress on Evolutionary
Computation (CEC 2010)},
editor = { Ishibuchi, Hisao and others},
author = { Michael Maur and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Pre-scheduled and adaptive parameter variation in
{\MaxMinAntSystem}},
pages = {3823--3830},
doi = {10.1109/CEC.2010.5586332},
pdf = {MauLopStu2010-Parameter Adaptation in Max-Min Ant System.pdf}
}
@incollection{LopStu09ea,
publisher = {Springer},
editor = {Pierre Collet and Nicolas Monmarch{\'e} and Pierrick
Legrand and Marc Schoenauer and Evelyne Lutton},
shorteditor = {Pierre Collet and others},
volume = 5975,
series = {Lecture Notes in Computer Science},
year = 2010,
booktitle = {Artificial Evolution: 9th International Conference, Evolution Artificielle, EA, 2009},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An Analysis of Algorithmic Components for
Multiobjective Ant Colony Optimization: {A} Case
Study on the Biobjective {TSP}},
pages = {134--145},
doi = {10.1007/978-3-642-14156-0_12}
}
@incollection{LopStu2010:ants,
volume = 6234,
series = {Lecture Notes in Computer Science},
publisher = {Springer},
fulleditor = { Marco Dorigo and Mauro Birattari and Gianni A. {Di Caro} and Doursat, R. and Engelbrecht, A. P. and Floreano,
D. and Gambardella, L. M. and Gro\ss, R. and Sahin,
E. and Thomas St{\"u}tzle and Sayama, H.},
editor = { Marco Dorigo and others },
year = 2010,
booktitle = {Swarm Intelligence, 7th International Conference, ANTS 2010},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Configuration of Multi-Objective {ACO}
Algorithms},
pages = {95--106},
doi = {10.1007/978-3-642-15461-4_9},
abstract = {In the last few years a significant number of ant
colony optimization (ACO) algorithms have been
proposed for tackling multi-objective optimization
problems. In this paper, we propose a software
framework that allows to instantiate the most
prominent multi-objective ACO (MOACO)
algorithms. More importantly, the flexibility of
this MOACO framework allows the application of
automatic algorithm configuration techniques. The
experimental results presented in this paper show
that such an automatic configuration of MOACO
algorithms is highly desirable, given that our
automatically configured algorithms clearly
outperform the best performing MOACO algorithms that
have been proposed in the literature. As far as we
are aware, this paper is also the first to apply
automatic algorithm configuration techniques to
multi-objective stochastic local search algorithms.}
}
@incollection{LopStu2010:gecco,
address = {New York, NY},
publisher = {ACM Press},
year = 2010,
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2010},
editor = {Martin Pelikan and J{\"u}rgen Branke },
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {The impact of design choices of multi-objective ant
colony optimization algorithms on performance: An
experimental study on the biobjective {TSP}},
doi = {10.1145/1830483.1830494},
pages = {71--78},
abstract = {Over the last few years, there have been a number of
proposals of ant colony optimization (ACO)
algorithms for tackling multiobjective combinatorial
optimization problems. These proposals adapt ACO
concepts in various ways, for example, some use
multiple pheromone matrices and multiple heuristic
matrices and others use multiple ant colonies.\\ In
this article, we carefully examine several of the
most prominent of these proposals. In particular, we
identify commonalities among the approaches by
recasting the original formulation of the algorithms
in different terms. For example, several proposals
described in terms of multiple colonies can be cast
equivalently using a single ant colony, where ants
use different weights for aggregating the pheromone
and/or the heuristic information. We study
algorithmic choices for the various proposals and we
identify previously undetected trade-offs in their
performance.}
}
@incollection{LopPaqStu09emaa,
editor = { Thomas Bartz-Beielstein and Marco Chiarandini and Lu{\'\i}s Paquete and Mike Preuss },
year = 2010,
address = {Berlin, Germany},
publisher = {Springer},
booktitle = {Experimental Methods for the Analysis of
Optimization Algorithms},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'\i}s Paquete and Thomas St{\"u}tzle },
title = {Exploratory Analysis of Stochastic Local Search
Algorithms in Biobjective Optimization},
pages = {209--222},
doi = {10.1007/978-3-642-02538-9_9},
abstract = {This chapter introduces two Perl programs that
implement graphical tools for exploring the
performance of stochastic local search algorithms
for biobjective optimization problems. These tools
are based on the concept of the empirical attainment
function (EAF), which describes the probabilistic
distribution of the outcomes obtained by a
stochastic algorithm in the objective space. In
particular, we consider the visualization of
attainment surfaces and differences between the
first-order EAFs of the outcomes of two
algorithms. This visualization allows us to identify
certain algorithmic behaviors in a graphical way.
We explain the use of these visualization tools and
illustrate them with examples arising from
practice.}
}
@incollection{DubLopStu10:lion-bfsp,
publisher = {Springer},
editor = { Christian Blum and Roberto Battiti },
series = {Lecture Notes in Computer Science},
volume = 6073,
year = 2010,
booktitle = {Learning and Intelligent Optimization, 4th
International Conference, LION 4},
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Adaptive ``Anytime'' Two-Phase Local Search},
pages = {52--67},
doi = {10.1007/978-3-642-13800-3_5}
}
@incollection{LopBlu09:evocop,
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 5482,
year = 2009,
editor = { Carlos Cotta and P. Cowling},
booktitle = {Proceedings of EvoCOP 2009 -- 9th European Conference on Evolutionary Computation in Combinatorial Optimization},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum and Dhananjay Thiruvady and Andreas T. Ernst and Bernd Meyer },
title = {Beam-{ACO} based on stochastic sampling for makespan
optimization concerning the {TSP} with time windows},
pages = {97--108},
pdf = {LopBlu09-Beam-ACO-TSPTW-evocop.pdf},
doi = {10.1007/978-3-642-01009-5_9},
alias = {Lop++09}
}
@incollection{LopBlu09:lion,
publisher = {Springer},
year = 2009,
editor = { Thomas St{\"u}tzle },
volume = 5851,
series = {Lecture Notes in Computer Science},
booktitle = {Learning and Intelligent Optimization, Third International Conference, LION 3},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum },
title = {Beam-{ACO} Based on Stochastic Sampling: {A} Case
Study on the {TSP} with Time Windows},
pages = {59--73},
doi = {10.1007/978-3-642-11169-3_5}
}
@incollection{DubLopStu09:hm-bfsp,
publisher = {Springer},
volume = 5818,
series = {Lecture Notes in Computer Science},
editor = { Mar{\'\i}a J. Blesa and Christian Blum and Luca {Di Gaspero} and Andrea Roli and M. Sampels and Andrea Schaerf},
year = 2009,
booktitle = {Hybrid Metaheuristics},
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Effective Hybrid Stochastic Local Search Algorithms for
Biobjective Permutation Flowshop Scheduling},
pages = {100--114},
pdf = {DubLopStu09hm-bfsp.pdf},
doi = {10.1007/978-3-642-04918-7_8},
alias = {DuboisHM09}
}
@incollection{LopPraPae:gecco07,
address = {New York, NY},
publisher = {ACM Press},
year = 2007,
editor = {Dirk Thierens and others},
booktitle = {Proceedings of the Genetic and Evolutionary Computation
Conference, GECCO 2007},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Solving Optimal Pump Control Problem using
{\MaxMinAntSystem}},
volume = 1,
pages = 176,
doi = {10.1145/1276958.1276990},
pdf = {pap212s1-lopezibanez.pdf}
}
@incollection{PaqStuLop07metaheuristics,
author = { Lu{\'\i}s Paquete and Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Using experimental design to analyze stochastic
local search algorithms for multiobjective problems},
booktitle = {Metaheuristics: Progress in Complex Systems
Optimization},
pages = {325--344},
year = 2007,
doi = {10.1007/978-0-387-71921-4_17},
volume = 39,
series = {Operations Research / Computer Science Interfaces},
publisher = {Springer, New York, NY},
annote = {Post-Conference Proceedings of the 6th
Metaheuristics International Conference (MIC 2005)},
editor = {Karl F. Doerner and Michel Gendreau and Peter
Greistorfer and Gutjahr, Walter J. and Richard F. Hartl and Marc Reimann },
abstract = {Stochastic Local Search (SLS) algorithms can be seen
as being composed of several algorithmic components,
each playing some specific role with respect to
overall performance. This article explores the
application of experimental design techniques to
analyze the effect of components of SLS algorithms
for Multiobjective Combinatorial Optimization
problems, in particular for the Biobjective
Quadratic Assignment Problem. The analysis shows
that there exists a strong dependence between the
choices for these components and various instance
features, such as the structure of the input data
and the correlation between the objectives.}
}
@incollection{FonPaqLop06:hypervolume,
address = {Piscataway, NJ},
publisher = {IEEE Press},
month = jul,
year = 2006,
booktitle = {Proceedings of the 2006 Congress on Evolutionary
Computation (CEC 2006)},
key = {IEEE CEC},
author = { Carlos M. Fonseca and Lu{\'\i}s Paquete and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {An improved dimension\hspace{0pt}-\hspace{0pt}sweep
algorithm for the hypervolume indicator},
pages = {1157--1163},
doi = {10.1109/CEC.2006.1688440},
pdf = {FonPaqLop06-hypervolume.pdf},
abstract = {This paper presents a recursive, dimension-sweep
algorithm for computing the hypervolume indicator of
the quality of a set of $n$ non-dominated points in
$d>2$ dimensions. It improves upon the existing HSO
(Hypervolume by Slicing Objectives) algorithm by
pruning the recursion tree to avoid repeated
dominance checks and the recalculation of partial
hypervolumes. Additionally, it incorporates a recent
result for the three-dimensional special case. The
proposed algorithm achieves $O(n^{d-2} \log n)$ time
and linear space complexity in the worst-case, but
experimental results show that the pruning
techniques used may reduce the time complexity
exponent even further.}
}
@incollection{LopPraPaech05:cec,
address = {Piscataway, NJ},
publisher = {IEEE Press},
month = sep,
year = 2005,
booktitle = {Proceedings of the 2005 Congress on Evolutionary
Computation (CEC 2005)},
key = {IEEE CEC},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Multi-objective Optimisation of the Pump Scheduling
Problem using {SPEA2}},
pages = {435--442},
volume = 1,
doi = {10.1109/CEC.2005.1554716}
}
@incollection{LopPaqStu04:ants,
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 3172,
editor = { Marco Dorigo and others },
fulleditor = { Marco Dorigo and L. M. Gambardella and Francesco Mondada and Thomas St{\"u}tzle and Mauro Birattari and Christian Blum },
year = 2004,
booktitle = {Ant Colony Optimization and Swarm Intelligence, 4th
International Workshop, ANTS 2004},
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'\i}s Paquete and Thomas St{\"u}tzle },
title = {On the Design of {ACO} for the Biobjective Quadratic
Assignment Problem},
pages = {214--225},
doi = {10.1007/978-3-540-28646-2_19}
}
@book{GECCO2019c,
title = {Genetic and Evolutionary Computation Conference Companion,
{GECCO} 2019, Prague, Czech Republic, July 13-17, 2019},
booktitle = {GECCO'19 Companion},
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
year = 2019,
publisher = {ACM Press},
address = {New York, NY},
isbn = {978-1-4503-6748-6},
doi = {10.1145/3319619},
aurl = {https://dl.acm.org/citation.cfm?id=3319619}
}
@book{GECCO2019,
title = {Proceedings of the Genetic and Evolutionary Computation
Conference, {GECCO} 2019, Prague, Czech Republic, July 13-17,
2019},
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2019},
editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle },
year = 2019,
publisher = {ACM Press},
address = {New York, NY},
isbn = {978-1-4503-6111-8},
doi = {10.1145/3321707},
aurl = {https://dl.acm.org/citation.cfm?id=3321707}
}
@book{EVOCOP2018,
editor = { Arnaud Liefooghe and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Evolutionary Computation in Combinatorial Optimization --
18th European Conference, EvoCOP 2018, Parma, Italy, April
4-6, 2018, Proceedings},
booktitle = {Proceedings of EvoCOP 2018 -- 18th European Conference on Evolutionary Computation in Combinatorial Optimization},
year = 2018,
series = {Lecture Notes in Computer Science},
volume = 10782,
doi = {10.1007/978-3-319-77449-7},
publisher = {Springer}
}
@book{EVOCOP2017,
editor = { Bin Hu and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Evolutionary Computation in Combinatorial Optimization -- 17th
European Conference, EvoCOP 2017, Amsterdam, The Netherlands,
April 19-21, 2017, Proceedings},
booktitle = {Proceedings of EvoCOP 2017 -- 17th European Conference on Evolutionary Computation in Combinatorial Optimization},
year = 2017,
series = {Lecture Notes in Computer Science},
volume = 10197,
doi = {10.1007/978-3-319-55453-2},
publisher = {Springer}
}
@book{ANTS2016,
title = {Swarm Intelligence, 10th International Conference, ANTS 2016,
Brussels, Belgium, September 7-9, 2016, Proceedings},
booktitle = {Swarm Intelligence, 10th International Conference, ANTS 2016},
year = 2016,
editor = { Marco Dorigo and Mauro Birattari and Li, Xiaodong and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Kazuhiro Ohkura and Carlo Pinciroli and Thomas St{\"u}tzle },
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = 9882,
doi = {10.1007/978-3-319-44427-7}
}
@book{PPSN2016,
booktitle = {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},
volume = 9921,
year = 2016,
doi = {10.1007/978-3-319-45823-6},
isbn = {978-3-319-45822-9}
}
@article{StrLopBroLee2020,
title = {General Northern English: Exploring regional variation in the
North of England with machine learning},
author = { Strycharczuk, Patrycja and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Brown, Georgina and Adrian Leemann },
journal = { Frontiers in Artificial Intelligence },
year = 2020,
keywords = {vowels, accent features, dialect leveling, Random forest
(bagging), Feature selecion},
doi = {10.3389/frai.2020.00048}
}
@article{BezLopStu2019ec,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatically Designing State-of-the-Art Multi- and
Many-Objective Evolutionary Algorithms},
journal = {Evolutionary Computation},
year = 2020,
volume = 28,
number = 2,
pages = {195--226},
doi = {10.1162/evco_a_00263},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2016-004/},
pdf = {BezLopStu2019ec.pdf}
}
@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}
}
@article{WesLop2018ecj,
title = {Latin Hypercube Designs with Branching and Nested Factors for
Initialization of Automatic Algorithm Configuration},
author = { Simon Wessing and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
doi = {10.1162/evco_a_00241},
journal = {Evolutionary Computation},
year = 2018,
pdf = {WesLop2018ecj.pdf},
volume = 27,
number = 1,
pages = {129--145}
}
@article{BezLopStu2017assessment,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {A Large-Scale Experimental Evaluation of High-Performing
Multi- and Many-Objective Evolutionary Algorithms},
year = 2018,
journal = {Evolutionary Computation},
doi = {10.1162/evco_a_00217},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2015-007/},
pdf = {BezLopStu2017assessment.pdf},
volume = 26,
number = 4,
pages = {621--656},
alias = {BezLopStu2016assessment},
abstract = {Research on multi-objective evolutionary algorithms (MOEAs)
has produced over the past decades a large number of
algorithms and a rich literature on performance assessment
tools to evaluate and compare them. Yet, newly proposed MOEAs
are typically compared against very few, often a decade older
MOEAs. One reason for this apparent contradiction is the lack
of a common baseline for comparison, with each subsequent
study often devising its own experimental scenario, slightly
different from other studies. As a result, the state of the
art in MOEAs is a disputed topic. This article reports a
systematic, comprehensive evaluation of a large number of
MOEAs that covers a wide range of experimental scenarios. A
novelty of this study is the separation between the
higher-level algorithmic components related to
multi-objective optimization (MO), which characterize each
particular MOEA, and the underlying parameters-such as
evolutionary operators, population size, etc.-whose
configuration may be tuned for each scenario. Instead of
relying on a common or "default" parameter configuration that
may be low-performing for particular MOEAs or scenarios and
unintentionally biased, we tune the parameters of each MOEA
for each scenario using automatic algorithm configuration
methods. Our results confirm some of the assumed knowledge in
the field, while at the same time they provide new insights
on the relative performance of MOEAs for many-objective
problems. For example, under certain conditions,
indicator-based MOEAs are more competitive for such problems
than previously assumed. We also analyze problem-specific
features affecting performance, the agreement between
performance metrics, and the improvement of tuned
configurations over the default configurations used in the
literature. Finally, the data produced is made publicly
available to motivate further analysis and a baseline for
future comparisons.}
}
@article{KabColKorLop2017jacryst,
author = { Kabova, Elena A. and Cole, Jason C. and Oliver Korb and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Williams, Adrian C. and Shankland, Kenneth },
title = {Improved performance of crystal structure solution from
powder diffraction data through parameter tuning of a
simulated annealing algorithm},
journal = {Journal of Applied Crystallography},
year = 2017,
volume = 50,
number = 5,
pages = {1411--1420},
month = oct,
doi = {10.1107/S1600576717012602},
abstract = {Significant gains in the performance of the simulated
annealing algorithm in the {\it DASH} software package have
been realized by using the {\it irace} automatic
configuration tool to optimize the values of three key
simulated annealing parameters. Specifically, the success
rate in finding the global minimum in intensity $\chi^2$
space is improved by up to an order of magnitude. The general
applicability of these revised simulated annealing parameters
is demonstrated using the crystal structure determinations of
over 100 powder diffraction datasets.},
keywords = {crystal structure determination, powder diffraction,
simulated annealing, parameter tuning, irace}
}
@article{DorBirLiLop2017si,
author = { Marco Dorigo and Mauro Birattari and Li, Xiaodong and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Kazuhiro Ohkura and Carlo Pinciroli and Thomas St{\"u}tzle },
title = {{ANTS} 2016 Special Issue: Editorial},
journal = {Swarm Intelligence},
year = 2017,
month = nov,
doi = {10.1007/s11721-017-0146-5}
}
@article{LopKesStu2017:cim,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Marie-El{\'e}onore Kessaci and Thomas St{\"u}tzle },
title = {Automatic Design of Hybrid Metaheuristics from Algorithmic Components},
journal = {Submitted},
year = {2017},
optvolume = {},
optnumber = {},
optpages = {}
}
@article{LopDubPerStuBir2016irace,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and P{\'e}rez C{\'a}ceres, Leslie and Thomas St{\"u}tzle and Mauro Birattari },
title = {The {\Rpackage{irace}} package: Iterated Racing for Automatic
Algorithm Configuration},
journal = {Operations Research Perspectives},
year = 2016,
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2016-003/},
doi = {10.1016/j.orp.2016.09.002},
volume = 3,
pages = {43--58}
}
@article{BezLopStu2015tec,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Component-Wise Design of Multi-Objective
Evolutionary Algorithms},
journal = {IEEE Transactions on Evolutionary Computation},
doi = {10.1109/TEVC.2015.2474158},
year = 2016,
volume = 20,
number = 3,
pages = {403--417},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2014-010/},
pdf = {BezLopStu2015tec.pdf}
}
@article{BluPinLopLoz2015cor,
author = { Christian Blum and Pedro Pinacho and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Jos{\'e} A. Lozano },
title = {Construct, Merge, Solve \& Adapt: A New General Algorithm for
Combinatorial Optimization},
journal = {Computers \& Operations Research},
year = 2016,
volume = 68,
pages = {75--88},
doi = {10.1016/j.cor.2015.10.014},
keywords = {irace}
}
@article{TriLop2015plos,
author = { Vito Trianni and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Advantages of Task-Specific Multi-Objective Optimisation in
Evolutionary Robotics},
journal = {PLoS One},
year = 2015,
volume = 10,
number = 8,
pages = {e0136406},
doi = {10.1371/journal.pone.0136406},
abstract = {The application of multi-objective optimisation to
evolutionary robotics is receiving increasing attention. A
survey of the literature reveals the different possibilities
it offers to improve the automatic design of efficient and
adaptive robotic systems, and points to the successful
demonstrations available for both task-specific and
task-agnostic approaches (i.e., with or without reference to
the specific design problem to be tackled). However, the
advantages of multi-objective approaches over
single-objective ones have not been clearly spelled out and
experimentally demonstrated. This paper fills this gap for
task-specific approaches: starting from well-known results in
multi-objective optimisation, we discuss how to tackle
commonly recognised problems in evolutionary robotics. In
particular, we show that multi-objective optimisation (i)
allows evolving a more varied set of behaviours by exploring
multiple trade-offs of the objectives to optimise, (ii)
supports the evolution of the desired behaviour through the
introduction of objectives as proxies, (iii) avoids the
premature convergence to local optima possibly introduced by
multi-component fitness functions, and (iv) solves the
bootstrap problem exploiting ancillary objectives to guide
evolution in the early phases. We present an experimental
demonstration of these benefits in three different case
studies: maze navigation in a single robot domain, flocking
in a swarm robotics context, and a strictly collaborative
task in collective robotics.}
}
@article{DubLopStu2015ejor,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Anytime {P}areto Local Search},
journal = {European Journal of Operational Research},
year = 2015,
volume = 243,
number = 2,
pages = {369--385},
doi = {10.1016/j.ejor.2014.10.062},
pdf = {DubLopStu2015ejor.pdf},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2013-003/},
alias = {DubLopStu2013cor}
}
@article{PerLopStu2015si,
author = { P{\'e}rez C{\'a}ceres, Leslie and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Ant colony optimization on a limited budget of evaluations},
journal = {Swarm Intelligence},
year = 2015,
doi = {10.1007/s11721-015-0106-x},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2015-004},
pdf = {PerLopStu2015si.pdf},
volume = 9,
number = {2-3},
pages = {103--124}
}
@article{LopStu2013ejor,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatically Improving the Anytime Behaviour of Optimisation
Algorithms},
journal = {European Journal of Operational Research},
year = 2014,
volume = 235,
number = 3,
pages = {569--582},
doi = {10.1016/j.ejor.2013.10.043},
pdf = {LopStu2014ejor.pdf},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2012-011/},
abstract = {Optimisation algorithms with good anytime behaviour try to
return as high-quality solutions as possible independently of
the computation time allowed. Designing algorithms with good
anytime behaviour is a difficult task, because performance is
often evaluated subjectively, by plotting the trade-off curve
between computation time and solution quality. Yet, the
trade-off curve may be modelled also as a set of mutually
nondominated, bi-objective points. Using this model, we
propose to combine an automatic configuration tool and the
hypervolume measure, which assigns a single quality measure
to a nondominated set. This allows us to improve the anytime
behaviour of optimisation algorithms by means of
automatically finding algorithmic configurations that produce
the best nondominated sets. Moreover, the recently proposed
weighted hypervolume measure is used here to incorporate the
decision-maker's preferences into the automatic tuning
procedure. We report on the improvements reached when
applying the proposed method to two relevant scenarios: (i)
the design of parameter variation strategies for MAX-MIN Ant
System and (ii) the tuning of the anytime behaviour of SCIP,
an open-source mixed integer programming solver with more
than 200 parameters.}
}
@article{MasLopDubStu2014cor,
author = { Franco Mascia and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and Thomas St{\"u}tzle },
title = {Grammar-based generation of stochastic local search
heuristics through automatic algorithm configuration tools},
journal = {Computers \& Operations Research},
year = 2014,
doi = {10.1016/j.cor.2014.05.020},
pdf = {MasLopDubStu2014cor.pdf},
volume = 51,
pages = {190--199}
}
@article{LopBlu2013asoc,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum and Jeffrey W. Ohlmann and Barrett W. Thomas },
title = {The Travelling Salesman Problem with Time Windows:
Adapting Algorithms from Travel-time to Makespan
Optimization},
journal = {Applied Soft Computing},
year = 2013,
volume = 13,
number = 9,
pages = {3806--3815},
doi = {10.1016/j.asoc.2013.05.009},
pdf = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2013-011.pdf}
}
@article{LopStu2012swarm,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An experimental analysis of design choices of multi-objective ant colony optimization algorithms},
journal = {Swarm Intelligence},
year = 2012,
number = 3,
volume = 6,
pages = {207--232},
doi = {10.1007/s11721-012-0070-7},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2012-006/}
}
@article{LopStu2012tec,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {The Automatic Design of Multi-Objective Ant Colony
Optimization Algorithms},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2012,
volume = 16,
number = 6,
pages = {861--875},
doi = {10.1109/TEVC.2011.2182651},
abstract = {
Multi-objective optimization problems are problems with several,
typically conflicting criteria for evaluating solutions. Without
any a priori preference information, the Pareto optimality
principle establishes a partial order among solutions, and the
output of the algorithm becomes a set of nondominated solutions
rather than a single one. Various ant colony optimization (ACO)
algorithms have been proposed in recent years for solving such
problems. These multi-objective ACO (MOACO) algorithms exhibit
different design choices for dealing with the particularities of
the multi-objective context. This paper proposes a formulation of
algorithmic components that suffices to describe most MOACO
algorithms proposed so far. This formulation also shows that
existing MOACO algorithms often share equivalent design choices
but they are described in different terms. Moreover, this
formulation is synthesized into a flexible algorithmic framework,
from which not only existing MOACO algorithms may be
instantiated, but also combinations of components that were never
studied in the literature. In this sense, this paper goes beyond
proposing a new MOACO algorithm, but it rather introduces a
family of MOACO algorithms. The flexibility of the proposed MOACO
framework facilitates the application of automatic algorithm
configuration techniques. The experimental results presented in
this paper show that the automatically configured MOACO framework
outperforms the MOACO algorithms that inspired the framework
itself. This paper is also among the first to apply automatic
algorithm configuration techniques to multi-objective algorithms.}
}
@article{LopPraPae2011ec,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Representations and Evolutionary Operators for the
Scheduling of Pump Operations in Water Distribution
Networks},
journal = {Evolutionary Computation},
year = 2011,
doi = {10.1162/EVCO_a_00035},
volume = 19,
number = 3,
pages = {429--467},
abstract = {Reducing the energy consumption of water
distribution networks has never had more
significance. The greatest energy savings can be
obtained by carefully scheduling the operations of
pumps. Schedules can be defined either implicitly,
in terms of other elements of the network such as
tank levels, or explicitly by specifying the time
during which each pump is on/off. The traditional
representation of explicit schedules is a string of
binary values with each bit representing pump on/off
status during a particular time interval. In this
paper, we formally define and analyze two new
explicit representations based on time-controlled
triggers, where the maximum number of pump switches
is established beforehand and the schedule may
contain less switches than the maximum. In these
representations, a pump schedule is divided into a
series of integers with each integer representing
the number of hours for which a pump is
active/inactive. This reduces the number of
potential schedules compared to the binary
representation, and allows the algorithm to operate
on the feasible region of the search space. We
propose evolutionary operators for these two new
representations. The new representations and their
corresponding operations are compared with the two
most-used representations in pump scheduling,
namely, binary representation and level-controlled
triggers. A detailed statistical analysis of the
results indicates which parameters have the greatest
effect on the performance of evolutionary
algorithms. The empirical results show that an
evolutionary algorithm using the proposed
representations improves over the results obtained
by a recent state-of-the-art Hybrid Genetic
Algorithm for pump scheduling using level-controlled
triggers.}
}
@article{DubLopStu2011amai,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Improving the Anytime Behavior of Two-Phase Local
Search},
journal = {Annals of Mathematics and Artificial Intelligence},
year = 2011,
volume = 61,
number = 2,
pages = {125--154},
doi = {10.1007/s10472-011-9235-0},
alias = {DubLopStu2010amai},
pdf = {DubLopStu2011amai.pdf}
}
@article{DubLopStu2011cor,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {A Hybrid {TP$+$PLS} Algorithm for Bi-objective
Flow-Shop Scheduling Problems},
journal = {Computers \& Operations Research},
year = 2011,
volume = 38,
number = 8,
pages = {1219--1236},
doi = {10.1016/j.cor.2010.10.008},
supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2010-001/},
pdf = {DubLopStu2011cor.pdf}
}
@article{LopBlu2010cor,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum },
title = {Beam-{ACO} for the travelling salesman problem with
time windows},
journal = {Computers \& Operations Research},
year = 2010,
doi = {10.1016/j.cor.2009.11.015},
volume = 37,
number = 9,
pages = {1570--1583},
keywords = {Ant colony optimization, Travelling salesman problem with
time windows, Hybridization},
alias = {LopBlu09tsptw},
abstract = {The travelling salesman problem with time windows is
a difficult optimization problem that arises, for
example, in logistics. This paper deals with the
minimization of the travel-cost. For solving this
problem, this paper proposes a Beam-ACO algorithm,
which is a hybrid method combining ant colony
optimization with beam search. In general, Beam-ACO
algorithms heavily rely on accurate and
computationally inexpensive bounding information for
differentiating between partial solutions. This work
uses stochastic sampling as a useful alternative. An
extensive experimental evaluation on seven benchmark
sets from the literature shows that the proposed
Beam-ACO algorithm is currently a state-of-the-art
technique for the travelling salesman problem with
time windows when travel-cost optimization is
concerned.}
}
@article{BeuFonLopPaqVah09:tec,
author = { Nicola Beume and Carlos M. Fonseca and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'\i}s Paquete and Jan Vahrenhold },
title = {On the complexity of computing the hypervolume
indicator},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2009,
volume = 13,
number = 5,
pages = {1075--1082},
doi = {10.1109/TEVC.2009.2015575},
abstract = {The goal of multi-objective optimization is to find
a set of best compromise solutions for typically
conflicting objectives. Due to the complex nature of
most real-life problems, only an approximation to
such an optimal set can be obtained within
reasonable (computing) time. To compare such
approximations, and thereby the performance of
multi-objective optimizers providing them, unary
quality measures are usually applied. Among these,
the \emph{hypervolume indicator} (or
\emph{S-metric}) is of particular relevance due to
its favorable properties. Moreover, this indicator
has been successfully integrated into stochastic
optimizers, such as evolutionary algorithms, where
it serves as a guidance criterion for finding good
approximations to the Pareto front. Recent results
show that computing the hypervolume indicator can be
seen as solving a specialized version of Klee's
Measure Problem. In general, Klee's Measure Problem
can be solved with $\mathcal{O}(n \log n +
n^{d/2}\log n)$ comparisons for an input instance of
size $n$ in $d$ dimensions; as of this writing, it
is unknown whether a lower bound higher than
$\Omega(n \log n)$ can be proven. In this article,
we derive a lower bound of $\Omega(n\log n)$ for the
complexity of computing the hypervolume indicator in
any number of dimensions $d>1$ by reducing the
so-called \textsc{UniformGap} problem to it. For
the three dimensional case, we also present a
matching upper bound of $\mathcal{O}(n\log n)$
comparisons that is obtained by extending an
algorithm for finding the maxima of a point set.}
}
@article{BluBleLop09-BeamSearch-LCS,
author = { Christian Blum and Mar{\'\i}a J. Blesa and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Beam search for the longest common subsequence
problem},
number = 12,
journal = {Computers \& Operations Research},
year = 2009,
pages = {3178--3186},
volume = 36,
doi = {10.1016/j.cor.2009.02.005},
pdf = {BluBleLop09-BeamSearch-LCS.pdf},
abstract = { The longest common subsequence problem is a
classical string problem that concerns finding the
common part of a set of strings. It has several
important applications, for example, pattern
recognition or computational biology. Most research
efforts up to now have focused on solving this
problem optimally. In comparison, only few works
exist dealing with heuristic approaches. In this
work we present a deterministic beam search
algorithm. The results show that our algorithm
outperforms the current state-of-the-art approaches
not only in solution quality but often also in
computation time.}
}
@article{LopPraPae08aco,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Ant Colony Optimisation for the Optimal Control of
Pumps in Water Distribution Networks},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
year = 2008,
volume = 134,
number = 4,
pages = {337--346},
publisher = {{ASCE}},
pdf = {LopezPrasadPaechter08-jwrpm.pdf},
eprint = {http://link.aip.org/link/?QWR/134/337/1},
doi = {10.1061/(ASCE)0733-9496(2008)134:4(337)},
abstract = {Reducing energy consumption of water distribution
networks has never had more significance than today. The greatest
energy savings can be obtained by careful scheduling of operation of
pumps. Schedules can be defined either implicitly, in terms of other
elements of the network such as tank levels, or explicitly by
specifying the time during which each pump is on/off. The
traditional representation of explicit schedules is a string of
binary values with each bit representing pump on/off status during a
particular time interval. In this paper a new explicit
representation is presented. It is based on time controlled
triggers, where the maximum number of pump switches is specified
beforehand. In this representation a pump schedule is divided into a
series of integers with each integer representing the number of
hours for which a pump is active/inactive. This reduces the number
of potential schedules (search space) compared to the binary
representation. Ant colony optimization (ACO) is a stochastic
meta-heuristic for combinatorial optimization problems that is
inspired by the foraging behavior of some species of ants. In this
paper, an application of the ACO framework was developed for the
optimal scheduling of pumps. The proposed representation was adapted
to an ant colony Optimization framework and solved for the optimal
pump schedules. Minimization of electrical cost was considered as
the objective, while satisfying system constraints. Instead of using
a penalty function approach for constraint violations, constraint
violations were ordered according to their importance and solutions
were ranked based on this order. The proposed approach was tested on
a small test network and on a large real-world network. Results are
compared with those obtained using a simple genetic algorithm based
on binary representation and a hybrid genetic algorithm that uses
level-based triggers.}
}
@article{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.}
}