Publications
NOTE: Below you will find
pre-print versions of my publications. If you don't have access to any of
my papers, please write to me.
Theses
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Manuel
López-Ibáñez. Operational Optimisation of Water Distribution
Networks. PhD thesis, School of Engineering and the
Built Environment, Edinburgh Napier University, UK, 2009.
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Manuel
López-Ibáñez. Multi-objective Ant Colony
Optimization. Diploma thesis, Intellectics Group,
Computer Science Department, Technische Universität Darmstadt,
Germany, 2004.
International Journals
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Manuel
López-Ibáñez, Diederick Vermetten, Johann Dreo, and
Carola Doerr. Using
the Empirical Attainment Function for Analyzing Single-objective
Black-box Optimization Algorithms. IEEE Transactions on
Evolutionary Computation, 2025.
A widely accepted way to assess the performance of iterative black-box
optimizers is to analyze their empirical cumulative distribution
function (ECDF) of pre-defined quality targets achieved not later than a
given runtime. In this work, we consider an alternative approach, based
on the empirical attainment function (EAF) and we show that the
target-based ECDF is an approximation of the EAF. We argue that the EAF
has several advantages over the target-based ECDF. In particular, it
does not require defining a priori quality targets per function,
captures performance differences more precisely, and enables the use of
additional summary statistics that enrich the analysis. We also show
that the average area over the convergence curves is a
simpler-to-calculate, but equivalent, measure of anytime performance. To
facilitate the accessibility of the EAF, we integrate a module to
compute it into the IOHanalyzer platform. Finally, we illustrate the use
of the EAF via synthetic examples and via the data available for the
BBOB suite.
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Raul
Martín-Santamaría, Manuel
López-Ibáñez, Thomas Stützle, and
J. Manuel Colmenar. On the automatic generation
of metaheuristic algorithms for combinatorial optimization
problems. European Journal of Operational Research,
2024.
Metaheuristic algorithms have become one of the preferred approaches for
solving optimization problems. Finding the best metaheuristic for a
given problem is often difficult due to the large number of available
approaches and possible algorithmic designs. Moreover, high-performing
metaheuristics often combine general-purpose and problem-specific
algorithmic components. We propose here an approach for automatically
designing metaheuristics using a flexible framework of algorithmic
components, from which algorithms are instantiated and evaluated by an
automatic configuration method. The rules for composing algorithmic
components are defined implicitly by the properties of each algorithmic
component, in contrast to previous proposals, which require a
handwritten algorithmic template or grammar. As a result, extending our
framework with additional components, even problem-specific or
user-defined ones, automatically updates the design space. Furthermore,
since the generated algorithms are made up of components, they can be
easily interpreted. We provide an implementation of our proposal and
demonstrate its benefits by outperforming previous research in three
distinct problems from completely different families: a facility layout
problem, a vehicle routing problem and a clustering problem.
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Antonio J.
Nebro, Manuel López-Ibáñez, José
García-Nieto, and Carlos A. Coello Coello. On the automatic design of
multi-objective particle swarm optimizers: experimentation and
analysis. Swarm Intelligence, 2023.
Research in multi-objective particle swarm optimizers (MOPSOs)
progresses by proposing one new MOPSO at a time. In spite of the
commonalities among different MOPSOs, it is often unclear which
algorithmic components are crucial for explaining the performance of a
particular MOPSO design. Moreover, it is expected that different designs
may perform best on different problem families and identifying a best
overall MOPSO is a challenging task. We tackle this challenge here by:
(1) proposing AutoMOPSO, a flexible algorithmic template for designing
MOPSOs with a design space that can instantiate thousands of potential
MOPSOs; and (2) searching for good-performing MOPSO designs given a
family of training problems by means of an automatic configuration tool
(irace). We apply this automatic design methodology to generate a MOPSO
that significantly outperforms two state-of-the-art MOPSOs on four
well-known bi-objective problem families. We also identify the key
design choices and parameters of the winning MOPSO by means of ablation.
AutoMOPSO is publicly available as part of the jMetal framework.
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Miqing Li, Manuel
López-Ibáñez, and Xin Yao. Multi-Objective
Archiving. IEEE Transactions on Evolutionary
Computation, 2023.
Most multi-objective optimisation algorithms maintain an archive
explicitly or implicitly during their search. Such an archive can be
solely used to store high-quality solutions presented to the decision
maker, but in many cases may participate in the search process (e.g., as
the population in evolutionary computation). Over the last two decades,
archiving, the process of comparing new solutions with previous ones and
deciding how to update the archive/population, stands as an important
issue in evolutionary multi-objective optimisation (EMO). This is
evidenced by constant efforts from the community on developing various
effective archiving methods, ranging from conventional Pareto-based
methods to more recent indicator-based and decomposition-based ones.
However, the focus of these efforts is on empirical performance
comparison in terms of specific quality indicators; there is lack of
systematic study of archiving methods from a general theoretical
perspective. In this paper, we attempt to conduct a systematic overview
of multi-objective archiving, in the hope of paving the way to
understand archiving algorithms from a holistic perspective of theory
and practice, and more importantly providing a guidance on how to design
theoretically desirable and practically useful archiving algorithms. In
doing so, we also present that archiving algorithms based on weakly
Pareto compliant indicators (e.g., ε-indicator), as long as
designed properly, can achieve the same theoretical desirables as
archivers based on Pareto compliant indicators (e.g., hypervolume
indicator). Such desirables include the property limit-optimal, the
limit form of the possible optimal property that a bounded archiving
algorithm can have with respect to the most general form of superiority
between solution sets.
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Seyed Mahdi
Shavarani, Manuel López-Ibáñez, and Joshua D.
Knowles. On
Benchmarking Interactive Evolutionary Multi-Objective Algorithms.
IEEE Transactions on Evolutionary Computation,
28(4):1084–1098, 2023.
We carry out a detailed performance assessment of two interactive
evolutionary multi-objective algorithms (EMOAs) using a machine decision
maker that enables us to repeat experiments and study specific
behaviours modeled after human decision makers (DMs). Using the same set
of benchmark test problems as in the original papers on these
interactive EMOAs (in up to 10 objectives), we bring to light
interesting effects when we use a machine DM based on sigmoidal utility
functions that have support from the psychology literature (replacing
the simpler utility functions used in the original papers). Our machine
DM enables us to go further and simulate human biases and
inconsistencies as well. Our results from this study, which is the most
comprehensive assessment of multiple interactive EMOAs so far conducted,
suggest that current well-known algorithms have shortcomings that need
addressing. These results further demonstrate the value of improving the
benchmarking of interactive EMOAs
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Atanu Mazumdar, Manuel
López-Ibáñez, Tinkle Chugh, and Kaisa Miettinen.
Treed Gaussian Process
Regression for Solving Offline Data-Driven Continuous Multiobjective
Optimization Problems. Evolutionary Computation,
2023.
For offline data-driven multiobjective optimization problems (MOPs), no
new data is available during the optimization process. Approximation
models (or surrogates) are first built using the provided offline data
and an optimizer, e.g. a multiobjective evolutionary algorithm, can then
be utilized to find Pareto optimal solutions to the problem with
surrogates as objective functions. In contrast to online data-driven
MOPs, these surrogates cannot be updated with new data and, hence, the
approximation accuracy cannot be improved by considering new data during
the optimization process. Gaussian process regression (GPR) models are
widely used as surrogates because of their ability to provide
uncertainty information. However, building GPRs becomes computationally
expensive when the size of the dataset is large. Using sparse GPRs
reduces the computational cost of building the surrogates. However,
sparse GPRs are not tailored to solve offline data-driven MOPs, where
good accuracy of the surrogates is needed near Pareto optimal solutions.
Treed GPR (TGPR-MO) surrogates for offline data-driven MOPs with
continuous decision variables are proposed in this paper. The proposed
surrogates first split the decision space into subregions using
regression trees and build GPRs sequentially in regions close to Pareto
optimal solutions in the decision space to accurately approximate
tradeoffs between the objective functions. TGPR-MO surrogates are
computationally inexpensive because GPRs are built only in a smaller
region of the decision space utilizing a subset of the data. The TGPR-MO
surrogates were tested on distance-based visualizable problems with
various data sizes, sampling strategies, numbers of objective functions,
and decision variables. Experimental results showed that the TGPR-MO
surrogates are computationally cheaper and can handle datasets of large
size. Furthermore, TGPR-MO surrogates produced solutions closer to
Pareto optimal solutions compared to full GPRs and sparse GPRs.
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Seyed Mahdi
Shavarani, Manuel López-Ibáñez, and Richard
Allmendinger. Detecting Hidden and
Irrelevant Objectives in Interactive Multi-Objective
Optimization. IEEE Transactions on Evolutionary
Computation, 28(2):544–557, 2023.
Evolutionary multi-objective optimization algorithms (EMOAs) typically
assume that all objectives that are relevant to the decision-maker (DM)
are optimized by the EMOA. In some scenarios, however, there are
irrelevant objectives that are optimized by the EMOA but ignored by the
DM, as well as, hidden objectives that the DM considers when judging the
utility of solutions but are not optimized. This discrepancy between the
EMOA and the DM's preferences may impede the search for the
most-preferred solution and waste resources evaluating irrelevant
objectives. Research on objective reduction has focused so far on the
structure of the problem and correlations between objectives and
neglected the role of the DM. We formally define here the concepts of
irrelevant and hidden objectives and propose methods for detecting them,
based on uni-variate feature selection and recursive feature
elimination, that use the preferences already elicited when a DM
interacts with a ranking-based interactive EMOA (iEMOA). We incorporate
the detection methods into an iEMOA capable of dynamically switching the
objectives being optimized. Our experiments show that this approach can
efficiently identify which objectives are relevant to the DM and reduce
the number of objectives being optimized, while keeping and often
improving the utility, according to the DM, of the best solution found.
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Laurens Bliek, Paulo
da Costa, Reza Refaei Afshar, Robbert Reijnen, Yingqian Zhang, Tom
Catshoek, Daniël Vos, Sicco Verwer, Fynn Schmitt-Ulms, André
Hottung, Tapan Shah, Meinolf Sellmann, Kevin Tierney, Carl
Perreault-Lafleur, Caroline Leboeuf, Federico Bobbio, Justine Pepin,
Warley Almeida Silva, Ricardo Gama, Hugo L. Fernandes, Martin
Zaefferer, Manuel López-Ibáñez, and Ekhine Irurozki.
The First AI4TSP
Competition: Learning to Solve Stochastic Routing Problems.
Artificial Intelligence, 319:103918, 2023.
This paper reports on the first international competition on AI for the
traveling salesman problem (TSP) at the International Joint Conference
on Artificial Intelligence 2021 (IJCAI-21). The TSP is one of the
classical combinatorial optimization problems, with many variants
inspired by real-world applications. This first competition asked the
participants to develop algorithms to solve an orienteering problem with
stochastic weights and time windows (OPSWTW). It focused on two learning
approaches: surrogate-based optimization and deep reinforcement
learning. In this paper, we describe the problem, the competition setup,
and the winning methods, and give an overview of the results. The
winning methods described in this work have advanced the
state-of-the-art in using AI for stochastic routing problems. Overall,
by organizing this competition we have introduced routing problems as an
interesting problem setting for AI researchers. The simulator of the
problem has been made open-source and can be used by other researchers
as a benchmark for new learning-based methods. The instances and code
for the competition are available at https://github.com/paulorocosta/ai-for-tsp-competition.
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Christian Cintrano,
Javier Ferrer, Manuel López-Ibáñez, and Enrique
Alba. Hybridization of
Evolutionary Operators with Elitist Iterated Racing for the Simulation
Optimization of Traffic Lights Programs. Evolutionary
Computation, 2022.
In the traffic light scheduling problem the evaluation of candidate
solutions requires the simulation of a process under various (traffic)
scenarios. Thus, good solutions should not only achieve good objective
function values, but they must be robust (low variance) across all
different scenarios. Previous work has shown that combining IRACE with
evolutionary operators is effective for this task due to the power of
evolutionary operators in numerical optimization. In this paper, we
further explore the hybridization of evolutionary operators and the
elitist iterated racing of IRACE for the simulation-optimization of
traffic light programs. We review previous works from the literature to
find the evolutionary operators performing the best when facing this
problem to propose new hybrid algorithms. We evaluate our approach over
a realistic case study derived from the traffic network of Málaga
(Spain) with 275 traffic lights that should be scheduled optimally. The
experimental analysis reveals that the hybrid algorithm comprising IRACE
plus differential evolution offers statistically better results than the
other algorithms when the budget of simulations is low. In contrast,
IRACE performs better than the hybrids for high simulations budget,
although the optimization time is much longer.
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Marcelo De Souza, Marcus
Ritt, and Manuel López-Ibáñez. Capping Methods for the
Automatic Configuration of Optimization Algorithms. Computers
& Operations Research, 139:105615, 2022.
Automatic configuration techniques are widely and successfully used to
find good parameter settings for optimization algorithms. Configuration
is costly, because it is necessary to evaluate many configurations on
different instances. For decision problems, when the objective is to
minimize the running time of the algorithm, many configurators implement
capping methods to discard poor configurations early. Such methods are
not directly applicable to optimization problems, when the objective is
to optimize the cost of the best solution found, given a predefined
running time limit. We propose new capping methods for the automatic
configuration of optimization algorithms. They use the previous
executions to determine a performance envelope, which is used to
evaluate new executions and cap those that do not satisfy the envelope
conditions. We integrate the capping methods into the irace configurator
and evaluate them on different optimization scenarios. Our results show
that the proposed methods can save from about 5% to 78% of the
configuration effort, while finding configurations of the same quality.
Based on the computational analysis, we identify two conservative and
two aggressive methods, that save an average of about 20% and 45% of the
configuration effort, respectively. We also provide evidence that
capping can help to better use the available budget in scenarios with a
configuration time limit.
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Manuel
López-Ibáñez, Jürgen Branke, and Luís
Paquete. Reproducibility in
Evolutionary Computation. ACM Transactions on Evolutionary
Learning and Optimization, 1(4):1–21, 2021.
Experimental studies are prevalent in Evolutionary Computation (EC), and
concerns about the reproducibility and replicability of such studies
have increased in recent times, reflecting similar concerns in other
scientific fields. In this article, we suggest a classification of
different types of reproducibility that refines the badge system of the
Association of Computing Machinery (ACM) adopted by TELO. We discuss,
within the context of EC, the different types of reproducibility as well
as the concepts of artifact and measurement, which are crucial for
claiming reproducibility. We identify cultural and technical obstacles
to reproducibility in the EC field. Finally, we provide guidelines and
suggest tools that may help to overcome some of these reproducibility
obstacles.
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Babooshka Shavazipour,
Manuel López-Ibáñez, and Kaisa Miettinen.
Visualizations for
Decision Support in Scenario-based Multiobjective Optimization.
Information Sciences, 578:1–21, 2021.
We address challenges of decision problems when managers need to
optimize several conflicting objectives simultaneously under
uncertainty. We propose visualization tools to support the solution of
such scenario-based multiobjective optimization problems. Suitable
graphical visualizations are necessary to support managers in
understanding, evaluating, and comparing the performances of management
decisions according to all objectives in all plausible scenarios. To
date, no appropriate visualization has been suggested. This paper fills
this gap by proposing two visualization methods: a novel extension of
empirical attainment functions for scenarios and an adapted version of
heatmaps. They help a decision-maker in gaining insight into
realizations of trade-offs and comparisons between objective functions
in different scenarios. Some fundamental questions that a decision-maker
may wish to answer with the help of visualizations are also identified.
Several examples are utilized to illustrate how the proposed
visualizations support a decision-maker in evaluating and comparing
solutions to be able to make a robust decision by answering the
questions. Finally, we validate the usefulness of the proposed
visualizations in a real-world problem with a real decision-maker. We
conclude with guidelines regarding which of the proposed visualizations
are best suited for different problem classes.
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Marcelo De Souza,
Marcus Ritt, Manuel López-Ibáñez, and Leslie
Pérez Cáceres. ACVIZ: A Tool for the Visual
Analysis of the Configuration of Algorithms with irace.
Operations Research Perspectives, 8:100186, 2021.
This paper introduces acviz, a tool that helps to analyze the automatic
configuration of algorithms with irace. It provides a visual
representation of the configuration process, allowing users to extract
useful information, e.g. how the configurations evolve over time. When
test data is available, acviz also shows the performance of each
configuration on the test instances. Using this visualization, users can
analyze and compare the quality of the resulting configurations and
observe the performance differences on training and test instances.
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Juan Esteban Diaz and
Manuel López-Ibáñez. Incorporating
Decision-Maker's Preferences into the Automatic Configuration of
Bi-Objective Optimisation Algorithms. European Journal of
Operational Research, 289(3):1209–1222, 2021.
★
Editor's Choice Article
Automatic configuration (AC) methods are increasingly used to tune and
design optimisation algorithms for problems with multiple objectives.
Most AC methods use unary quality indicators, which assign a single
scalar value to an approximation to the Pareto front, to compare the
performance of different optimisers. These quality indicators, however,
imply preferences beyond Pareto-optimality that may differ from those of
the decision maker (DM). Although it is possible to incorporate DM's
preferences into quality indicators, e.g., by means of the weighted
hypervolume indicator (HVw), expressing preferences in terms
of weight function is not always intuitive nor an easy task for a DM, in
particular, when comparing the stochastic outcomes of several algorithm
configurations. A more visual approach to compare such outcomes is the
visualisation of their empirical attainment functions (EAFs)
differences. This paper proposes using such visualisations as a way of
eliciting information about regions of the objective space that are
preferred by the DM. We present a method to convert the information
about EAF differences into a HVw that will assign higher
quality values to approximation fronts that result in EAF differences
preferred by the DM. We show that the resulting HVw may be
used by an AC method to guide the configuration of multi-objective
optimisers according to the preferences of the DM. We evaluate the
proposed approach on a well-known benchmark problem. Finally, we apply
our approach to re-configuring, according to different DM's preferences,
a multi-objective optimiser tackling a real-world production planning
problem arising in the manufacturing industry.
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Patrycja Strycharczuk,
Manuel López-Ibáñez, Georgina Brown, and Adrian
Leemann. General
Northern English: Exploring regional variation in the North of England
with machine learning. Frontiers in Artificial
Intelligence, 2020.
In this paper, we present a novel computational approach to the analysis
of accent variation. The case study is dialect leveling in the North of
England, manifested as reduction of accent variation across the North
and emergence of General Northern English (GNE), a pan-regional standard
accent associated with middle-class speakers. We investigated this
instance of dialect leveling using random forest classification, with
audio data from a crowd-sourced corpus of 105 urban, mostly
highly-educated speakers from five northern UK cities: Leeds, Liverpool,
Manchester, Newcastle upon Tyne, and Sheffield. We trained random forest
models to identify individual northern cities from a sample of other
northern accents, based on first two formant measurements of full vowel
systems. We tested the models using unseen data. We relied on
undersampling, bagging (bootstrap aggregation) and leave-one-out
cross-validation to address some challenges associated with the data
set, such as unbalanced data and relatively small sample size. The
accuracy of classification provides us with a measure of relative
similarity between different pairs of cities, while calculating
conditional feature importance allows us to identify which input
features (which vowels and which formants) have the largest influence in
the prediction. We do find a considerable degree of leveling, especially
between Manchester, Leeds and Sheffield, although some differences
persist. The features that contribute to these differences most
systematically are typically not the ones discussed in previous dialect
descriptions. We propose that the most systematic regional features are
also not salient, and as such, they serve as sociolinguistic regional
indicators. We supplement the random forest results with a more
traditional variationist description of by-city vowel systems, and we
use both sources of evidence to inform a description of the vowels of
General Northern English.
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Leonardo C. T.
Bezerra, Manuel López-Ibáñez, and Thomas
Stützle. Automatically Designing
State-of-the-Art Multi- and Many-Objective Evolutionary
Algorithms. Evolutionary Computation,
28(2):195–226, 2020.
A recent comparison of well-established multiobjective evolutionary
algorithms (MOEAs) has helped better identify the current
state-of-the-art by considering (i) parameter tuning through automatic
configuration, (ii) a wide range of different setups, and (iii) various
performance metrics. Here, we automatically devise MOEAs with verified
state-of-the-art performance for multi- and many-objective continuous
optimization. Our work is based on two main considerations. The first is
that high-performing algorithms can be obtained from a configurable
algorithmic framework in an automated way. The second is that multiple
performance metrics may be required to guide this automatic design
process. In the first part of this work, we extend our previously
proposed algorithmic framework, increasing the number of MOEAs,
underlying evolutionary algorithms, and search paradigms that it
comprises. These components can be combined following a general MOEA
template, and an automatic configuration method is used to instantiate
high-performing MOEA designs that optimize a given performance metric
and present state-of-the-art performance. In the second part, we propose
a multiobjective formulation for the automatic MOEA design, which proves
critical for the context of many-objective optimization due to the
disagreement of established performance metrics. Our proposed
formulation leads to an automatically designed MOEA that presents
state-of-the-art performance according to a set of metrics, rather than
a single one.
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Jennifer Bealt,
Duncan Shaw, Chris M. Smith, and Manuel
López-Ibáñez. Peer Reviews for Making Cities
Resilient: A Systematic Literature Review. International
Journal of Emergency Management, 15(4):334–359, 2019.
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Javier Ferrer, Manuel
López-Ibáñez, and Enrique Alba. Reliable
simulation-optimization of traffic lights in a real-world city.
Applied Soft Computing, 78:697–711, 2019.
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Simon Wessing and Manuel
López-Ibáñez. Latin Hypercube Designs with
Branching and Nested Factors for Initialization of Automatic Algorithm
Configuration. Evolutionary Computation,
27(1):129–145, 2018.
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Leonardo
C. T. Bezerra, Manuel López-Ibáñez, and Thomas
Stützle. A
Large-Scale Experimental Evaluation of High-Performing Multi- and
Many-Objective Evolutionary Algorithms. Evolutionary
Computation, 26(4):621–656, 2018.
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Elena A.
Kabova, Jason C. Cole, Oliver Korb, Manuel
López-Ibáñez, Adrian C. Williams, and Kenneth
Shankland. Improved
performance of crystal structure solution from powder diffraction data
through parameter tuning of a simulated annealing algorithm.
Journal of Applied Crystallography, 50(5):1411–1420,
2017.
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Manuel
López-Ibáñez, Jérémie Dubois-Lacoste,
Leslie Pérez Cáceres, Thomas Stützle, and Mauro
Birattari. The
irace package: Iterated Racing for Automatic Algorithm
Configuration. Operations Research Perspectives,
3:43–58, 2016.
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Leonardo C. T.
Bezerra, Manuel López-Ibáñez, and Thomas
Stützle. Automatic Component-Wise
Design of Multi-Objective Evolutionary Algorithms. IEEE
Transactions on Evolutionary Computation, 20(3):403–417,
2016.
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Christian Blum, Pedro
Pinacho, Manuel López-Ibáñez, and
José A. Lozano. Construct, Merge, Solve &
Adapt: A New General Algorithm for Combinatorial Optimization.
Computers & Operations Research, 68:75–88, 2016.
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Vito Trianni and Manuel
López-Ibáñez. Advantages of Task-Specific
Multi-Objective Optimisation in Evolutionary Robotics. PLoS
One, 10(8):e0136406, 2015.
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Leslie Pérez
Cáceres, Manuel López-Ibáñez, and Thomas
Stützle. Ant
colony optimization on a limited budget of evaluations. Swarm
Intelligence, 9(2-3):103-124, 2015.
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Jérémie
Dubois-Lacoste, Manuel López-Ibáñez, and Thomas
Stützle. Anytime Pareto Local
Search. European Journal of Operational Research,
243(2):369–385, 2015.
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Franco Mascia, Manuel
López-Ibáñez, Jérémie Dubois-Lacoste,
and Thomas Stützle. Grammar-based generation of
stochastic local search heuristics through automatic algorithm
configuration tools. Computers & Operations
Research, 51:190–199, 2014.
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Manuel
López-Ibáñez and Thomas Stützle. Automatically Improving the
Anytime Behaviour of Optimisation Algorithms. European
Journal of Operational Research, 235(3):569–582, 2014.
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Manuel
López-Ibáñez, Christian Blum, Jeffrey W.
Ohlmann, and Barrett W. Thomas. The Travelling Salesman
Problem with Time Windows: Adapting Algorithms from Travel-time to
Makespan Optimization. Applied Soft Computing,
13(9):3806–3815, 2013.
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Manuel
López-Ibáñez and Thomas Stützle. An experimental analysis of
design choices of multi-objective ant colony optimization
algorithms. Swarm Intelligence, 6(3):207–232,
2012.
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Manuel
López-Ibáñez and Thomas Stützle. The Automatic Design of
Multi-Objective Ant Colony Optimization Algorithms. IEEE
Transactions on Evolutionary Computation, 16(6):861–875,
2012.
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Jérémie
Dubois-Lacoste, Manuel López-Ibáñez, and Thomas
Stützle. Improving the Anytime Behavior
of Two-Phase Local Search. Annals of Mathematics and
Artificial Intelligence, 61(2):125–154, 2011.
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Manuel
López-Ibáñez, T. Devi Prasad, and Ben Paechter.
Representations and
Evolutionary Operators for the Scheduling of Pump Operations in Water
Distribution Networks. Evolutionary Computation,
19(3):429–467, 2011.
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Jérémie
Dubois-Lacoste, Manuel López-Ibáñez, and Thomas
Stützle. A
Hybrid TP+PLS Algorithm for Bi-objective Flow-Shop Scheduling
Problems. Computers & Operations Research,
38(8):1219–1236, 2011.
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Manuel
López-Ibáñez and Christian Blum. Beam-ACO for the travelling
salesman problem with time windows. Computers &
Operations Research, 37(9):1570–1583, 2010.
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Nicola Beume,
Carlos M. Fonseca, Manuel López-Ibáñez,
Luís Paquete, and Jan Vahrenhold. On the complexity of computing
the hypervolume indicator. IEEE Transactions on Evolutionary
Computation, 13(5):1075–1082, 2009.
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Christian Blum,
María J. Blesa, and Manuel López-Ibáñez.
Beam search for the
longest common subsequence problem. Computers &
Operations Research, 36(12):3178–3186, 2009.
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Manuel
López-Ibáñez, T. Devi Prasad, and Ben Paechter.
Ant
Colony Optimisation for the Optimal Control of Pumps in Water
Distribution Networks. Journal of Water Resources Planning
and Management, ASCE, 134(4):337–346, 2008.
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Manuel
López-Ibáñez, Luís Paquete, and Thomas
Stützle. Hybrid Population-based
Algorithms for the Bi-objective Quadratic Assignment Problem.
Journal of Mathematical Modelling and Algorithms,
5(1):111–137, 2006.
Book Chapters
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Leonardo C. T.
Bezerra, Manuel López-Ibáñez, and Thomas
Stützle. Automatic Configuration of
Multi-objective Optimizers and Multi-objective Configuration. In
T. Bartz-Beielstein, B. Filipič, P. Korošec,
and E.-G. Talbi, editors, High-Performance Simulation-Based
Optimization, pages 69–92. Springer International Publishing,
Cham, Switzerland, 2020.
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Thomas Stützle and
Manuel López-Ibáñez. Automated Design of
Metaheuristic Algorithms. In M. Gendreau and J.-Y. Potvin,
editors, Handbook of Metaheuristics, volume 272 of
International Series in Operations Research & Management
Science, pages 541–579. Springer, 2019.
-
Manuel
López-Ibáñez, Thomas Stützle, and Marco Dorigo.
Ant Colony
Optimization: A Component-Wise Overview. In R. Martí,
P. M. Pardalos, and M. G. C. Resende, editors,
Handbook of Heuristics, pages 1–37. Springer International
Publishing, 2017.
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Jérémie
Dubois-Lacoste, Manuel López-Ibáñez, and Thomas
Stützle. Combining Two Search
Paradigms for Multi-objective Optimization: Two-Phase and Pareto Local
Search. In E.-G. Talbi, editor, Hybrid Metaheuristics,
volume 434 of Studies in Computational Intelligence, pages
97–117. Springer Verlag, 2013.
-
Thomas Stützle,
Manuel López-Ibáñez, Paola Pellegrini, Michael Maur,
Marco A. Montes de Oca, Mauro Birattari, and Marco Dorigo.
Parameter
Adaptation in Ant Colony Optimization. In Y. Hamadi,
E. Monfroy, and F. Saubion, editors, Autonomous
Search, pages 191–215. Springer, Berlin, Germany, 2012.
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Christian Blum and Manuel
López-Ibáñez. Ant Colony Optimization. In
The Industrial Electronics Handbook: Intelligent Systems. CRC
Press, second edition, 2011.
-
Thomas Stützle,
Manuel López-Ibáñez, and Marco Dorigo. A Concise Overview of
Applications of Ant Colony Optimization. In J. J. Cochran,
editor, Wiley Encyclopedia of Operations Research and Management
Science, volume 2, pages 896–911. John Wiley & Sons,
2011.
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Manuel
López-Ibáñez, Luís Paquete, and Thomas
Stützle. Exploratory Analysis of
Stochastic Local Search Algorithms in Biobjective Optimization.
In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and
M. Preuss, editors, Experimental Methods for the Analysis of
Optimization Algorithms, pages 209–222. Springer, Berlin,
Germany, 2010.
-
Luís
Paquete, Thomas Stützle, and Manuel
López-Ibáñez. Using experimental design
to analyze stochastic local search algorithms for multiobjective
problems. In K. F. Doerner, M. Gendreau,
P. Greistorfer, W. J. Gutjahr, R. F. Hartl, and
M. Reimann, editors, Metaheuristics: Progress in Complex Systems
Optimization, volume 39 of Operations Research / Computer
Science Interfaces, pages 325–344. Springer, New York, NY,
2007.
Edited Books
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M. Dorigo, H. Hamann,
M. López-Ibáñez, J. García-Nieto,
A. Engelbrecht, C. Pinciroli, V. Strobel, and C. L.
Camacho-Villalón, editors. Swarm Intelligence, 13th
International Conference, ANTS 2022, Málaga, Spain, November 2-4,
2022, Proceedings, volume 13491 of Lecture Notes in Computer
Science. Springer, Cham, Switzerland, 2022.
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M. López-Ibáñez,
A. Auger, and T. Stützle, editors. Genetic and
Evolutionary Computation Conference Companion, GECCO 2019, Prague, Czech
Republic, July 13-17, 2019. ACM Press, New York, NY, 2019.
-
M. López-Ibáñez,
A. Auger, and T. Stützle, editors. Proceedings of
the Genetic and Evolutionary Computation Conference, GECCO 2019, Prague,
Czech Republic, July 13-17, 2019. ACM Press, New York, NY,
2019.
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A. Liefooghe and
M. López-Ibáñez, editors. Evolutionary
Computation in Combinatorial Optimization – 18th European
Conference, EvoCOP 2018, Parma, Italy, April 4-6, 2018,
Proceedings, volume 10782 of Lecture Notes in Computer
Science. Springer, 2018.
-
B. Hu and
M. López-Ibáñez, editors. Evolutionary
Computation in Combinatorial Optimization – 17th European
Conference, EvoCOP 2017, Amsterdam, The Netherlands, April 19-21, 2017,
Proceedings, volume 10197 of Lecture Notes in Computer
Science. Springer, 2017.
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M. Dorigo,
M. Birattari, X. Li, M. López-Ibáñez,
K. Ohkura, C. Pinciroli, and T. Stützle, editors.
Swarm Intelligence, 10th International Conference, ANTS 2016,
Brussels, Belgium, September 7-9, 2016, Proceedings, volume 9882
of Lecture Notes in Computer Science. Springer, 2016.
-
J. Handl, E. Hart,
P. R. Lewis, M. López-Ibáñez,
G. Ochoa, and B. Paechter, editors. Parallel Problem
Solving from Nature - PPSN XIV 14th International Conference, Edinburgh,
UK, September 17-21, 2016, Proceedings, volume 9921 of
Lecture Notes in Computer Science. Springer, 2016.
Conference Papers
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Stefan Pricopie, Richard
Allmendinger, Manuel López-Ibáñez, Clyde Fare, Matt
Benatan, and Joshua D. Knowles. An Adaptive Approach to
Bayesian Optimization with Setup Switching Costs. In
M. Affenzeller, S. M. Winkler, A. V. Kononova,
H. Trautmann, T. Tušar, P. Machado, and
T. Bäck, editors, Parallel Problem Solving from Nature
– PPSN XVIII, volume 15149 of Lecture Notes in Computer
Science, pp. 340–355. Springer, Cham, Switzerland,
2024.
Black-box optimization methods typically assume that evaluations of the
black-box objective function are equally costly to evaluate. We
investigate here a resource-constrained setting where changes to certain
decision variables of the search space incur a higher switching cost,
e.g., due to expensive changes to the experimental setup. In this
scenario, there is a trade-off between fixing the values of those costly
variables or accepting this additional cost to explore more of the
search space. We adapt two process-constrained batch algorithms to this
sequential problem formulation, and propose two new methods: one one
cost-aware and one cost-ignorant. We validate and compare the algorithms
using a set of 7 scalable test functions with different switching-cost
settings. Our proposed cost-aware parameter-free algorithm yields
comparable results to tuned process-constrained algorithms in all
settings we considered, suggesting some degree of robustness to varying
landscape features and cost trade-offs. This method starts to outperform
the other algorithms with increasing switching cost. Our work expands on
other recent Bayesian Optimization studies in resource-constrained
settings that consider a batch setting only. Although the contributions
of this work are relevant to the general class of resource-constrained
problems, they are particularly relevant to problems where adaptability
to varying resource availability is of high importance.
-
Shuaiqun Pan,
Diederick Vermetten, Manuel López-Ibáñez, Thomas
Bäck, and Hao Wang. Transfer Learning of Surrogate
Models via Domain Affine Transformation. In J. Handl and
X. Li, editors, Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2024. ACM Press, New York, NY,
2024.
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Arnaud Liefooghe and Manuel
López-Ibáñez. Many-objective (Combinatorial)
Optimization is Easy. In S. Silva and L. Paquete,
editors, Proceedings of the Genetic and Evolutionary Computation
Conference, GECCO 2023, pp. 704–712. ACM Press, New
York, NY, 2023.
It is a common held assumption that problems with many objectives are
harder to optimize than problems with two or three objectives. In this
paper, we challenge this assumption and provide empirical evidence that
increasing the number of objectives tends to reduce the difficulty of
the landscape being optimized. Of course, increasing the number of
objectives brings about other challenges, such as an increase in the
computational effort of many operations, or the memory requirements for
storing non-dominated solutions. More precisely, we consider a broad
range of multi- and many-objective combinatorial benchmark problems, and
we measure how the number of objectives impacts the dominance relation
among solutions, the connectedness of the Pareto set, and the landscape
multimodality in terms of local optimal solutions and sets. Our analysis
shows the limit behavior of various landscape features when adding more
objectives to a problem. Our conclusions do not contradict previous
observations about the inability of Pareto-optimality to drive search,
but we explain these observations from a different perspective. Our
findings have important implications for the design and analysis of
many-objective optimization algorithms.
-
Daniel Doblas,
Antonio J. Nebro, Manuel López-Ibáñez,
José García-Nieto, and Carlos A. Coello Coello.
Automatic Design
of Multi-objective Particle Swarm Optimizers. In M. Dorigo,
, M. López-Ibáñez, J. García-Nieto,
A. Engelbrecht, C. Pinciroli, V. Strobel, and C. L.
Camacho-Villalón, editors, Swarm Intelligence, 13th
International Conference, ANTS 2022, volume 13491 of Lecture
Notes in Computer Science, pp. 28–40. Springer, Cham,
Switzerland, 2022.
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Risto Trajanov, Ana
Nikolikj, Gjorgjina Cenikj, Fabien Teytaud, Mathurin Videau, Olivier
Teytaud, Tome Eftimov, Manuel López-Ibáñez, and
Carola Doerr. Improving Nevergrad's
Algorithm Selection Wizard NGOpt Through Automated Algorithm
Configuration. In G. Rudolph, A. V. Kononova,
H. E. Aguirre, P. Kerschke, G. Ochoa, and
T. Tušar, editors, Parallel Problem Solving from Nature -
PPSN XVII, volume 13398 of Lecture Notes in Computer
Science, pp. 18–31. Springer, Cham, Switzerland,
2022.
Algorithm selection wizards are effective and versatile tools that
automatically select an optimization algorithm given high-level
information about the problem and available computational resources,
such as number and type of decision variables, maximal number of
evaluations, possibility to parallelize evaluations, etc.
State-of-the-art algorithm selection wizards are complex and difficult
to improve. We propose in this work the use of automated configuration
methods for improving their performance by finding better configurations
of the algorithms that compose them. In particular, we use elitist
iterated racing (irace) to find CMA configurations for specific
artificial benchmarks that replace the hand-crafted CMA configurations
currently used in the NGOpt wizard provided by the Nevergrad platform.
We discuss in detail the setup of irace for the purpose of generating
configurations that work well over the diverse set of problem instances
within each benchmark. Our approach improves the performance of the
NGOpt wizard, even on benchmark suites that were not part of the tuning
by irace.
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Mayowa Ayodele, Richard
Allmendinger, Manuel López-Ibáñez, and Matthieu
Parizy. Multi-Objective QUBO Solver:
Bi-Objective Quadratic Assignment Problem. In J. E.
Fieldsend and M. Wagner, editors, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2022, pages
467–475. ACM Press, New York, NY, 2022.
Quantum and quantum-inspired optimisation algorithms are designed to
solve problems represented in binary, quadratic and unconstrained form.
Combinatorial optimisation problems are therefore often formulated as
Quadratic Unconstrained Binary Optimisation Problems (QUBO) to solve
them with these algorithms. Moreover, these QUBO solvers are often
implemented using specialised hardware to achieve enormous speedups,
e.g. Fujitsu's Digital Annealer (DA) and D-Wave's Quantum Annealer.
However, these are single-objective solvers, while many real-world
problems feature multiple conflicting objectives. Thus, a common
practice when using these QUBO solvers is to scalarise such
multi-objective problems into a sequence of single-objective problems.
Due to design trade-offs of these solvers, formulating each
scalarisation may require more time than finding a local optimum. We
present the first attempt to extend the algorithm supporting a
commercial QUBO solver as a multi-objective solver that is not based on
scalarisation. The proposed multi-objective DA algorithm is validated on
the bi-objective Quadratic Assignment Problem. We observe that algorithm
performance significantly depends on the archiving strategy adopted, and
that combining DA with non-scalarisation methods to optimise multiple
objectives outperforms the current scalarised version of the DA in terms
of final solution quality.
-
Diederick
Vermetten, Hao Wang, Manuel López-Ibáñez, Carola
Doerr, and Thomas Bäck. Analyzing the Impact of
Undersampling on the Benchmarking and Configuration of Evolutionary
Algorithms. In J. E. Fieldsend and M. Wagner, editors,
Proceedings of the Genetic and Evolutionary Computation Conference,
GECCO 2022, pages 867–875. ACM Press, New York, NY, 2022.
The stochastic nature of iterative optimization heuristics leads to
inherently noisy performance measurements. Since these measurements are
often gathered once and then used repeatedly, the number of collected
samples will have a significant impact on the reliability of algorithm
comparisons. We show that care should be taken when making decisions
based on limited data. Particularly, we show that the number of runs
used in many benchmarking studies, e.g., the default value of 15
suggested by the COCO environment, can be insufficient to reliably rank
algorithms on well-known numerical optimization benchmarks.Additionally,
methods for automated algorithm configuration are sensitive to
insufficient sample sizes. This may result in the configurator choosing
a "lucky" but poor-performing configuration despite exploring better
ones. We show that relying on mean performance values, as many
configurators do, can require a large number of runs to provide accurate
comparisons between the considered configurations. Common statistical
tests can greatly improve the situation in most cases but not always. We
show examples of performance losses of more than 20%, even when using
statistical races to dynamically adjust the number of runs, as done by
irace. Our results underline the importance of appropriately considering
the statistical distribution of performance values.
-
Youngmin Kim, Richard
Allmendinger, and Manuel López-Ibáñez. Are Evolutionary Algorithms Safe
Optimizers? In J. E. Fieldsend and M. Wagner, editors,
Proceedings of the Genetic and Evolutionary Computation
Conference, pages 814–822. ACM Press, New York, NY, 2022.
We consider a type of constrained optimization problem, where the
violation of a constraint leads to an irrevocable loss, such as breakage
of a valuable experimental resource/platform or loss of human life. Such
problems are referred to as safe optimization problems (SafeOPs). While
SafeOPs have received attention in the machine learning community in
recent years, there was little interest in the evolutionary computation
(EC) community despite some early attempts between 2009 and 2011.
Moreover, there is a lack of acceptable guidelines on how to benchmark
different algorithms for SafeOPs, an area where the EC community has
significant experience in. Driven by the need for more eficient
algorithms and benchmark guidelines for SafeOPs, the objective of this
paper is to reignite the interest of the EC community in this problem
class. To achieve this we (i) provide a formal definition of SafeOPs and
contrast it to other types of optimization problems that the EC
community is familiar with, (ii) investigate the impact of key SafeOP
parameters on the performance of selected safe optimization algorithms,
(iii) benchmark EC against state-of-the-art safe optimization algorithms
from the machine learning community, and (iv) provide an open-source
Python framework to replicate and extend our work.
-
Manuel
López-Ibáñez, Francisco Chicano, and Rodrigo
Gil-Merino. The
Asteroid Routing Problem: A Benchmark for Expensive Black-Box Permutation
Optimization. In J. L. Jiménez Laredo
et al., editors, Applications of Evolutionary Computation,
volume 13224 of Lecture Notes in Computer Science. Springer
Nature, Switzerland, 2022.
Inspired by the recent 11th Global Trajectory Optimisation Competition,
this paper presents the asteroid routing problem (ARP) as a realistic
benchmark of algorithms for expensive bound-constrained black-box
optimization in permutation space. Given a set of asteroids' orbits and
a departure epoch, the goal of the ARP is to find the optimal sequence
for visiting the asteroids, starting from Earth's orbit, in order to
minimize both the cost, measured as the sum of the magnitude of velocity
changes required to complete the trip, and the time, measured as the
time elapsed from the departure epoch until visiting the last asteroid.
We provide open-source code for generating instances of arbitrary sizes
and evaluating solutions to the problem. As a preliminary analysis, we
compare the results of two methods for expensive black-box optimization
in permutation spaces, namely, Combinatorial Efficient Global
Optimization (CEGO), a Bayesian optimizer based on Gaussian processes,
and Unbalanced Mallows Model (UMM), an estimation-of-distribution
algorithm based on probabilistic Mallows models. We investigate the best
permutation representation for each algorithm, either rank-based or
order-based. Moreover, we analyze the effect of providing a good initial
solution, generated by a greedy nearest neighbor heuristic, on the
performance of the algorithms. The results suggest directions for
improvements in the algorithms being compared.
-
Ekhine Irurozki and Manuel
López-Ibáñez. Unbalanced Mallows Models for
Optimizing Expensive Black-Box Permutation Problems. In
Proceedings of the Genetic and Evolutionary Computation Conference,
GECCO 2021. ACM Press, New York, NY, 2021.
Expensive black-box combinatorial optimization problems arise in
practice when the objective function is evaluated by means of a
simulator or a real-world experiment. Since each fitness evaluation is
expensive in terms of time or resources, only a limited number of
evaluations is possible, typically several orders of magnitude smaller
than in non-expensive problems. In this scenario, classical optimization
methods such as mixed-integer programming and local search are not
useful. In the continuous case, Bayesian optimization, in particular
using Gaussian processes, has proven very effective under these
conditions. Much less research is available in the combinatorial case.
In this paper, we propose and analyze UMM, an estimation-of-distribution
(EDA) algorithm based on a Mallows probabilistic model and unbalanced
rank aggregation (uBorda). Experimental results on black-box versions of
LOP and PFSP show that UMM is able to match, and sometimes surpass, the
solutions obtained by CEGO, a Bayesian optimization algorithm for
combinatorial optimization. Moreover, the computational complexity of
UMM increases linearly with both the number of function evaluations and
the permutation size.
-
Seyed Mahdi
Shavarani, Manuel López-Ibáñez, and Joshua D.
Knowles. Realistic
Utility Functions Prove Difficult for State-of-the-Art Interactive
Multiobjective Optimization Algorithms. In F. Chicano and
K. Krawiec, editors, Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2021, pages 457–465. ACM Press,
New York, NY, 2021.
-
Tinkle Chugh and Manuel
López-Ibáñez. Maximising Hypervolume and
Minimising ε-Indicators using Bayesian Optimisation over
Sets. In F. Chicano and K. Krawiec, editors,
GECCO'21 Companion. ACM Press, New York, NY, 2021.
-
Andreea Avramescu,
Richard Allmendinger, and Manuel López-Ibáñez.
A
Multi-Objective Multi-Type Facility Location Problem for the Delivery of
Personalised Medicine. In P. Castillo and J. L. Jiménez
Laredo, editors, Applications of Evolutionary Computation,
volume 12694 of Lecture Notes in Computer Science, pages
388–403. Springer, Cham, Switzerland, 2021.
Advances in personalised medicine targeting specific sub-populations and
individuals pose a challenge to the traditional pharmaceutical industry.
With a higher level of personalisation, an already critical supply chain
is facing additional demands added by the very sensitive nature of its
products. Nevertheless, studies concerned with the efficient development
and delivery of these products are scarce. Thus, this paper presents the
case of personalised medicine and the challenges imposed by its mass
delivery. We propose a multi-objective mathematical model for the
location-allocation problem with two interdependent facility types in
the case of personalised medicine products. We show its practical
application through a cell and gene therapy case study. A
multi-objective genetic algorithm with a novel population initialisation
procedure is used as solution method.
-
Christian Cintrano,
Javier Ferrer, Manuel López-Ibáñez, and Enrique
Alba. Hybridization of Racing
Methods with Evolutionary Operators for Simulation Optimization of
Traffic Lights Programs. In C. Zarges and S. Verel,
editors, Proceedings of EvoCOP 2021 – 21th European Conference
on Evolutionary Computation in Combinatorial Optimization, volume
12692 of Lecture Notes in Computer Science, pages 17–33.
Springer, Cham, Switzerland, 2021.
★ Best paper award
In many real-world optimization problems, like the traffic light
scheduling problem tackled here, the evaluation of candidate solu- tions
requires the simulation of a process under various scenarios. Thus, good
solutions should not only achieve good objective function values, but
they must be robust (low variance) across all different scenarios.
Previous work has revealed the effectiveness of IRACE for this task.
However, the operators used by IRACE to generate new solutions were
designed for configuring algorithmic parameters, that have various data
types (categorical, numerical, etc.). Meanwhile, evolutionary algorithms
have powerful operators for numerical optimization, which could help to
sample new solutions from the best ones found in the search. Therefore,
in this work, we propose a hybridization of the elitist iterated racing
mechanism of IRACE with evolutionary operators from differential evo-
lution and genetic algorithms. We consider a realistic case study
derived from the traffic network of Malaga (Spain) with 275 traffic
lights that should be scheduled optimally. After a meticulous study, we
discovered that the hybrid algorithm comprising IRACE plus differential
evolution offers statistically better results than conventional
algorithms and also improves travel times and reduces pollution.
-
Youngmin Kim, Richard
Allmendinger, and Manuel López-Ibáñez. Safe Learning and
Optimization Techniques: Towards a Survey of the State of the
Art. In F. Heintz, M. Milano, and B. O'Sullivan,
editors, Trustworthy AI – Integrating Learning, Optimization
and Reasoning. TAILOR 2020, volume 12641 of Lecture Notes in
Computer Science, pages 123–139. Springer, Cham, Switzerland,
2020.
Safe learning and optimization deals with learning and optimization
problems that avoid, as much as possible, the evaluation of non-safe
input points, which are solutions, policies, or strategies that cause an
irrecoverable loss (e.g., breakage of a machine or equipment, or life
threat). Although a comprehensive survey of safe reinforcement learning
algorithms was published in 2015, a number
of new algorithms have been proposed thereafter, and related works in
active learning and in optimization were not considered. This paper
reviews those algorithms from a number of domains including
reinforcement learning, Gaussian process regression and classification,
evolutionary computing, and active learning. We provide the fundamental
concepts on which the reviewed algorithms are based and a
characterization of the individual algorithms. We conclude by explaining
how the algorithms are connected and suggestions for future
research.
-
Maura Hunt and Manuel
López-Ibáñez. Modeling a Decision-Maker in Goal
Programming by means of Computational Rationality. In
I. Palomares, editor, International Alan Turing Conference on
Decision Support and Recommender systems, pages 17–20, London,
UK, November, 21–22 2019. Alan Turing Institute.
-
Antonio J.
Nebro, Manuel López-Ibáñez, Cristóbal
Barba-González, and José García-Nieto. Automatic Configuration of
NSGA-II with jMetal and irace. In
M. López-Ibáñez and A. Auger, editors,
GECCO'19 Companion. ACM Press, New York, NY, 2019.
-
Bhupinder Singh
Saini, Manuel López-Ibáñez, and Kaisa Miettinen.
Automatic Surrogate
Modelling Technique Selection based on Features of Optimization
Problems. In M. López-Ibáñez and
A. Auger, editors, GECCO'19 Companion. ACM Press, New York,
NY, 2019.
A typical scenario when solving industrial single or multiobjective
optimization problems is that no explicit formulation of the problem is
available. Instead, a dataset containing vectors of decision variables
together with their objective function value(s) is given and a surrogate
model (or metamodel) is build from the data and used for optimization
and decision-making. This data-driven optimization process strongly
depends on the ability of the surrogate model to predict the objective
value of decision variables not present in the original dataset.
Therefore, the choice of surrogate modelling technique is crucial. While
many surrogate modelling techniques have been discussed in the
literature, there is no standard procedure that will select the best
technique for a given problem. In this work, we propose the automatic
selection of a surrogate modelling technique based on exploratory
landscape features of the optimization problem that underlies the given
dataset. The overall idea is to learn offline from a large pool of
benchmark problems, on which we can evaluate a large number of surrogate
modelling techniques. When given a new dataset, features are used to
select the most appropriate surrogate modelling technique. The
preliminary experiments reported here suggest that the proposed
automatic selector is able to identify high-accuracy surrogate models as
long as an appropriate classifier is used for selection.
-
Mudita Sharma,
Alexandros Komninos, Manuel López-Ibáñez, and
Dimitar Kazakov. Deep
Reinforcement Learning-Based Parameter Control in Differential
Evolution. In M. López-Ibáñez and
A. Auger, editors, Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2019. ACM Press, New York, NY,
2019.
-
Leonardo C. T.
Bezerra, Manuel López-Ibáñez, and Thomas
Stützle. Archiver Effects on the
Performance of State-of-the-art Multi- and Many-objective Evolutionary
Algorithms. In M. López-Ibáñez and
A. Auger, editors, Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2019. ACM Press, New York, NY,
2019.
-
Atanu Mazumdar,
Tinkle Chugh, Kaisa Miettinen, and Manuel
López-Ibáñez. On Dealing with
Uncertainties from Kriging Models in Offline Data-Driven Evolutionary
Multiobjective Optimization. In K. Deb, E. D. Goodman,
C. A. Coello Coello, K. Klamroth, K. Miettinen,
S. Mostaghim, and P. Reed, editors, Evolutionary
Multi-criterion Optimization, EMO 2019, volume 11411 of Lecture
Notes in Computer Science, pages 463–474. Springer
International Publishing, Cham, Switzerland, 2019.
-
Aymeric Blot, Manuel
López-Ibáñez, Marie-Eléonore Kessaci-Marmion,
and Laetitia Jourdan. New Initialisation
Techniques for Multi-Objective Local Search: Application to the
Bi-objective Permutation Flowshop. In A. Auger, C. M.
Fonseca, N. Lourenço, P. Machado, L. Paquete, and
D. Whitley, editors, Parallel Problem Solving from Nature - PPSN
XV, volume 11101 of Lecture Notes in Computer Science,
pages 323–334. Springer, Cham, 2018.
★ Nominated for Best paper award
-
Arnaud Liefooghe,
Bilel Derbel, Sébastien Verel, Manuel
López-Ibáñez, Hernán E. Aguirre, and
Kiyoshi Tanaka. On Pareto Local Optimal
Solutions Networks. In A. Auger, C. M. Fonseca,
N. Lourenço, P. Machado, L. Paquete, and
D. Whitley, editors, Parallel Problem Solving from Nature - PPSN
XV, volume 11102 of Lecture Notes in Computer Science,
pages 232–244. Springer, Cham, 2018.
★ Nominated for Best paper award
-
Mudita Sharma, Manuel
López-Ibáñez, and Dimitar Kazakov. Performance Assessment of
Recursive Probability Matching for Adaptive Operator Selection in
Differential Evolution. In A. Auger, C. M. Fonseca,
N. Lourenço, P. Machado, L. Paquete, and
D. Whitley, editors, Parallel Problem Solving from Nature - PPSN
XV, volume 11102 of Lecture Notes in Computer Science,
pages 321–333. Springer, Cham, 2018.
-
Arnaud Liefooghe,
Manuel López-Ibáñez, Luís Paquete, and
Sébastien Verel. Dominance, Epsilon, and
Hypervolume Local Optimal Sets in Multi-objective Optimization, and How
to Tell the Difference. In H. E. Aguirre and
K. Takadama, editors, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2018, pages
324–331. ACM Press, New York, NY, 2018.
-
Leslie Pérez
Cáceres, Manuel López-Ibáñez, Holger H.
Hoos, and Thomas Stützle. An experimental study of
adaptive capping in irace. In R. Battiti, D. E. Kvasov,
and Y. D. Sergeyev, editors, Learning and Intelligent
Optimization, 11th International Conference, LION 11, volume 10556
of Lecture Notes in Computer Science, pages 235–250.
Springer, Cham, 2017.
-
Leonardo C. T.
Bezerra, Manuel López-Ibáñez, and Thomas
Stützle. An
empirical assessment of the properties of inverted generational distance
indicators on multi- and many-objective optimization. In
H. Trautmann, G. Rudolph, K. Klamroth,
O. Schütze, M. M. Wiecek, Y. Jin, and C. Grimme,
editors, Evolutionary Multi-criterion Optimization, EMO 2017,
Lecture Notes in Computer Science, pages 31–45. Springer, 2017.
-
Manuel
López-Ibáñez and Joshua D. Knowles. Machine Decision Makers as
a Laboratory for Interactive EMO. In A. Gaspar-Cunha,
C. H. Antunes, and C. A. Coello Coello, editors,
Evolutionary Multi-criterion Optimization, EMO 2015 Part II,
volume 9019 of Lecture Notes in Computer Science, pages
295–309. Springer, 2015.
-
Leonardo C. T.
Bezerra, Manuel López-Ibáñez, and Thomas
Stützle. Comparing
Decomposition-Based and Automatically Component-Wise Designed
Multi-Objective Evolutionary Algorithms. In A. Gaspar-Cunha,
C. H. Antunes, and C. A. Coello Coello, editors,
Evolutionary Multi-criterion Optimization, EMO 2015 Part I,
volume 9018 of Lecture Notes in Computer Science, pages
396–410. Springer, 2015.
-
Leonardo C. T.
Bezerra, Manuel López-Ibáñez, and Thomas
Stützle. To
DE or Not to DE? Multi-objective Differential Evolution Revisited from a
Component-Wise Perspective. In A. Gaspar-Cunha, C. H.
Antunes, and C. A. Coello Coello, editors, Evolutionary
Multi-criterion Optimization, EMO 2015 Part I, volume 9018 of
Lecture Notes in Computer Science, pages 48–63. Springer,
2015.
-
Leonardo C. T.
Bezerra, Manuel López-Ibáñez, and Thomas
Stützle. Automatic Design of
Evolutionary Algorithms for Multi-Objective Combinatorial
Optimization. In T. Bartz-Beielstein, J. Branke,
B. Filipič, and J. Smith, editors, PPSN 2014,
volume 8672 of Lecture Notes in Computer Science, pages
508–517. Springer, 2014.
-
Manuel
López-Ibáñez, Arnaud Liefooghe, and Sébastien
Verel. Local
Optimal Sets and Bounded Archiving on Multi-objective NK-Landscapes with
Correlated Objectives. In T. Bartz-Beielstein,
J. Branke, B. Filipič, and J. Smith, editors,
Parallel Problem Solving from Nature – PPSN XIII, volume
8672 of Lecture Notes in Computer Science, pp.
621–630. Springer, Heidelberg, 2014.
The properties of local optimal solutions in multi-objective
combinatorial optimization problems are crucial for the effectiveness of
local search algorithms, particularly when these algorithms are based on
Pareto dominance. Such local search algorithms typically return a set of
mutually nondominated Pareto local optimal (PLO) solutions, that is, a
PLO-set. This paper investigates two aspects of PLO-sets by means of
experiments with Pareto local search (PLS). First, we examine the impact
of several problem characteristics on the properties of PLO-sets for
multi-objective NK-landscapes with correlated objectives. In particular,
we report that either increasing the number of objectives or decreasing
the correlation between objectives leads to an exponential increment on
the size of PLO-sets, whereas the variable correlation has only a minor
effect. Second, we study the running time and the quality reached when
using bounding archiving methods to limit the size of the archive
handled by PLS, and thus, the maximum size of the PLO-set found. We
argue that there is a clear relationship between the running time of PLS
and the difficulty of a problem instance.
-
Leslie Pérez
Cáceres, Manuel López-Ibáñez, and Thomas
Stützle. Ant
Colony Optimization on a Budget of 1000. In M. Dorigo
et al., editors, Swarm Intelligence, 8th International
Conference, ANTS 2014, volume 8667 of Lecture Notes in Computer
Science, pages 50–61. Springer, 2014.
-
Leslie Pérez
Cáceres, Manuel López-Ibáñez, and Thomas
Stützle. An
Analysis of Parameters of irace. In Proceedings of EvoCOP
2014 - 14th European Conference on Evolutionary Computation in
Combinatorial Optimization, Lecture Notes in Computer Science, pages
37–48. Springer, 2014.
-
Leonardo C. T.
Bezerra, Manuel López-Ibáñez, and Thomas
Stützle. Deconstructing
Multi-Objective Evolutionary Algorithms: An Iterative Analysis on the
Permutation Flowshop. In P. M. Pardalos, M. G. C.
Resende, C. Vogiatzis, and J. L. Walteros, editors,
Learning and Intelligent Optimization, 8th International Conference,
LION 8, volume 8426 of Lecture Notes in Computer Science,
pages 57–172. Springer, 2014.
-
Frank Hutter, Manuel
López-Ibáñez, Chris Fawcett, Marius Thomas
Lindauer, Holger H. Hoos, Kevin Leyton-Brown, and Thomas
Stützle. AClib: a Benchmark Library
for Algorithm Configuration. In P. M. Pardalos,
M. G. C. Resende, C. Vogiatzis, and J. L. Walteros,
editors, Learning and Intelligent Optimization, 8th International
Conference, LION 8, volume 8426 of Lecture Notes in Computer
Science, pages 36–40. Springer, 2014.
-
Franco Mascia, Manuel
López-Ibáñez, Jérémie Dubois-Lacoste,
Marie-Eléonore Marmion, and Thomas Stützle. Algorithm Comparison by
Automatically Configurable Stochastic Local Search Frameworks: A Case
Study Using Flow-Shop Scheduling Problems. In M. J. Blesa,
C. Blum, and S. Voß, editors, Hybrid
Metaheuristics, volume 8457 of Lecture Notes in Computer
Science, pages 30–44. Springer, 2014.
-
Marie-Eléonore
Marmion, Franco Mascia, Manuel López-Ibáñez, and
Thomas Stützle. Automatic Design of Hybrid
Stochastic Local Search Algorithms. In M. J. Blesa,
C. Blum, P. Festa, A. Roli, and M. Sampels, editors,
Hybrid Metaheuristics, volume 7919 of Lecture Notes in
Computer Science, pages 144–158. Springer, Heidelberg,
Germany, 2013.
-
Florence Massen, Manuel
López-Ibáñez, Thomas Stützle, and Yves Deville.
Experimental
Analysis of Pheromone-Based Heuristic Column Generation Using
irace. In M. J. Blesa, C. Blum, P. Festa,
A. Roli, and M. Sampels, editors, Hybrid
Metaheuristics, volume 7919 of Lecture Notes in Computer
Science, pages 92–106. Springer, Heidelberg, Germany,
2013.
-
Andreea Radulescu, Manuel
López-Ibáñez, and Thomas Stützle. Automatically Improving the
Anytime Behaviour of Multiobjective Evolutionary Algorithms. In
R. C. Purshouse, P. J. Fleming, C. M. Fonseca,
S. Greco, and J. Shaw, editors, EMO, volume 7811 of
Lecture Notes in Computer Science, pages 825–840.
Springer, Heidelberg, Germany, 2013.
-
Franco Mascia, Manuel
López-Ibáñez, Jérémie Dubois-Lacoste,
and Thomas Stützle. From Grammars to
Parameters: Automatic Iterated Greedy Design for the Permutation
Flow-shop Problem with Weighted Tardiness. In P. Pardalos
and G. Nicosia, editors, Learning and Intelligent Optimization,
7th International Conference, LION 7, volume 7997 of Lecture
Notes in Computer Science, pages 321–334. Springer,
Heidelberg, Germany, 2013.
-
Leonardo C. T.
Bezerra, Manuel López-Ibáñez, and Thomas
Stützle. An
Analysis of Local Search for the Bi-objective Bidimensional Knapsack
Problem. In M. Middendorf and C. Blum, editors,
Proceedings of EvoCOP 2013 - 13th European Conference on Evolutionary
Computation in Combinatorial Optimization, volume 7832 of
Lecture Notes in Computer Science, pages 85–96. Springer,
Heidelberg, Germany, 2013.
-
Manuel
López-Ibáñez, Franco Mascia, Marie-Eléonore
Marmion, and Thomas Stützle. Automatic Design of a Hybrid
Iterated Local Search for the Multi-Mode Resource-Constrained
Multi-Project Scheduling Problem. In G. Kendall, G. V.
Berghe, and B. McCollum, editors, Multidisciplinary
International Conference on Scheduling: Theory and Applications (MISTA
2013), pages 1–6, Gent, Belgium, 2013.
-
Leonardo C. T.
Bezerra, Manuel López-Ibáñez, and Thomas
Stützle. Automatic Generation of
Multi-objective ACO Algorithms for the Biobjective Knapsack
Problem. In M. Dorigo et al., editors, Swarm
Intelligence, 8th International Conference, ANTS 2012, volume 7461
of Lecture Notes in Computer Science, pages 37–48.
Springer, Heidelberg, Germany, 2012.
-
Manuel
López-Ibáñez, Tianjun Liao, and Thomas Stützle.
On the anytime
behavior of IPOP-CMA-ES. In C. A. Coello Coello et al.,
editors, PPSN 2012, Part I, volume 7491 of Lecture Notes in
Computer Science, pages 357–366. Springer, Heidelberg,
Germany, 2012.
-
Dimo Brockhoff, Manuel
López-Ibáñez, Boris Naujoks, and Günther
Rudolph. Runtime
Analysis of Simple Interactive Evolutionary Biobjective Optimization
Algorithms. In C. A. Coello Coello et al., editors,
PPSN 2012, Part I, volume 7491 of Lecture Notes in Computer
Science, pages 123–132. Springer, Heidelberg, Germany,
2012.
-
Jérémie
Dubois-Lacoste, Manuel López-Ibáñez, and Thomas
Stützle. Pareto Local Search
Algorithms for Anytime Bi-objective Optimization. In J.-K. Hao
and M. Middendorf, editors, Proceedings of EvoCOP 2012 - 12th
European Conference on Evolutionary Computation in Combinatorial
Optimization, volume 7245 of Lecture Notes in Computer
Science, pages 206–217. Springer, Heidelberg, Germany,
2012.
- Jérémie Dubois-Lacoste, Manuel
López-Ibáñez, and Thomas Stützle. Automatic configuration of
state-of-the-art multi-objective algorithms using the TP+PLS
framework. In N. Krasnogor et al., editors,
Proceedings of the Genetic and Evolutionary Computation Conference,
GECCO 2011, pages 2019–2026. ACM press, New York, NY, 2011.
[ bibtex |
PDF | doi: 10.1145/2001576.2001847 ]
-
Stefan Eppe, Manuel
López-Ibáñez, Thomas Stützle, and Yves De Smet.
An Experimental Study of Preference Model Integration into
Multi-Objective Optimization Heuristics. In Proceedings of the
2011 Congress on Evolutionary Computation (CEC 2011), pages
2751–2758. IEEE Press, Piscataway, NJ, 2011.
-
Manuel
López-Ibáñez, Joshua D. Knowles, and Marco
Laumanns. On
Sequential Online Archiving of Objective Vectors. In
R. H. C. Takahashi et al., editors, Evolutionary
Multi-criterion Optimization, EMO 2011, volume 6576 of Lecture
Notes in Computer Science, pages 46–60. Springer, 2011.
-
Carlos M.
Fonseca, Andreia P. Guerreiro, Manuel
López-Ibáñez, and Luís Paquete. On the Computation of the
Empirical Attainment Function. In R. H. C. Takahashi
et al., editors, Evolutionary Multi-criterion Optimization, EMO
2011, volume 6576 of Lecture Notes in Computer Science,
pages 106–120. Springer, 2011.
-
Manuel
López-Ibáñez and Thomas Stützle. Automatic Configuration of
Multi-Objective ACO Algorithms. In M. Dorigo et al.,
editors, Swarm Intelligence, 7th International Conference, ANTS
2010, volume 6234 of Lecture Notes in Computer Science,
pages 95–106. Springer, 2010.
-
Michael Maur, Manuel
López-Ibáñez, and Thomas Stützle. Pre-scheduled and adaptive
parameter variation in Max-Min Ant System. In H. Ishibuchi
et al., editors, Proceedings of the 2010 Congress on
Evolutionary Computation (CEC 2010), pages 3823–3830. IEEE
Press, Piscataway, NJ, 2010.
-
Manuel
López-Ibáñez and Thomas Stützle. The impact of design choices of
multi-objective ant colony optimization algorithms on performance: An
experimental study on the biobjective TSP. In GECCO
2010, pages 71–78. ACM press, New York, NY, 2010.
★ Best paper award
-
Jérémie Dubois-Lacoste, Manuel
López-Ibáñez, and Thomas Stützle. Adaptive
“Anytime” Two-Phase Local Search. In Learning and
Intelligent Optimization, 4th International Conference, LION 4,
volume 6073 of Lecture Notes in Computer Science, pages
52–67. Springer, Heidelberg, Germany, 2010.
★ Best paper award
- Manuel
López-Ibáñez and Thomas Stützle. An Analysis of Algorithmic
Components for Multiobjective Ant Colony Optimization: A Case Study on the
Biobjective TSP. In P. Collet et al., editors,
Artificial Evolution, volume 5975 of Lecture Notes in
Computer Science, pages 134–145. Springer, Heidelberg, Germany,
2010.
★ 3rd best paper award
[ bibtex |
doi: 10.1007/978-3-642-14156-0_12 ]
- Manuel López-Ibáñez, Christian
Blum, Dhananjay Thiruvady, Andreas T. Ernst, and Bernd Meyer.
Beam-ACO based on
stochastic sampling for makespan optimization concerning the TSP with time
windows. In C. Cotta and P. Cowling, editors,
Proceedings of EvoCOP 2009 - 9th European Conference Evolutionary
Computation in Combinatorial Optimization, volume 5482 of Lecture
Notes in Computer Science, pages 97–108. Springer, Heidelberg,
2009.
[ bibtex |
doi: 10.1007/978-3-642-01009-5_9 ]
- Manuel
López-Ibáñez and Christian Blum. Beam-ACO Based on Stochastic
Sampling: A Case Study on the TSP with Time Windows. In
Proceedings of LION 3 - 3rd International Conference on Learning and
Intelligent Optimization, volume 5851 of Lecture Notes in
Computer Science, pages 59–73. Springer, Heidelberg, 2009.
[ bibtex |
doi: 10.1007/978-3-642-11169-3_5 ]
- Jérémie Dubois-Lacoste, Manuel
López-Ibáñez, and Thomas Stützle. Effective Hybrid Stochastic
Local Search Algorithms for Biobjective Permutation Flowshop
Scheduling. In M. J. Blesa, C. Blum, L. Di Gaspero,
A. Roli, M. Sampels, and A. Schaerf, editors, Hybrid
Metaheuristics, volume 5818 of Lecture Notes in Computer
Science, pages 100–114. Springer, Heidelberg, Germany,
2009.
[ bibtex |
doi: 10.1007/978-3-642-04918-7_8 |
supplementary
material ]
- Manuel López-Ibáñez,
T. Devi Prasad, and Ben Paechter. Parallel optimisation of
pump schedules with a thread-safe variant of EPANET toolkit. In
Jakobus E. van Zyl, A. A. Ilemobade, and H. E. Jacobs,
editors, Proceedings of the 10th Annual Water Distribution Systems
Analysis Conference (WDSA 2008). ASCE, August 2008.
[ bibtex |
doi: 10.1061/41024(340)40 |
PDF |
software ]
- Carlos M. Fonseca, Luís
Paquete, and Manuel López-Ibáñez. An improved dimension - sweep
algorithm for the hypervolume indicator. In Proceedings of the
2006 Congress on Evolutionary Computation (CEC'06), pages
1157–1163. IEEE Press, Piscataway, NJ, July 2006.
[ bibtex | doi: 10.1109/CEC.2006.1688440 |
PDF |
software ]
- Manuel López-Ibáñez,
T. Devi Prasad, and Ben Paechter. Multi-objective Optimisation of
the Pump Scheduling Problem using SPEA2. In Proceedings of the
2005 Congress on Evolutionary Computation (CEC 2005), volume 1,
pages 435–442. IEEE Press, Piscataway, NJ, September 2005.
[ bibtex |
doi: 10.1109/CEC.2005.1554716 |
PDF | Presentation
(PDF) ]
- Manuel López-Ibáñez,
T. Devi Prasad, and Ben Paechter. Optimal pump scheduling: Representation and
multiple objectives. In Dragan A. Savic, Godfrey A.
Walters, Roger King, and Soon Thiam-Khu, editors, Proceedings of the
Eighth International Conference on Computing and Control for the Water
Industry (CCWI 2005), volume 1, pages 117–122, University
of Exeter, UK, September 2005.
[ bibtex |
PDF ]
-
Manuel
López-Ibáñez, Luís Paquete, and Thomas
Stützle. On
the Design of ACO for the Biobjective Quadratic Assignment
Problem. In M. Dorigo et al., editors, Ant Colony
Optimization and Swarm Intelligence, 4th International Workshop, ANTS
2004, volume 3172 of Lecture Notes in Computer Science,
pp. 214–225. Springer, Heidelberg, 2004.
Technical Reports
-
Jürgen Branke,
Salvatore Corrente, Salvatore Greco, Milosz Kadzinski, Manuel
López-Ibáñez, Vincent Mousseau, Mauro Munerato, and
Roman Slowiński. Behavior-Realistic Artificial
Decision-Makers to Test Preference-Based Multi-objective Optimization
Method (Working Group “Machine Decision-Making”). In
S. Greco, K. Klamroth, J. D. Knowles, and G. Rudolph,
editors, Understanding Complexity in Multiobjective Optimization
(Dagstuhl Seminar 15031), volume 5(1) of Dagstuhl Reports,
pages 110–116. Schloss Dagstuhl–Leibniz-Zentrum für
Informatik, Germany, 2015.
-
Vito Trianni and Manuel
López-Ibáñez. Advantages
of Multi-Objective Optimisation in Evolutionary Robotics: Survey and Case
Studies. Technical Report TR/IRIDIA/2014-014, IRIDIA,
Université Libre de Bruxelles, Belgium, 2014.
- Manuel López-Ibáñez and Thomas
Stützle. The
Automatic Design of Multi-Objective Ant Colony Optimization
Algorithms. Technical Report TR/IRIDIA/2011-003, IRIDIA,
Université Libre de Bruxelles, Belgium, 2011. Published in IEEE Transactions on Evolutionary
Computation.
[ bibtex ]
- Manuel López-Ibáñez,
Jérémie Dubois-Lacoste, Thomas Stützle, and Mauro
Birattari. The
irace package, Iterated Race for Automatic Algorithm
Configuration. Technical Report TR/IRIDIA/2011-004, IRIDIA,
Université Libre de Bruxelles, Belgium, 2011.
[ bibtex |
software ]
- Manuel López-Ibáñez,
Joshua D. Knowles, and Marco Laumanns. On
Sequential Online Archiving of Objective Vectors. Technical Report
TR/IRIDIA/2011-001, IRIDIA, Université Libre de Bruxelles, Belgium,
Brussels, Belgium, 2011. This is a revised version of the one published in EMO 2011.
[ bibtex |
software ]
- Jérémie Dubois-Lacoste, Manuel
López-Ibáñez, and Thomas Stützle. Improving
the Anytime Behavior of Two-Phase Local Search. Technical Report
TR/IRIDIA/2010-022, IRIDIA, Université Libre de Bruxelles, Belgium,
2010. Published in Annals of Mathematics and
Artificial Intelligence.
[ bibtex ]
- Jérémie Dubois-Lacoste, Manuel
López-Ibáñez, and Thomas Stützle. A Hybrid
TP+PLS Algorithm for Bi-objective Flow-Shop Scheduling Problems.
Technical Report TR/IRIDIA/2010-019, IRIDIA, Université Libre de
Bruxelles, Belgium, Brussels, Belgium, 2010. Published in Computers & Operations
Research.
[ bibtex ]
- Thomas Stützle, Manuel
López-Ibáñez, P. Pellegrini, Michael Maur, Marco
Montes de Oca, Mauro Birattari, and Marco Dorigo. Parameter Adaptation
in Ant Colony Optimization. Technical Report TR/IRIDIA/2010-002,
IRIDIA, Université Libre de Bruxelles, Belgium, January 2010.
Published as a book chapter.
[ bibtex ]
- Jérémie Dubois-Lacoste, Manuel
López-Ibáñez, and Thomas Stützle. Adaptive
“Anytime” Two-Phase Local Search. Technical Report
TR/IRIDIA/2009-026, IRIDIA, Université Libre de Bruxelles, Belgium,
Brussels, Belgium, 2010. Published in the proceedings of LION 4.
[ bibtex ]
- Jérémie Dubois-Lacoste, Manuel
López-Ibáñez, and Thomas Stützle. Effective
Hybrid Stochastic Local Search Algorithms for Biobjective Permutation
Flowshop Scheduling. Technical Report TR/IRIDIA/2009-020, IRIDIA,
Université Libre de Bruxelles, Belgium, June 2009. Published in the proceedings of Hybrid
Metaheuristics 2009.
[ bibtex ]
- Manuel López-Ibáñez and Thomas
Stützle. An Analysis of Algorithmic Components for Multiobjective
Ant Colony Optimization: A Case Study on the Biobjective TSP.
Technical Report TR/IRIDIA/2009-019, IRIDIA, Université Libre de
Bruxelles, Belgium, June 2009. Published in the
proceedings of Evolution Artificielle, 2009.
[ bibtex ]
- Manuel López-Ibáñez,
Luís Paquete, and Thomas Stützle. Exploratory analysis
of stochastic local search algorithms in biobjective
optimization. Technical Report TR/IRIDIA/2009-015, IRIDIA,
Université Libre de Bruxelles, Belgium, May 2009. Published as a book chapter.
[ bibtex ]
- Christian Blum, María J. Blesa, and
Manuel López-Ibáñez. Beam search for the
longest common subsequence problem. Technical Report LSI-08-29,
Department LSI, Univeristat Politècnica de Catalunya, 2008.
Published in Computers &
Operations Research.
[ bibtex ]
- Manuel López-Ibáñez and
Christian Blum. Beam-ACO Based on Stochastic Sampling: A Case Study on
the TSP with Time Windows. Technical Report LSI-08-28, Department LSI,
Universitat Politècnica de Catalunya, 2008. Extended version published in Computers & Operations
Research.
[ bibtex ]
- Nicola Beume,
Carlos M. Fonseca, Manuel López-Ibáñez,
Luís Paquete, and Jan Vahrenhold. On the complexity of
computing the hypervolume indicator. Technical Report CI-235/07,
University of Dortmund, December 2007. Published in IEEE Transactions on Evolutionary
Computation.
[ bibtex ]
- Luís Paquete, Carlos M. Fonseca,
and Manuel López-Ibáñez. An optimal algorithm
for a special case of Klee's measure problem in three dimensions.
Technical Report CSI-RT-I-01/2006, CSI, Universidade do Algarve, 2006.
Superseded by paper in IEEE Transactions
on Evolutionary Computation.
[ bibtex ]
- Luís Paquete, Thomas Stützle,
and Manuel López-Ibáñez. On
the design and analysis of SLS algorithms for multiobjective combinatorial
optimization problems. Technical Report TR/IRIDIA/2005/029,
IRIDIA, Université Libre de Bruxelles, Belgium, 2005.
[ bibtex ]
-
Manuel
López-Ibáñez, Luís Paquete, and Thomas
Stützle. Hybrid Population-based Algorithms for the Bi-objective
Quadratic Assignment Problem. Technical Report
AIDA–04–11, FG Intellektik, FB Informatik, TU Darmstadt,
December 2004. Published in Journal of
Mathematical Modelling and Algorithms.
Last modified: 21 September 2024