Publications


WARNINGNOTE: 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

  1. Manuel López-Ibáñez. Operational Optimisation of Water Distribution Networks. PhD thesis, School of Engineering and the Built Environment, Edinburgh Napier University, UK, 2009.
  2. Manuel López-Ibáñez. Multi-objective Ant Colony Optimization. Diploma thesis, Intellectics Group, Computer Science Department, Technische Universität Darmstadt, Germany, 2004.

International Journals

  1. 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.
  2. 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, 318(3):740–751, 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.
  3. 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, 18:105–139, 2024.
    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.
  4. Miqing Li, Manuel López-Ibáñez, and Xin Yao. Multi-Objective Archiving. IEEE Transactions on Evolutionary Computation, 28(3):696–717, 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.
  5. 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
  6. 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, 31(4):375–399, 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. Lucía Rivadeneira, Jian-Bo Yang, and Manuel López-Ibáñez. Predicting tweet impact using a novel evidential reasoning prediction method. Expert Systems with Applications, 2021.
    This study presents a novel evidential reasoning (ER) prediction model called MAKER-RIMER to examine how different features embedded in Twitter posts (tweets) can predict the number of retweets achieved during an electoral campaign. The tweets posted by the two most voted candidates during the official campaign for the 2017 Ecuadorian Presidential election were used for this research. For each tweet, five features including type of tweet, emotion, URL, hashtag, and date are identified and coded to predict if tweets are of either high or low impact. The main contributions of the new proposed model include its suitability to analyse tweet datasets based on likelihood analysis of data. The model is interpretable, and the prediction process relies only on the use of available data. The experimental results show that MAKER-RIMER performed better, in terms of misclassification error, when compared against other predictive machine learning approaches. In addition, the model allows observing which features of the candidates' tweets are linked to high and low impact. Tweets containing allusions to the contender candidate, either with positive or negative connotations, without hashtags, and written towards the end of the campaign, were persistently those with the highest impact. URLs, on the other hand, is the only variable that performs differently for the two candidates in terms of achieving high impact. MAKER-RIMER can provide campaigners of political parties or candidates with a tool to measure how features of tweets are predictors of their impact, which can be useful to tailor Twitter content during electoral campaigns.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. Vito Trianni and Manuel López-Ibáñez. Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics. PLoS One, 10(8):e0136406, 2015.
  27. 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.
  28. Jérémie Dubois-Lacoste, Manuel López-Ibáñez, and Thomas Stützle. Anytime Pareto Local Search. European Journal of Operational Research, 243(2):369–385, 2015.
  29. 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.
  30. Manuel López-Ibáñez and Thomas Stützle. Automatically Improving the Anytime Behaviour of Optimisation Algorithms. European Journal of Operational Research, 235(3):569–582, 2014.
  31. 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.
  32. 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.
  33. 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.
  34. 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.
  35. 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.
  36. 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.
  37. Manuel López-Ibáñez and Christian Blum. Beam-ACO for the travelling salesman problem with time windows. Computers & Operations Research, 37(9):1570–1583, 2010.
  38. 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.
  39. 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.
  40. 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.
  41. 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

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. Christian Blum and Manuel López-Ibáñez. Ant Colony Optimization. In The Industrial Electronics Handbook: Intelligent Systems. CRC Press, second edition, 2011.
  7. 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.
  8. 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.
  9. 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

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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
  23. 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
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. 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.
  32. 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.
  33. 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.
  34. 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.
  35. 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.
  36. 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.
  37. 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.
  38. 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.
  39. 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.
  40. 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.
  41. 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.
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. 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.
  47. 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.
  48. 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 | ACM DL Author-ize servicePDF  | doi: 10.1145/2001576.2001847 ]
  49. 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.
  50. 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.
  51. 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.
  52. 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.
  53. 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.
  54. 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
  55. 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
  56. 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 ]
  57. 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 ]
  58. 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 ]
  59. 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 ]
  60. 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 ]
  61. 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  ]
  62. 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) ]
  63. 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 ]
  64. 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

  1. 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.
  2. 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.
  3. 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 ]
  4. 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 ]
  5. 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 ]
  6. 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 ]
  7. 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 ]
  8. 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 ]
  9. 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 ]
  10. 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 ]
  11. 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 ]
  12. 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 ]
  13. 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 ]
  14. 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 ]
  15. 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 ]
  16. 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 ]
  17. 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 ]
  18. 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: 12 November 2024