• Feature-based benchmarking of...

*Result*: Feature-based benchmarking of distance-based multi/many-objective optimisation problems: A machine learning perspective

Title:
Feature-based benchmarking of distance-based multi/many-objective optimisation problems: A machine learning perspective
Authors:
Liefooghe, Arnaud, Verel, Sébastien, Chugh, Tinkle, Fieldsend, Jonathan, Allmendinger, Richard, Miettinen, Kaisa
Contributors:
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Optimisation de grande taille et calcul large échelle (BONUS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique Signal et Image de la Côte d'Opale (LISIC), Université du Littoral Côte d'Opale (ULCO), University of Exeter, University of Manchester Manchester, University of Jyväskylä (JYU), Emmerich, Michael, Deutz, André, Wang, Hao, Kononova, Anna V., Naujoks, Boris, Li, Ke, Miettinen, Kaisa, Yevseyeva, Iryna
Source:
Evolutionary Multi-Criterion Optimization : 12th International conference, EMO 2023 Leiden, The Netherlands, March 20-24, 2023. Proccedings ; EMO 2023 - 12th International Conference on Evolutionary Multi-Criterion Optimization ; https://hal.science/hal-04021499 ; EMO 2023 - 12th International Conference on Evolutionary Multi-Criterion Optimization, Mar 2023, Leiden, Netherlands. pp.260-273, ⟨10.1007/978-3-031-27250-9_19⟩
Publisher Information:
HAL CCSD
Springer Nature Switzerland
Publication Year:
2023
Collection:
Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe)
Subject Terms:
Multi/many-objective distance problems, Feature-based performance prediction, Automated algorithm selection, [INFO]Computer Science [cs], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO]
Subject Geographic:
Leiden, Netherlands
Document Type:
*Conference* conference object
Language:
English
ISBN:
978-3-031-27249-3
3-031-27249-8
Relation:
hal-04021499; https://hal.science/hal-04021499; https://hal.science/hal-04021499/document; https://hal.science/hal-04021499/file/liefooghe_emo2023.pdf
DOI:
10.1007/978-3-031-27250-9_19
Availability:
https://hal.science/hal-04021499
https://hal.science/hal-04021499/document
https://hal.science/hal-04021499/file/liefooghe_emo2023.pdf
https://doi.org/10.1007/978-3-031-27250-9_19
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edsbas.AB5A4294
Database:
BASE

*Further Information*

*International audience ; We consider the application of machine learning techniques to gain insights into the effect of problem features on algorithm performance, and to automate the task of algorithm selection for distance-based multi- and many-objective optimisation problems. This is the most extensive benchmark study of such problems to date. The problem features can be set directly by the problem generator, and include e.g. the number of variables, objectives, local fronts, and disconnected Pareto sets. Using 945 problem configurations (leading to 28 350 instances) of varying complexity, we find that the problem features and the available optimisation budget (i) affect the considered algorithms (NSGA-II, IBEA, MOEA/D, and random search) in different ways and that (ii) it is possible to recommend a relevant algorithm based on problem features.*