On Using Populations of Sets in Multiobjective Optimization

Johannes Bader 1 Dimo Brockhoff 2 Samuel Welten 1 Eckart Zitzler 1
2 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : Most existing evolutionary approaches to multiobjective optimization aim at finding an appropriate set of compromise solutions, ideally a subset of the Pareto-optimal set. That means they are solving a set problem where the search space consists of all possible solution sets. Taking this perspective, multiobjective evolutionary algorithms can be regarded as hill-climbers on solution sets: the population is one element of the set search space and selection as well as variation implement a specific type of set mutation operator. Therefore, one may ask whether a ‘real' evolutionary algorithm on solution sets can have advantages over the classical single-population approach. This paper investigates this issue; it presents a multi-population multiobjective optimization framework and demonstrates its usefulness on several test problems and a sensor network application.
Type de document :
Communication dans un congrès
Evolutionary Multi-Criterion Optimization (EMO 2009), Apr 2009, Nantes, France. 5467, pp.140-154, 2009, LNCS
Liste complète des métadonnées

Littérature citée [21 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00535807
Contributeur : Dimo Brockhoff <>
Soumis le : vendredi 12 novembre 2010 - 19:02:53
Dernière modification le : jeudi 5 avril 2018 - 12:30:12
Document(s) archivé(s) le : dimanche 13 février 2011 - 02:51:38

Fichier

EMO2009authorVersion.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : inria-00535807, version 1

Collections

Citation

Johannes Bader, Dimo Brockhoff, Samuel Welten, Eckart Zitzler. On Using Populations of Sets in Multiobjective Optimization. Evolutionary Multi-Criterion Optimization (EMO 2009), Apr 2009, Nantes, France. 5467, pp.140-154, 2009, LNCS. 〈inria-00535807〉

Partager

Métriques

Consultations de la notice

242

Téléchargements de fichiers

111