Analysis of Adaptive Operator Selection Techniques on the Royal Road and Long K-Path Problems

Álvaro Fialho 1 Marc Schoenauer 1, 2, 3 Michèle Sebag 1, 2, 3
1 MSR - INRIA - Microsoft Research - Inria Joint Centre
2 LRI - Laboratoire de Recherche en Informatique
3 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 : One of the choices that most affect the performance of Evolutionary Algorithms is the selection of the variation operators that are efficient to solve the problem at hand. This work presents an empirical analysis of different Adaptive Operator Selection (AOS) methods, i.e., techniques that automatically select the operator to be applied among the available ones, while searching for the solution. Four previously published operator selection rules are combined to four different credit assignment mechanisms. These 16 AOS combinations are analyzed and compared in the light of two well-known benchmark problems in Evolutionary Computation, the Royal Road and the Long K-Path.
Type de document :
Communication dans un congrès
Genetic and Evolutionary Computation Conference (GECCO), Jul 2009, Montreal, Canada. pp.779-786, 2009, 〈10.1145/1569901.1570009〉
Liste complète des métadonnées
https://hal.inria.fr/inria-00377449
Contributeur : Álvaro Fialho <>
Soumis le : jeudi 16 juillet 2009 - 16:49:05
Dernière modification le : jeudi 5 avril 2018 - 12:30:12
Document(s) archivé(s) le : mercredi 22 septembre 2010 - 13:28:56

Fichiers

banditGECCO09.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

INRIA | UMR8623 | LRI-AO

Citation

Álvaro Fialho, Marc Schoenauer, Michèle Sebag. Analysis of Adaptive Operator Selection Techniques on the Royal Road and Long K-Path Problems. Genetic and Evolutionary Computation Conference (GECCO), Jul 2009, Montreal, Canada. pp.779-786, 2009, 〈10.1145/1569901.1570009〉. 〈inria-00377449v2〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

325

Téléchargements de fichiers

870

Contact


archive-ouverte@inria.fr