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
3 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
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.
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Communication dans un congrès
Genetic and Evolutionary Computation Conference (GECCO), Jul 2009, Montreal, Canada. pp.779-786, 2009, 〈10.1145/1569901.1570009〉
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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

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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〉

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