Parameter Setting with Dynamic Island Models

TitreParameter Setting with Dynamic Island Models
Type de publicationCommunication
TypeCommunication avec actes dans un congrès
Année2013
LangueAnglais
Titre du colloqueLecture Notes in Computer Science, Learning and Intelligent Optimization (LION 7)
Volume7997
AuteurCandan, Caner , Goëffon, Adrien , Lardeux, Frédéric , Saubion, Frédéric
EditeurSpringer Berlin Heidelberg
VilleBerlin, Heidelberg
ISBN978-3-642-44972-7
Résumé en anglais

In this paper we proposed the use of a dynamic island model which aim at adapting parameter settings dynamically. Since each island corresponds to a specific parameter setting, measuring the evolution of islands populations sheds light on the optimal parameter settings efficiency throughout the search. This model can be viewed as an alternative adaptive operator selection technique for classic steady state genetic algorithms. Empirical studies provide competitive results with respect to other methods like automatic tuning tools. Moreover, this model could ease the parallelization of evolutionary algorithms and can be used in a synchronous or asynchronous way.

URL de la noticehttp://okina.univ-angers.fr/publications/ua7698
DOI10.1007/978-3-642-44973-4_26