Controlling Genetic Algorithms (1996)
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BibTeX
@MISC{Sebag96controllinggenetic,
author = {Michèle Sebag and Marc Schoenauer and Inductif Optimisation Hybride},
title = {Controlling Genetic Algorithms},
year = {1996}
}
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Abstract
. This paper briefly recalls the principle of genetic algorithms (GA) and discusses their efficiency. The Radcliffe's principles, linking the representation of the problem and the evolution operators, are presented. Last, we address the control of an AG by an induction-based approach. Experimental results obtained on typical GA problems and a combinatoric optimization problem, are discussed. Mots cl' es : Algorithme G'en'etique, Evolution, Controle, Optimisation, Apprentissage Inductif, Optimisation Hybride Keywords : Genetic Algorithm, Evolution, Control, Optimization, Inductive Learning, Hybrid Optimization 1 Introduction Les algorithmes de Calcul ' Evolutif (Evolutionary Computation, EC) sont des m'ethodes d'optimisation stochastiques (i.e. fond'ees sur des tirages al'eatoires) , op'erant dans des espaces de taille extremement vaste, et grossi`erement inspir'es de la m'etaphore darwinienne les plus adapt'es survivent. Leur vogue r'ecente ([56, 5, 19, 40, 43, 41]) est due en par...
Keyphrases
genetic algorithm de tirages hybrid optimization e survivent typical ga problem erant dans de espaces mot cl optimisation stochastiques evolutionary computation etaphore darwinienne apprentissage inductif leur vogue due en par induction-based approach erement inspir optimisation hybride keywords evolution operator paper briefly sont de inductive learning combinatoric optimization problem introduction le extremement vaste experimental result
