Spacing Memetic Algorithms

TitreSpacing Memetic Algorithms
Type de publicationCommunication
TypeCommunication avec actes dans un congrès
Date du colloque2011
Titre du colloque2th Annual Conference on Genetic and Evolutionary Computation (GECCO-2011)
Titre des actes ou de la revueProceedings of the 13th Annual Conference on Genetic and Evolutionary Computation
Pagination1061 - 1068
AuteurCosmin Porumbel, Daniel, Hao, Jin-Kao , Kuntz, Pascale
Mots-clésdistance measure, diversity, Memetic algorithms, spacing
Résumé en anglais

We introduce the Spacing Memetic Algorithm (SMA), a formal evolutionary model devoted to a systematic control of spacing (distances) among individuals. SMA uses search space distance information to decide what individuals are acceptable in the population, what individuals need to be replaced and when to apply mutations. By ensuring a "healthy" spacing (and thus diversity), SMA substantially reduces the risk of premature convergence and helps the search process to continuously discover new high-quality search areas. Generally speaking, the number of distance calculations represents a limited computational overhead compared to the number of local search iterations. Most existing memetic algorithms can be "upgraded" to a spacing memetic algorithm, provided that a suitable distance measure can be specified. The impact of the main SMA components is assessed within several case studies on different problems.


Date du colloque : 07/2011

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