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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Influence of the Migration Period in Parallel Distributed GAs for Dynamic Optimization
verfasst von : Yesnier Bravo, Gabriel Luque, Enrique Alba
Erschienen in: Learning and Intelligent Optimization
Verlag: Springer Berlin Heidelberg
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Dynamic optimization problems (DOP) challenge the performance of the standard Genetic Algorithm (GA) due to its
panmictic
population strategy. Several approaches have been proposed to tackle this limitation. However, one of the barely studied domains has been the parallel distributed GA (dGA), characterized by decentralizing the population in islands communicating through
migrations
of individuals. In this article, we analyze the influence of the migration period in dGAs for DOPs. Results show how to adjust this parameter for addressing different change severities in a comprehensive set of dynamic test-bed functions.