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.
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