Finite population models of dynamic optimization with alternating fitness functions

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In order to study genetic algorithms in dynamic environments, we describe a stochastic finite population model of dynamic optimization, assuming an alternating fitness functions approach. We propose models and methods that can be used to determine exact expectations of performance. As an application of the model, an analysis of the performance of haploid and diploid genetic algorithms for a small problem is given. Some preliminary, exact results on the influences of mutation rates, population sizes and ploidy on the performance of a genetic algorithm in dynamic environments are presented.

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Original languageEnglish
Title of host publicationWorkshop on Evolutionary Algorithms for Dynamic Optimization Problems, 2003 Genetic and Evolutionary Computation COnference (GECCO 2003)
Place of PublicationUnited States, Chicago
Publication statusPublished - 2003


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