Modeling evolutionary algorithms for dynamic and stochastic optimization

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The aim of this research is to study and model the behavior of evolutionary algorithms (EA) in stochastic and dynamic environments, with an eye to better understand and so better control evolutionary behavior under these environments. The models used in this research are based on statistically significant observations of production-level EA systems as opposed to a bottom-up analysis of the mathematical (probabilistic) properties of a very simplified EA. Furthermore they are not just performance (output) models of the EA but also comprise the properties that make up the state and dynamics of the EA within a complex environment.