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Evolutionary algorithms are successful biologically inspired metaheuristics. Their success depends on adequate parameter settings. The question arises: how can evolutionary algorithms learn parameters automatically during the optimization? Evolution strategies gave an answer decades ago: selfadaptation. Their selfadaptive mutation control turned out to be exceptionally successful. But nevertheless selfadaptation has not achieved the attention it deserves.This book introduces various types of selfadaptive parameters for evolutionary computation. Biased mutation for evolution strategies is useful for constrained search spaces. Selfadaptive inversion mutation accelerates the search on combinatorial TSPlike problems. After the analysis of selfadaptive crossover operators the book concentrates on premature convergence of selfadaptive mutation control at the constraint boundary. Besides extensive experiments, statistical tests and some theoretical investigations enrich the analysis of the proposed concepts.
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