Evolutionary algorithms are successful biologically inspired meta-heuristics. 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: self-adaptation. Their self-adaptive mutation control turned out to be
exceptionally successful. But nevertheless self-adaptation has not achieved the attention it
deserves. This book introduces various types of self-adaptive parameters for evolutionary
computation. Biased mutation for evolution strategies is useful for constrained search spaces.
Self-adaptive inversion mutation accelerates the search on combinatorial TSP-like problems.
After the analysis of self-adaptive crossover operators the book concentrates on premature
convergence of self-adaptive mutation control at the constraint boundary. Besides extensive
experiments statistical tests and some theoretical investigations enrich the analysis of the
proposed concepts.
