Self-Adaptive Heuristics for Evolutionary Computation
Oliver Kramer “Self-Adaptive Heuristics for Evolutionary Computation"
Springer | 2008-09-26 | ISBN: 3540692800 | 182 pages | PDF | 3,35 MB
Springer | 2008-09-26 | ISBN: 3540692800 | 182 pages | PDF | 3,35 MB
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.
–-No mirrors, please–-