Self-Adaptive Heuristics for Evolutionary Computation (Studies in Computational Intelligence, 147)

[LeeAndro]

English | 194 pages | Springer; 2008th edition (August 19, 2008) | 3540692800 | PDF | 4.37 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 strats 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 strats 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 expents, statistical tests and some theoretical investigations enrich the analysis of the proposed concepts.



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