Self Adaptive Heuristics for Evolutionary Computation is popular PDF and ePub book, written by Oliver Kramer in 2008-10-10, it is a fantastic choice for those who relish reading online the Computers genre. Let's immerse ourselves in this engaging Computers book by exploring the summary and details provided below. Remember, Self Adaptive Heuristics for Evolutionary Computation can be Read Online from any device for your convenience.
Self Adaptive Heuristics for Evolutionary Computation Book PDF Summary
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.
Detail Book of Self Adaptive Heuristics for Evolutionary Computation PDF
- Author : Oliver Kramer
- Release : 10 October 2008
- Publisher : Springer
- ISBN : 9783540692812
- Genre : Computers
- Total Page : 181 pages
- Language : English
- PDF File Size : 13,8 Mb
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