000			01770 a2200217 4500
008			140323b2008 xxu||||| |||| 00| 0 eng d
020			_a9783540692805
082			_a006.3
100			_aKramer, Oliver _956907
245			_aSelf-adaptive heuristics for evolutionary computation _cKramer, Oliver
260			_aNew York _bSpringer _c2008
300			_axii, 181 p.
365			_bEUR 99.95
440			_aStudies in computational intelligence, vol. 147 _956908
520			_aEvolutionary 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. (Source: www.alibris.com)
650			_aEvolutionary programming (Computer science)
650			_aNeural networks (Computer science)
650			_aEvolutionary computation.
942			_cBK
999			_c366322 _d366322