TY  - GEN
AU  - Kramer, Oliver
TI  - Self-adaptive heuristics for evolutionary computation
SN  - 9783540692805
U1  - 006.3 
PY  - 2008///
CY  - New York
PB  - Springer
KW  - Evolutionary programming (Computer science)
KW  - Neural networks (Computer science)
KW  - Evolutionary computation
N2  - 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. (Source: www.alibris.com)
ER  -