DocumentCode :
296223
Title :
Towards self-adapting evolution strategies
Author :
Kursawe, Frank
Volume :
1
fYear :
1995
fDate :
Nov. 29 1995-Dec. 1 1995
Firstpage :
283
Abstract :
Optimization algorithms imitating certain principles of nature have proved their capability in various domains of applications. Dealing with parameter optimization problems one usually trades the original problem for a much simpler one, estimating the exogenous parameters of the algorithm chosen to yield a good solution as fast as possible. On the one hand, this paper demonstrates empirically for a small set of test functions, how convergence velocity and reliability of evolution strategies depend on the recombination operator chosen. On the other hand, first results indicate that the capability of self-adaptation within evolution strategies may be exploited in order to reduce the number of exogenous parameters, thus leading to more robust strategies
Keywords :
Algorithm design and analysis; Application software; Computer science; Convergence; Evolutionary computation; Genetic mutations; Parameter estimation; Robustness; Testing; Yield estimation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 1995., IEEE International Conference on
Conference_Location :
Perth, WA, Australia
Print_ISBN :
0-7803-2759-4
Type :
conf
DOI :
10.1109/ICEC.1995.489160
Filename :
489160
Link To Document :
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