DocumentCode
418995
Title
Evolution to the Xtreme: evolving evolutionary strategies using a meta-level approach
Author
Deugo, Dwight ; Ferguson, Darrell
Author_Institution
Sch. of Comput. Sci., Carleton Univ., Ottawa, Ont., Canada
Volume
1
fYear
2004
fDate
19-23 June 2004
Firstpage
31
Abstract
In this paper we describe a meta-level evolutionary system that uses a meta-level GA to evolve strategies that perform better than known good strategies on a test bed of mathematical optimization problems. We examine the effects of the meta-level components and parameters on the problem set in order to help others in choosing the components and parameters for their meta-GAs.
Keywords
genetic algorithms; mathematical analysis; Xtreme; evolving evolutionary strategies; genetic algorithm; mathematical optimization problems; metaGA; metalevel components; metalevel evolutionary system; metalevel parameters; natural genetics; natural selection; Algorithm design and analysis; Buildings; Computer science; Drives; Evolutionary computation; Genetic algorithms; Genetic mutations; Neural networks; Performance evaluation; System testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2004. CEC2004. Congress on
Print_ISBN
0-7803-8515-2
Type
conf
DOI
10.1109/CEC.2004.1330834
Filename
1330834
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