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
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;
Conference_Titel :
Evolutionary Computation, 2004. CEC2004. Congress on
Print_ISBN :
0-7803-8515-2
DOI :
10.1109/CEC.2004.1330834
