DocumentCode
2998538
Title
Diversity analysis in cellular and multipopulation genetic programming
Author
Folino, G. ; Pizzuti, C. ; Spezzano, G. ; Vanneschi, L. ; Tomassini, M.
Author_Institution
ICAR-CNR, Rende, Italy
Volume
1
fYear
2003
fDate
8-12 Dec. 2003
Firstpage
305
Abstract
This paper presents a study that evaluates the influence of the parallel genetic programming (GP) models in maintaining diversity in a population. The parallel models used are the cellular and the multipopulation one. Several measures of diversity are considered to gain a deeper understanding of the conditions under which the evolution of both models is successful. Three standard test problems are used to illustrate the different diversity measures and analyze their correlation with performance. Results show that diversity is not necessarily synonym of good convergence.
Keywords
convergence; genetic algorithms; parallel algorithms; statistical analysis; cellular genetic programming; convergence; diversity analysis; diversity measures; evolution; multipopulation genetic programming; parallel genetic programming model; population diversity; Computer science; Convergence; Costs; Evolutionary computation; Genetic mutations; Genetic programming; Measurement standards; Performance analysis; Size measurement; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2003. CEC '03. The 2003 Congress on
Print_ISBN
0-7803-7804-0
Type
conf
DOI
10.1109/CEC.2003.1299589
Filename
1299589
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