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
Link To Document :
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