DocumentCode
389729
Title
Theoretical study on diversity of population in parallel genetic algorithms
Author
Pan, Mei Qin ; He, Guo Ping
Author_Institution
Coll. of Inf. Sci. & Eng., Shandong Univ. of Sci. & Technol., China
Volume
1
fYear
2002
fDate
2002
Firstpage
472
Abstract
In this paper, conditional probability density and marginal distribution are proposed as measures of population in genetic algorithms. The influence of selection, crossover and mutation on population distribution is analyzed. In addition, the recursive equations governing population density are derived, and a conclusion of global convergence is also shown.
Keywords
convergence; genetic algorithms; probability; recursive estimation; conditional probability density; crossover; diversity of population; global convergence; marginal distribution; mutation; parallel genetic algorithms; population distribution; recursive equations; selection; Artificial intelligence; Convergence; Density measurement; Educational institutions; Equations; Genetic algorithms; Genetic mutations; Helium; Machine learning; Mathematical model;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on
Print_ISBN
0-7803-7508-4
Type
conf
DOI
10.1109/ICMLC.2002.1176799
Filename
1176799
Link To Document