DocumentCode :
445558
Title :
Adaptive population size for univariate marginal distribution algorithm
Author :
Hong, Yi ; Ren, Qingsheng ; Zeng, Jin
Author_Institution :
Dept. of Comput. Sci. & Eng., Shanghai Jiaotong Univ., China
Volume :
2
fYear :
2005
fDate :
2-5 Sept. 2005
Firstpage :
1396
Abstract :
Population size is an important parameter in univariate marginal distribution algorithm (UMDA). Too large or too small value both goes against its search. A good population sizing method should consider the distribution of individuals. Since the distribution of individuals varies from generation to generation, static population sizing method probably isn´t a good choice. Like Darwinian-type genetic algorithm, UMDA is an intelligent search strategy, it should have the ability to adjust its parameters. In this study, two adaptive population sizing methods are presented for UMDA in continuous domain and in discrete domain respectively. Numerical results show that both of them can get a good balance between convergent velocity and convergent reliability.
Keywords :
convergence; genetic algorithms; search problems; Darwinian-type genetic algorithm; adaptive population size; convergent reliability; convergent velocity; intelligent search strategy; univariate marginal distribution algorithm; Computer science; Costs; Density functional theory; Distributed computing; Frequency estimation; Genetic algorithms; Mathematics; Probability distribution; Search methods; Steady-state;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2005. The 2005 IEEE Congress on
Print_ISBN :
0-7803-9363-5
Type :
conf
DOI :
10.1109/CEC.2005.1554853
Filename :
1554853
Link To Document :
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