DocumentCode
2014686
Title
The optimization of genetic algorithm control parameters
Author
Minle, Wang ; Xiaoguang, Gao
Author_Institution
Second Artillery Eng. Inst., Xi´´an, China
Volume
3
fYear
2002
fDate
2002
Firstpage
2504
Abstract
In the paper, methods of optimizing genetic algorithm control parameters are presented, including the method of adjusting crossover probability and mutation probability, the dynamic convergence rule and the method of determining the optimal population size. All the methods can be applied to enhancing genetic algorithm running efficiency and preventing premature convergence.
Keywords
convergence; genetic algorithms; probability; control parameters optimization; crossover probability; dynamic convergence rule; genetic algorithm; mutation probability; optimal population size; running efficiency; Automatic control; Automation; Convergence; Genetic algorithms; Genetic engineering; Genetic mutations; Intelligent control; Optimal control; Optimization methods; Size control;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on
Print_ISBN
0-7803-7268-9
Type
conf
DOI
10.1109/WCICA.2002.1021545
Filename
1021545
Link To Document