Title :
The optimization of genetic algorithm control parameters
Author :
Minle, Wang ; Xiaoguang, Gao
Author_Institution :
Second Artillery Eng. Inst., Xi´´an, China
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;
Conference_Titel :
Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on
Print_ISBN :
0-7803-7268-9
DOI :
10.1109/WCICA.2002.1021545
