%0 Journal Article
	%A Panpan Xu and  Shulin Sui
	%D 2015
	%J International Journal of Mathematical and Computational Sciences
	%B World Academy of Science, Engineering and Technology
	%I International Science Index 108, 2015
	%T Application of Adaptive Genetic Algorithm in Function Optimization
	%U https://publications.waset.org/pdf/10003327
	%V 108
	%X The crossover probability and mutation probability are the two important factors in genetic algorithm. The adaptive genetic algorithm can improve the convergence performance of genetic algorithm, in which the crossover probability and mutation probability are adaptively designed with the changes of fitness value. We apply adaptive genetic algorithm into a function optimization problem. The numerical experiment represents that adaptive genetic algorithm improves the convergence speed and avoids local convergence.

	%P 756 - 759