摘要
针对函数优化问题 ,提出一种自适应变异遗传算法来提高局部搜索的能力 ,弥补简单遗传算法易于早熟收敛的缺陷。最后以DeJong函数为仿真对象 ,将此算法与其它三种遗传算法进行比较 ,仿真结果表明此算法对于函数优化问题非常有效 ,大大加快了算法的收敛速度 ,并大幅度提高了搜寻到最优解的概率。
An adaptive mutation probability genetic algorithm(AMGA) was presented to improve the local search ability of traditional genetic algorithms. Two simulations of De Jong function were given, which illustrate that AMGA is available for function optimization through comparing with another three improved GA.
出处
《北京化工大学学报（自然科学版）》
CAS
CSCD
2003年第6期80-83,共4页
Journal of Beijing University of Chemical Technology(Natural Science Edition)
关键词
自适应变异遗传算法
函数优化
基因位的依赖性
变异概率
adaptive mutation GA
function optimization
dependence of gene bit
mutation probabilities
