摘要
针对遗传算法局部搜索能力弱的缺陷,提出了一种改进的混合遗传算法。根据遗传的不同阶段分为两个不同的群体——竞争群体和适应性群体,提出相关的遗传算子——繁殖因子。将运筹学中的单纯形法应用于遗传算法中,增强了遗传算法的局部搜索能力。对复杂函数的寻优实验验证了混合遗传算法的有效性,并通过与传统SGA的实算结果对比,更进一步说明了算法的改进效果。
There are some limitations that using genetic algorithms. This paper presents the Mixed Genetic Algorithm(MGA). Based on the phase of genetic, the colonies are differentiated to two different colonies-adaptive population and competing population. The correlated operator, propagate gene, is brought forward. The local search ability of the genetic algorithm is enhenced by using the simplex algorithm of operational research. The emulation experiment data shows the optimizing convergence reliability and higher converging velocity.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2006年第2期232-234,256,共4页
Journal of University of Electronic Science and Technology of China
基金
电子科技大学青年科学基金(YF021405)的资助.
关键词
遗传算法
竞争群体
适应性群体
繁殖因子
单纯形算法
混合遗传算法
genetic algorithm
adaptive population
competing population
propagate gene
simplex algorithm
mixed genetie algorithm