摘要
针对普通遗传算法(CGA)易陷入早熟,局部搜索能力较差,全局优化速度缓慢等问题,提出了一种改进的遗传算法(IM_GA),该算法融合了由进化代数或适应度分布调节变异交叉率的思想,从这两个方面共同改进了变异交叉率,仿真结果证明了该改进遗传算法的优越性.与普通标准遗传算法比较,该算法不仅收敛性较好,且能迅速找到全局最优解.
Common genetic algorithm（CGA） may have a tendency to converge towards local optima,and have the characteristics of poor local searching ability and slow global convergence.In order to solve these problems,this paper proposes an improved genetic algorithm（IM_GA）.This IM_GA combines the idea that evolutionary times and fitness distribution adjust the crossover probability and mutation probability.The simulation results demonstrate that the IM_GA is superior to CGA.Compared to CGA,IM_GA not only have better searching abilities,but can also converge to global optima quickly.
出处
《天津理工大学学报》
2010年第4期43-47,共5页
Journal of Tianjin University of Technology
关键词
改进遗传算法
二进制编码
适应度函数
交叉率
变异率
improved genetic algorithm
binary encoding
fitness
crossover rate
mutation rate
