摘要
提出了一种基于欧式距离约束的自适应遗传算法(Euclidean distance-Adaptive Genetic Algorithm,EAGA),该算法将欧式距离引入自适应交叉概率,使交叉概率随适应度和个体之间的相似度自适应变化,更好地增强种群的多样性,保存优良个体;为了防止EAGA在优化过程中出现退化现象,通过融合按照一定规则产生的新个体对算法进行了改进。采用EAGA选择最优参差比,使滤波器的零点尽可能的浅,在有效抑制杂波的同时避免目标丢失。同标准遗传算法相比,EAGA表现出了较好的搜索性能。
A novel adaptive genetic algorithm based on Euclidean distance is presented.By applying Euclidean distance into the adaptive crosser operator,this algorithm can better strengthen the diversity of population and preserve the desirable individuals.Some improvements are also realized to prevent degeneration in the process of optimization by introducing new individuals generated by certain rules into the group.Compared with SGA,EAGA shows a better global search capacity.
出处
《电子科技》
2009年第11期23-27,共5页
Electronic Science and Technology
基金
重点实验室基金资助项目(ZZ0303072207)
关键词
遗传算法
欧式距离
自适应控制
参差码
genetic algorithm
Euclidean distance
adaptive control
staggered code
