Generalized self-adaptive genetic algorithms

被引：0

作者：

Wu, B ^{[1
]}

Tu, XY

Wu, J

机构：

[1] Univ Sci & Technol Beijing, Informat Engn Sch, Beijing 100083, Peoples R China

[2] SW Inst Technol, Dept Informat & Control Engn, Mianyang 621002, Peoples R China

来源：

JOURNAL OF UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING | 2000年 / 7卷 / 01期

关键词：

generalized self-adaptive; genetic algorithm; initial population; immigration; fitness function;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In order to solve the problem between searching performance and convergence of genetic algorithms, a fast genetic algorithm-generalized serf-adaptive genetic algorithm (GSAGA) is presented. (1) Evenly distributed initial population is generated. (2) Superior individuals are not broken because of crossover and mutation operation for they are sent to subgeneration directly. (3) High quality immigrants are introduced according to the condition of the population schema. (4) Crossover and mutation are operated on self-adaptation. Therefore, GSAGA solves the coordination problem between convergence and searching performance. In GSAGA, the searching performance and global convergence are greatly improved compared with many existing genetic algorithms. Through simulation, the validity of this modified genetic algorithm is proved.

引用

页码：72 / 75

页数：4