Generalized Self-Adaptive Genetic Algorithms
In order to solve the problem between searching performance and convergence of genetic algorithms, a fast genetic algorithm-generalized self-adaptive genetic algorithm (GSAGA) is presented. (1) Evenlydistributed initial population is generated. (2) Superior individualsare 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.
作 者： Bin Wu Xuyan Tu Jian Wu 作者单位： Bin Wu(Information Engineering School, University of Science and Technology Beijing, Beijing 100083, China;Department of Information and Control Engineering,Southwest Instit ute of Technology,Mianyang 621002,China)Xuyan Tu(Information Engineering School, University of Science and Technology Beijing, Beijing 100083, China)
Jian Wu(Department of Information and Control Engineering,Southwest Instit ute of Technology,Mianyang 621002,China)
刊 名： 北京科技大学学报（英文版） EI SCI 英文刊名： JOURNAL OF UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING 年，卷(期)： 2000 7(1) 分类号： O22 关键词： generalized self-adaptive genetic algorithm initial population immigration fitness function