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基于克服过早收敛的自适应并行遗传算法
引用本文:周远晖,陆玉昌,石纯一.基于克服过早收敛的自适应并行遗传算法[J].清华大学学报(自然科学版),1998(3).
作者姓名:周远晖  陆玉昌  石纯一
作者单位:清华大学,计算机科学与技术系,国家智能技术与系统实验室,北京,100084
摘    要:为了克服遗传算法中存在的主要问题即过早收敛(过早收敛使得一些优秀个体或基因过早地被排除掉,从而导致搜索范围缩小及局部最优,影响了进一步搜索),从控制参数的改进着手,提出了多种群并行进化及自适应调整控制参数相结合的思想。克服了以往定常参数单种群进化的不足,综合了不同特性种群进化的长处,使得过早收敛问题得以缓解,同时又提高了搜索的范围和效率。

关 键 词:过早收敛  多种群进化  自适应参数调整  遗传算法

Adaptive and parallel genetic algorithm based on solving premature convergence
ZHOU Yuanhui,LU Yuchang,SHI Chunyi.Adaptive and parallel genetic algorithm based on solving premature convergence[J].Journal of Tsinghua University(Science and Technology),1998(3).
Authors:ZHOU Yuanhui  LU Yuchang  SHI Chunyi
Institution:ZHOU Yuanhui,LU Yuchang,SHI Chunyi State Key Laboratory of Intelligent Technology and System, Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China
Abstract:Premature convergence is still the preeminent problem in genetic algorithms (GAs). Some excellent individuals or genes are lost due to premature convergence, which causes local optimum. This paper presents an improved genetic algorithm based on multiple populations evolution and adaptive parameter adjusting. Experimental results show that this method alleviates the problem of premature convergence and improves the efficiency of searching.
Keywords:
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