计算机工程与应用2011，Vol.47Issue(29)：151-153,221,4.DOI:10.3778/j.issn.1002-8331.2011.29.043
基于K-Means变异算子的混合遗传算法聚类研究
Hybrid genetic algorithm clustering analysis based on K-Means mutation operator
摘要
Abstract
Genetic algorithm has better global search capability,but has the shortcomings of premature convergence and slow end.A-Means algorithm has strong local search ability,but it's sensitive to the initial cluster centers and easy to get stuck at locally optimal value.To solve such problems, it presents a hybrid genetic algorithm based on K-Means mutation operator.It combines the locally searching capability of the K-Means algorithm with the global optimization capability of genetic algorithm, and introduces the K-Means mutation operator into the genetic algorithm.It' s a hybrid algorithm using symbolic coding, adaptive mutation, and optimal individual retention policies.Simulation results show that the algorithm has effectively overcome the slow convergence of genetic algorithm and the locality convergence of AT-Means algorithm,in order to get better clustering.关键词
聚类分析/K-Means算法/K-Means变异算子/遗传算法Key words
cluster analysis/K-Means algorithm/K-Means mutation operator/genetic algorithm分类
信息技术与安全科学引用本文复制引用
耿跃,任军号,吉沛琦..基于K-Means变异算子的混合遗传算法聚类研究[J].计算机工程与应用,2011,47(29):151-153,221,4.基金项目
西咸一体化战略系统研究(No.2010KRM18) （No.2010KRM18）
