关键词: 聚类分析, K-Means算法, K-Means变异算子, 遗传算法

Abstract: Genetic algorithm has better global search capability，but has the shortcomings of premature convergence and slow end.K-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 K-Means algorithm，in order to get better clustering.

Key words: cluster analysis, K-Means algorithm, K-Means mutation operator, genetic algorithm