关键词: 遗传算法, 精英策略, K-medoids算法, 聚类分析

Abstract: A new elite genetic K-medoids clustering algorithm is proposed to solve the problem of local optimal solution and result instability. The algorithm uses elite strategies to control the overall evolutionary direction of genetic operations, and produces several random individuals based on the average fitness to reduce the impact of premature. At the same time, in order to improve the evolutionary efficiency, the algorithm designs a new crossover method. In order to ensure the superiority of the crossover and mutation, the algorithm introduces a competition mechanism. The simulation results of 8 data sets show that the algorithm improves the stability of clustering results and the accuracy of clustering.

Key words: genetic algorithm, elitist strategy, K-medoids algorithm, cluster analysis