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本文研究了基于自适应遗传算法进行聚类分析的基本原理和实现方法。自适应遗传算法不同于一般遗传算法之处是其交叉互换率与突变率这两个参数随串的适应度值而变化，极大地增强了算法的性能。实验结果表明，遗传算法应用于聚类分析能够搜索到更为精确的聚类中心值，在模式识别、数据压缩等领域有着广泛的应用前景。
This paper studies the basic principle and implementation method of clustering analysis based on adaptive genetic algorithm. Different from the general genetic algorithm, adaptive genetic algorithm is different from the traditional genetic algorithm in that the crossover rate and the mutation rate change with the fitness value of the string, which greatly enhances the performance of the algorithm. The experimental results show that the genetic algorithm can be applied to clustering analysis to search for more accurate clustering center values and has wide application prospects in the field of pattern recognition and data compression.