一种基于自适应遗传算法的聚类分析方法

A Clustering Analysis Approach Using Adaptive Genetic Algorithms

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。

The basic rules and procedures of applying adaptive genetic algorithms (AGA) to clustering analysis are studied. The difference between AGA and standard genetic algorithms (SGA) is that the probabilities of crossover and mutation of the former are varied depending on the fitness values of the solutions.Thus improve the performance of the algorithms.Experimental results show that applying AGA to clustering can accurately locate the clustering centers, and can be used in many fields such as pattern recognition and data compression.