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ＧＡ作为一类随机搜索算法在组合优化、自适应控制、机器学习等许多领域获得了成功的应用。本文采用遗传算法对成组技术中的零件进行近化分簇，提出了两种算法：（１）基于Ｐ－中位模型的遗传算法；（２）基于聚类分析的遗传算法。木文对两种算法进行编程测试，实验结果表明，当以类内样本距离之和最小作为评价准则时，两种算法明显好于Ｋ－平均算法．
As a kind of random search algorithm, GA has been successfully applied in many fields such as combinatorial optimization, adaptive control, machine learning and so on. In this paper, genetic algorithms are used to cluster the components in group technology. Two algorithms are proposed: (1) genetic algorithm based on P-median model; (2) genetic algorithm based on cluster analysis. The two algorithms are programmed and tested in the paper. The experimental results show that the two algorithms are obviously better than the K-means algorithm when the sum of the distances between the samples is the minimum.