Cluster analysis is a technique that is used to discover patterns and associations within data. One of the major problems is that different clustering methods can form different solutions for the same dataset in cluster analysis. Therefore, this study aimed to provide optimal clustering of units by using a genetic algorithm. To this end, a new fitness function was defined by adding the silhouette function that shows the units are in the correct clusters, to the fitness function, which minimizes the ratio of intra-cluster distances to inter-cluster distances. This algorithm was supported by simulation studies and tried on real data. The results of the analysis showed that this algorithm could generate better clustering results than some other clustering algorithms. Hence, in this algorithm, the use of fitness function ensured convergence to the global optimum.

中文翻译：

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具有新适应度函数的遗传算法进行聚类

聚类分析是一种用于发现数据中的模式和关联的技术。主要问题之一是在聚类分析中，不同的聚类方法可以为同一数据集形成不同的解决方案。因此，本研究旨在通过使用遗传算法提供最佳的单元聚类。为此，通过将表示单位在正确簇中的轮廓函数添加到适应函数中，从而定义了新的适应度函数，该函数使簇内距离与簇间距离之比最小。该算法得到了仿真研究的支持，并在实际数据上进行了尝试。分析结果表明，与其他聚类算法相比，该算法可以产生更好的聚类结果。因此，在此算法中