An evolutionary algorithm (EA) is developed as an alternative to the EM algorithm for parameter estimation in model-based clustering. This EA facilitates a different search of the fitness landscape, i.e., the likelihood surface, utilizing both crossover and mutation. Furthermore, this EA represents an efficient approach to "hard" model-based clustering and so it can be viewed as a sort of generalization of the k-means algorithm, which is itself equivalent to a restricted Gaussian mixture model. The EA is illustrated on several datasets, and its performance is compared to other hard clustering approaches and model-based clustering via the EM algorithm.

中文翻译：

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基于模型聚类的具有交叉和变异的进化算法

进化算法 (EA) 被开发作为 EM 算法的替代方案，用于基于模型的聚类中的参数估计。该 EA 利用交叉和变异促进了对适应度景观（即似然面）的不同搜索。此外，这个 EA 代表了一种有效的“硬”基于模型的聚类方法，因此它可以被视为 k-means 算法的一种泛化，它本身等效于受限制的高斯混合模型。EA 在几个数据集上进行了说明，并通过 EM 算法将其性能与其他硬聚类方法和基于模型的聚类进行了比较。