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This paper introduces an evolutionary algorithm, named ESM-EM (Evolutionary Split & Merge for Expectation Maximization), for estimating both the number of components and parameters of Gaussian Mixture Models. ESM-EM is based on splitting and merging operations, which are applied to the components of the mixture model. By combining such operations with random search procedures, an evolutionary algorithm potentially capable of escaping from local optima can be designed. In our experiments, we compare ESM-EM with a widely used approach that consists of getting a set of mixture models with different numbers of components and then selecting the model that provides the best result according to a particular model selection criterion. The results of the performed experiments show that ESM-EM provides best results (in terms of the Minimum Description Length principle) in seven out of the eight assessed datasets.