Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Learning mixture models using a genetic version of the EM algorithm
Pattern Recognition Letters
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This paper presents a mixed coding evolutionary algorithm for learning Gaussian mixture models. The proposed algorithm can find the optimal number of mixture components in addition to the various mixture parameters that include the mixing probabilities, mean vectors and covariance matrices. This is achieved by devising a mixed-coded genetic algorithm that encodes the mixture parameters into its chromosomes that will undergo different genetic operators that maximize a model-based fitness function. The likelihood of the observed data is maximized while the Akaike Information criterion (AIC) will be used to produce the minimum model structure.