mixed-coded evolutionary algorithm for gaussian mixture maximum likelihood clustering with model selection
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.
evolutionary algorithms maximum likelihood clustering