SMEM algorithm for mixture models
Proceedings of the 1998 conference on Advances in neural information processing systems II
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Efficient greedy learning of Gaussian mixture models
Neural Computation
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
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This paper presents a kernel density estimation method by means of real-coded crossovers. Estimation of density algorithms (EDAs) are evolutionary optimization techniques, which determine the sampling strategy by means of a parametric probabilistic density function estimated from the population. Real-coded Genetic Algorithm (RCGA) does not explicitly estimate any probabilistic distribution, however, the probabilistic model of the population is implicitly estimated by crossovers and the sampling strategy is determined by this implicit probabilistic model. Based on this understanding, we propose a novel density estimation algorithm by using crossovers as nonparametric kernels and apply this kernel density estimation to the Gaussian Mixture modeling. We show that the proposed method is superior in the robustness of the computation and in the accuracy of the estimation by the comparison of conventional EM estimation.