Evolutionary optimization by distribution estimation with mixtures of factor analyzers

Evolutionary optimization algorithms based on the probability models have been studied to capture the relationship between variables in the given problems and finally to find the optimal solutions more efficiently. However, premature convergence to local optima still happens in these algorithms. Many researchers have used the multiple populations to prevent this ill behavior since the key point is to ensure the diversity of the population. In this paper, we propose a new estimation of distribution algorithm by using the mixture of factor analyzers (MFA) which can cluster similar individuals in a group and explain the high order interactions with the latent variables for each group concurrently. We also adopt a stochastic selection method based on the evolutionary Markov chain Monte Carlo (eMCMC). Our experimental results support that the presented estimation of distribution algorithms with MFA and eMCMC-like selection scheme can achieve better performance for continuous optimization problems.