Enhancement of continuous estimation of distribution algorithms by density ensembles

In this article, two novel density ensembles methods-the resampling method and the subspaces method-are proposed for enhancing existing continuous Estimation of Distribution Algorithms (EDAs). In resampling continuous EDAs, a population of densities of the selected promising solutions is obtained by iteratively using the resampling operator and the density estimation operator, and new candidate solutions at the next generation are reproduced through sampling from all obtained densities of promising solutions. In subspaces continuous EDAs, a population of densities is obtained by randomly choosing a subset of all variables and estimating the density of all selected high-quality solutions in this subspace. The above steps iterate and many densities of high-quality solutions in different subspaces can be obtained. New candidate solutions at the next generation are reproduced through perturbing the old promising solutions by sampling from the densities in different subspaces. The results upon convergence with different numbers of variables and the effects of parameters on the performance of the density ensembles methods for continuous EDAs are studied based on the experimental results. © 2012 Copyright Taylor and Francis Group, LLC.