Analysis of classification learning based on estimation of distribution algorithms

Estimation of distribution algorithms (abbr. EDAs) is a relatively new branch of evolutionary algorithms. EDAs replace search operators with the estimation of the distribution of selected individuals + sampling from the population. In an EDAs, this explicit representation of the population is replaced with a probability distribution over the choices available at each position in the vector that represents a population member. In this paper, the explicit probability basis about the semi-supervised learning and unsupervised learning is analyzed, and the mathematical properties analysis of the implicit EDAs learning algorithms is provided.