General stochastic model for algorithm of distribution estimation with conditional probabilities and Gibbs sampling
利用条件概率和Gibbs抽样技术为分布估计算法构造通用概率模型

A stochastic model based on conditional probability and Gibbs sampling is proposed to cope with the modeling problems occurred in traditional algorithms for distribution estimation, and extends the generality of the algorithm. The algorithm with this model takes promised individuals in the evolution process to form supervised training sets. For each of such sets, we estimate the conditional probability of a component given other components, and execute a Gibbs sampling procedure to generate new candidates for replacing inferior ones. The result of computer experiments shows that the improved algorithm can obtain the global optimum of additively decomposed functions, demonstrating a strong ability in global optimization.