Gaussian process assisted coevolutionary estimation of distribution algorithm for computationally ex

• 作 者: LUO Na QIAN Feng ZHAO Liang ZHONG Wei-min
• 机 构: Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education,
• 出 处: 中南大学学报（英文版）
• 发表日期: 2012年2期
• 关键词: estimation of distribution algorithm fitness function modeling Gaussian process

In order to reduce the computation of complex problems,a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed.Coevolution was used in dual populations which evolved in parallel.The search space was projected into multiple subspaces and searched by sub-populations.Also,the whole space was exploited by the other population which exchanges information with the sub-populations.In order to make the evolutionary course efficient,multivariate Gaussian model and Gaussian mixture model were used in both populations separately to estimate the distribution of individuals and reproduce new generations.For the surrogate model,Gaussian process was combined with the algorithm which predicted variance of the predictions.The results on six benchmark functions show that the new algorithm performs better than other surrogate-model based algorithms and the computation complexity is only 10％ of the original estimation of distribution algorithm.