中文摘要：

以便减少复杂问题的计算，有 Gaussian 过程的分发算法的一个新帮助代理人的评价被建议。Coevolution 在在平行演变的双人口被使用。搜索空间被投射进多重 subspaces 并且由亚人口寻找了。另外，整个空间被与亚人口交换信息的另外的人口利用。以便使进化功课有效， multivariate Gaussian 模型和 Gaussian 混合模型独立在两张人口被使用估计个人的分发并且复制新一代。为代理人模型， Gaussian 过程与预言了预言的变化的算法被相结合。在新算法比另外的代理人模型更好执行的六基准功能表演的结果基于算法和计算复杂性仅仅是分发的 10% 原来的评价算法。

英文摘要：

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.