Estimation of Distribution Algorithm Based on Generic Gaussian Networks
建立在一般结构Gauss网络上的分布估计算法

Estimation of Distribution Algorithms (EDAs) available in continuous domains are based on non-generic Gaussian networks. The computational cost for learning this kind of networks is very great, moreover the low accuracy of the joint pdf will be resulted because the greedy algorithm is used to learn the Gaussian networks. To overcome these disadvantages, an Estimation of Distribution Algorithm based on generic Gaussian Networks (GN-EDA) is presented. It leads to the low computational cost by no structure learning of Gaussian networks. In the meanwhile, a generic Gaussian network is not an approximate one, so the joint pdf is of high accuracy. Due to an effective sampling is adopted, the computational cost for parameters learning is great reduced. The experimental results show that GN-EDA achieves a more stable performance and a stronger ability in searching the global optima.