%0 Journal Article
%T Estimation of Distribution Algorithm Based on Generic Gaussian Networks<br>建立在一般结构Gauss网络上的分布估计算法
%A Zhong Wei-cai
%A Liu Jing
%A Liu Fang
%A Jiao Li-cheng
%A <br>钟伟才
%A 刘静
%A 刘芳
%A 焦李成
%J 电子与信息学报
%D 2005
%I 
%X 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.
%K Evolutionary computation
%K Estimation of distribution algorithm
%K Gaussian networks<br>进化计算
%K 分布估计算法
%K Gauss网络
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=6ED6D033304526B9&yid=2DD7160C83D0ACED&vid=DB817633AA4F79B9&iid=38B194292C032A66&sid=98494933359B55EC&eid=47F7649551A37CFC&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=6&reference_num=11