%0 Journal Article
%T Optimizing Structure and Connection Weights of Feedforward Neural Networks Using Genetic Algorithms<br>遗传算法优化前向神经网络结构和权重矢量
%A Li Ming
%A Yan Chaohu
%A Liu Gaohang
%A <br>黎明
%A 严超华
%A 刘高航
%J 中国图象图形学报
%D 1999
%I 
%X A new genetic algorithm is proposed to optimize the topology and connection weights for neural networks. The mixed encoding schema of binary and real value code not only retains the advantages of traditional genetic method but also gains the advantages of evolutionary programming and evolution strategies. The offspring generation method which combines the genetic operators and Solis and Wets operator diversifys the search space and speeds up the convergence of genetic search. And the dynamic parameter encoding method for the mixed code can obtain more precise connection weights.
%K Genetic algorithm
%K Neural networks
%K Optimization
%K Evolutionary programming
%K Evolution strategies<br>遗传算法
%K 神经网络
%K 优化
%K 权重矢量
%K 遗传编程
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=E40EE294F39254B6&yid=B914830F5B1D1078&vid=E158A972A605785F&iid=B31275AF3241DB2D&sid=ABF2590617D31FFD&eid=C4490A71BEB872FA&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=18&reference_num=12