%0 Journal Article
%T GM-EA: guided mutation evolutionary algorithm<br>基于有导向变异算子的GM-EA算法*
%A BI Ying-zhou
%A LU Jian-bo
%A DING Li-xin
%A YUAN Chang-an
%A <br>闭应洲
%A 陆建波
%A 丁立新
%A 元昌安
%J 计算机应用研究
%D 2010
%I 
%X To design a more effective evolutionary algorithm, this paper introduced a new guided mutation evolutionary algorithm by combining Guotao algorithm with the idea from particle swarm optimization, which focused on exploiting the global best solution in population to direct the mutation. In order to preserve the components of building-blocks and avoid the premature problem, separated the search process as the exploration phase and exploitation phase, and in exploitation phase simulated annealing was applied as the replace policy. The experimental results show that the proposed algorithm is significantly superior to Guotao algorithm.
%K guided mutation
%K Guotao algorithm
%K particle swarm optimization(PSO)
%K simulated annealing<br>有导向的变异
%K 郭涛算法
%K 粒子群优化
%K 模拟退火
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=C9170820467626C49DDCE016CE3D7D5D&yid=140ECF96957D60B2&vid=DB817633AA4F79B9&iid=E158A972A605785F&sid=0522AC581E488FBF&eid=10592E9CA5FCF96B&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=10