%0 Journal Article
%T Co-evolutionary genetic algorithm based on multi-level search area<br>基于多级搜索区域的协同进化遗传算法*
%A MIAO Jin-feng
%A WANG Hong-guo
%A SHAO Zeng-zhen
%A ZHAO Xue-chen
%A <br>苗金凤
%A 王洪国
%A 邵增珍
%A 赵学臣
%J 计算机应用研究
%D 2010
%I 
%X This paper proposed a co-evolutionary genetic algorithm based on multi-level search area to cope with the limitation of traditional multi-population co-evolutionary genetic algorithm, for instance, convergent rate was slow, and computational complexity could not be effectively reduced according to evolutionary process. It put forward a standard which could measure evolutionary stagnate. Divided the search spaces into three levels via clustering, and the algorithm enhanced search granularity for higher levels. As the search spaces were gradually reduced, it improved convergent speed and reduced the complexity of the algorithm. The experimental results indicate that the algorithm is an effective method for solving optimization problems.
%K co-evolutionary
%K multi-level search area
%K genetic algorithm
%K evolutionary stagnate<br>协同进化
%K 多级搜索区域
%K 遗传算法
%K 进化停滞
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=94E8E5E90E506477ABC13754E81F6AA1&yid=140ECF96957D60B2&vid=DB817633AA4F79B9&iid=9CF7A0430CBB2DFD&sid=D3D464E0F6BEA0B6&eid=5CEA67B6F0808E47&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=1&reference_num=13