%0 Journal Article
%T Improved Genetic Algorithm for Traveling Salesman Problem<br>一种求解旅行商问题的改进遗传算法
%A ZHANG Jia-Shan
%A WANG Zhi-Hong
%A CHEN Ying-Xian
%A LIN Xiao-Qun
%A <br>张家善
%A 王志宏
%A 陈应显
%A 林晓群
%J 计算机系统应用
%D 2012
%I 
%X Premature convergence usually appears in basic genetic algorithm. So, new crossover and mutation operators are designed. Greedy strategy is introduced in construction of genetic operator. Diversity of population becomes Rich because of introduction of new operators. New algorithm improves the ability of global search. The simulation indicates that the improved genetic algorithm can jump out of local optimum in a short time, and continue seeking the optimum.
%K premature convergence
%K genetic operator
%K global search
%K simulation
%K local optimum<br>早熟
%K 遗传算子
%K 全局搜索
%K 仿真
%K 局部最优
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D4F6864C950C88FFCE5B6C948A639E39&aid=57C6AD9B0ADA05376737ABA68684583C&yid=99E9153A83D4CB11&vid=659D3B06EBF534A7&iid=9CF7A0430CBB2DFD&sid=C29816B2656377A7&eid=61CBE51B4C3C5D55&journal_id=1003-3254&journal_name=计算机系统应用&referenced_num=0&reference_num=10