%0 Journal Article
%T Improved genetic algorithm based on growth operator and simulation<br>基于成长算子的改进遗传算法及仿真
%A YAN Jing-yu
%A SUN De-min
%A LING Qing
%A <br>阎镜予
%A 孙德敏
%A 凌青
%J 控制理论与应用
%D 2006
%I 
%X By emulating the process of growth in nature and using growth operator, a growth genetic algorithm (GGA) is proposed to overcome the drawbacks of simple GA (SGA) such as slow optimization speed and weak local search ability. A practical realization of growth operator is proposed by making use of the strong local search ability of the hill climbing method. It has been demonstrated that adding the growth operator doesn't change the convergence property of SGA. The simulation result compared with SGA and deterministic crowding GA (DCGA) for function optimization verifies that the growth genetic algorithm facilitates the balance between optimization speed and convergence precision.
%K growth genetic algorithm
%K growth operator
%K convergence property
%K function optimization<br>成长遗传算法
%K 成长算子
%K 收敛性
%K 函数优化
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=68C347B7B5FD05BB&yid=37904DC365DD7266&vid=EA389574707BDED3&iid=94C357A881DFC066&sid=07C52AC66061533A&eid=F2947E14627CD734&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=1&reference_num=9