%0 Journal Article
%T Hybrid Simplex-improved Genetic Algorithm for Global Numerical Optimization<br>全局数值寻优的一种混合遗传算法
%A REN Zi-Wu
%A SAN Ye
%A CHEN Jun-Feng
%A <br>任子武
%A 伞冶
%A 陈俊风
%J 自动化学报
%D 2007
%I 
%X In this paper, a hybrid simplex-improved genetic algorithm (HSIGA) which combines simplex method (SM) and genetic algorithm (GA) is proposed to solve global numerical optimization problems. In this hybrid algorithm some improved genetic mechanisms, for example, non-linear ranking selection, competition and selection among several crossover offspring, adaptive change of mutation scaling and stage evolution, are adopted; and new population is produced through three ap-proaches, i.e. elitist strategy, modified simplex strategy and improved genetic algorithm (IGA) strategy. Numerical experi-ments are included to demonstrate effectiveness of the proposed algorithm.
%K Genetic algorithm
%K simplex method
%K competition and selection
%K mutation scaling<br>突变标定
%K 运算法则
%K 选择性竞争
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=F06478F1D6F6F866&yid=A732AF04DDA03BB3&vid=27746BCEEE58E9DC&iid=CA4FD0336C81A37A&sid=C753EB8AC8F551B9&eid=6700D0D256586E73&journal_id=0254-4156&journal_name=自动化学报&referenced_num=1&reference_num=12