%0 Journal Article
%T A RNA genetic algorithm with entropy based dynamic mutation probability<br>信息熵动态变异概率RNA遗传算法
%A WANG Kang-tai
%A WANG Ning
%A <br>王康泰
%A 王宁
%J 控制理论与应用
%D 2012
%I 
%X The constrained optimization problem becomes a focus and difficulty in science and engineering filed. Inspired by the expression of bio-genetic information of RNA molecular and entropy concept, a RNA genetic algorithm with entropy based dynamic mutation probability (edmp-RGA) is proposed. The algorithm adopts nucleotide base encoding, and RNA recoding operation and protein folding operation are designed to replace the conventional crossover operation. In the algorithm, the values of mutation probability are decided by nucleotide base distribution of the current bits of population. The numerical experiments on four benchmark functions show the effectiveness of the proposed algorithm. The solution to the short-time gasoline blending scheduling problem shows that the proposed algorithm gains a higher profit.
%K RNA genetic algorithm
%K entropy
%K dynamic mutation probability
%K short-time gasoline blending scheduling<br>RNA遗传算法
%K 信息熵
%K 动态变异概率
%K 汽油调合短期调度
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=77408F9630A866172E5E21B6DD126857&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=5D311CA918CA9A03&sid=BF1CF7F9466F9169&eid=7AD2D1CE7CD34BB7&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=0