文献类型：期刊文献

中文题名：基于递进制编码的遗传算法研究

英文题名：Research on Genetic Algorithm Based on Degressive Carry Number Encoding

作者：刘漫丹[1];钱锋[1]

机构：[1]华东理工大学自动化研究所,上海200237

年份：2004

卷号：33

期号：5

起止页码：614

中文期刊名：信息与控制

外文期刊名：Information and Control

收录：CSTPCD;;Scopus;北大核心:【北大核心2000】；CSCD:【CSCD2011_2012】；

基金：国家 973计划资助项目 ( 2 0 0 2CB3 12 2 0 0 );上海市"启明星"计划资助项目 ( 0 3QG14 0 14 )

语种：中文

中文关键词：遗传算法;递进制;编码;搜索能力

外文关键词：genetic algorithm; degressive mechanism; encoding; searching ability

摘要：首先分析了编码的进制数对遗传算法收敛速度的影响 .分析结果表明 ,当种群不稳定时 ,高进制编码较低进制编码具有更快地搜索至最优区域的能力 ,当种群较为稳定时 ,低进制编码较高进制编码具有更快地逼近最优点的能力 .然后提出了基于递进制的遗传算法 ,该算法能提高优化问题的收敛速度 ,在优化参数较多时 ,与单一进制编码的遗传算法相比具有明显的优势 .仿真实例也验证了这一结论 .
The influence of encoding mechanism on the convergence of genetic algorithm is analyzed. High carry number encoding has the ability of faster searching to optimization area, compared with low carry number encoding while the population is unstable. Low carry number encoding has the ability of faster searching to optimization value, compared with high carry number encoding while the population is stable. Then, the genetic algorithm based on degressive carry number encoding is proposed. The algorithm can improve the convergence speed of optimization problems, and this advantage is obvious while the parameters are excessive. Simulations validate the conclusion.