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基于生态竞争模型的遗传强化学习
引用本文:曹先彬,高 隽,王煦法.基于生态竞争模型的遗传强化学习[J].软件学报,1999,10(6):658-662.
作者姓名:曹先彬  高 隽  王煦法
作者单位:中国科学技术大学计算机科学与技术系,合肥,230027;合肥工业大学计算机与信息系,合肥,230009;中国科学技术大学计算机科学与技术系,合肥,230027
基金项目:本文研究得到国家自然科学基金和安徽省摼盼鍞重点攻关项目基金资助.
摘    要:未成熟收敛和收敛速度慢是目前遗传算法的明显缺点.借鉴生物在环境生态系统中的生长模式,文章提出一种生态竞争模型.该模型认为,竞争行为在生物的成长中占有十分重要的地位,在子群内实现了个体层次的先天遗传进化和后天竞争学习,在种群层次实现进一步的竞争强化学习.实验结果显示了该模型在解决收敛性问题时的有效性.

关 键 词:遗传算法  收敛性  生态竞争  强化学习  函数优化.
收稿时间:1998/4/20 0:00:00
修稿时间:1998/6/29 0:00:00

An Ecological Competition Model for Genetic Reinforcement Learning
CAO Xian-bin,GAO Jun and WANG Xu-fa.An Ecological Competition Model for Genetic Reinforcement Learning[J].Journal of Software,1999,10(6):658-662.
Authors:CAO Xian-bin  GAO Jun and WANG Xu-fa
Affiliation:CAO Xianbin1GAO Jun2WANG Xufa1 1(Department of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefei230027) 2(Department of Computer and InformationHefei University of TechnologyHefei230009)
Abstract:Premature convergence and low converging speed are the distinct weaknesses of the genetic algorithms. Using the living things' growth pattern for reference, a new model called ECM(ecological competition model) is proposed, in which the competition is considered to be in important position. In the ECM model, the congenital genetic evolution and the postnatal competition learning on individuals' level are realized in each sub-population, moreover, the competition reinforcement learning on population level is realized. The experimental results show the ECM model's effectiveness.
Keywords:Genetic algorithms  convergence  ecological competition  reinforcement learning  function optimization  
本文献已被 CNKI 维普 万方数据 等数据库收录!
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