| 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. |
| DOI： |
| 分类号: |
| 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. |
|
| An Ecological Competition Model for Genetic Reinforcement Learning |
|
CAO Xian-bin,GAO Jun,WANG Xu-fa
|
| 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. |
| Key words: Genetic algorithms, convergence, ecological competition, reinforcement learning, function optimization. |
