摘要
在遗传算法中引入个体学习机制能够提高算法的性能,避免算法收敛过慢或陷入局部最优。常用的个体学习机制有两种,即拉马克学习与鲍德温学习,通过分析比较了两种学习机制在遗传算法中的性能差异,指出了它们各自的优势与不足。为进一步提高算法性能,基于"学习潜能"的新概念及利用鲍德温学习挖掘个体学习潜能的方法,将两种学习机制有机结合在一起,使学习的优势得到充分发挥,使其不足得到有效抑制。数值试验结果表明,包含两种学习机制的新算法取得了很好的效果。
For accelerating the algorithm convergence and avoiding the local optimization, an individual learning mechanism is often applied to generic algorithm to improve algorithm performance. The usual individual learning mechanism includes two sorts： Lamarckian learning and Baldwinina learning. The advantages and disadvantages of both mechanisms are indicated according to their difference performance in the generic algorithm. Additionally, based on a novel concept, named learning potentiality, and the method of digging individual learning potentiality by Baldwinina learning, the Lamarckian learning and Baldwinina learning are appropriately integrated for better algorithm performance so that the advantages of learning could be sufficiently utilized and dis advantages could be effectively forbidden. Numerical experimental results indicate the excellent effectivity of the integrated algorithm.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2009年第8期1985-1989,共5页
Systems Engineering and Electronics
基金
教育部新世纪优秀人才支持计划(NCET-04-0325)资助课题
关键词
计算机工程
遗传算法
个体学习机制
个体学习潜能
拉马克学习
鲍德温学习
computer engineering
genetic algorithm
individual learning mechanism
individual learning potentiality
Lamarckian learning
Baldwinina learning
