Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (36): 34-36.DOI: 10.3778/j.issn.1002-8331.2010.36.009

• 研究、探讨 • Previous Articles     Next Articles

Research on adaptive genetic algorithm injected learning mechanism

ZHU Yan-guang,XU Yong-ping,ZHOU Xuan,ZHU Yi-fan   

  1. School of Information System and Management,National University of Defense Technology,Changsha 410073,China
  • Received:2010-03-15 Revised:2010-05-11 Online:2010-12-21 Published:2010-12-21
  • Contact: ZHU Yan-guang

引入学习机制的自适应遗传算法设计与实现

朱延广,许永平,周 旋,朱一凡   

  1. 国防科技大学 信息系统与管理学院,长沙 410073
  • 通讯作者: 朱延广

Abstract: The genetic operator is an important factor affecting the optimization effectiveness of genetic algorithm.However,among all the research of genetic algorithm,the potential evolutional rule under all optimal individuals is not being fully utilized yet.Thus,an adaptive operator is designed to meet this shortfall,and combination with existing improved genetic algorithm,a design process of genetic algorithm applying the adaptive operator is presented.Finally,two universal test functions are used to demonstrate the capability of the improved algorithm.The result indicates that the adaptive operator can achieve better convergence speed and optimum solution of genetic algorithm with the same parameters.

Key words: learning mechanism, genetic algorithm, adaptive operator, adaptive genetic algorithm

摘要: 目前遗传算法研究中,缺乏对历代群体进化规律的充分利用,因此引入学习机制,设计反映个体自主学习进化规律的自适应算子,并且结合现有的改进遗传算法,提出一种新的自适应遗传算法。最后以两个通用的测试函数为例对算法进行性能测试,结果表明,在采用相同参数的条件下,自适应算子能够以较低的代价提高遗传算法的收敛速度,并获得更好的最终优化结果。

关键词: 学习机制, 遗传算法, 自适应算子, 自适应遗传算法

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