计算机与现代化 ›› 2013, Vol. 1 ›› Issue (1): 25-28.doi:

• 算法设计与分析 • 上一篇    下一篇

基于蚁群遗传算法的属性约简

夏先智,杜新宇,郑扬飞
  

  1. 华北计算技术研究所公共安全信息化事业部,北京100083
  • 收稿日期:2012-08-15 修回日期:1900-01-01 出版日期:2013-02-06 发布日期:2013-02-06

Attribute Reduction Based on Ant Colony Genetic Algorithm

XIA Xianzhi, DU Xinyu, ZHENG Yangfei
  

  1. Dept. of Public Security Informatization Sector, North China Institute of Computing Technology, Beijing 100083, China
  • Received:2012-08-15 Revised:1900-01-01 Online:2013-02-06 Published:2013-02-06

摘要:

针对普通蚁群算法在属性约简中求解最小约简存在局部最优、迭代次数多、收敛慢的问题,将复制、交叉、变异这些遗传算子引入蚁群算法中,改进蚂蚁的产生方式和蚂蚁构造可行解的过程,提高算法的收敛速度和全局搜索能力。算法在加州大学机器学习数据库中的数据集的测试结果表明,该算法能快速有效地求解属性约简,能够找到最小约简集。

关键词: 关键词:遗传算法, 蚁群算法, 属性约简, 粗糙集

Abstract:

As for the ordinary ant colony algorithm for attribute reduction, which has the problems such as local minima, many iterations and slow convergence, this paper proposes the ant colony genetic algorithm that takes copy, crossover and mutation of genetic operators to ant colony algorithm, which can improve the generation of ants and the process of the feasible solution, to improve global search capability. The algorithm is validated on data sets of UCI machine learning database from the University of California. The results show that the algorithm can quickly and efficiently solve the attribute reduction, to be able to find the minimal reduction set.

Key words: Key words: genetic algorithm, ant colony algorithm, attribute reduction, rough set