ene Expression Programming for Attribution Reduction in Rough Set on Grid Environment

doi:10.1016/S1005-8885(09)60457-X

中国邮电高校学报(英文) ›› 2010, Vol. 17 ›› Issue (2): 122-126.doi: 10.1016/S1005-8885(09)60457-X

ene Expression Programming for Attribution Reduction in Rough Set on Grid Environment

邓松¹,付雄¹,王汝传²

1. 南京邮电大学
2. 南京邮电大学计算机学院

收稿日期:2009-02-15 修回日期:2009-10-12 出版日期:2010-04-30 发布日期:2010-06-01
通讯作者: 王汝传 E-mail:wangrc@njupt.edu.cn
基金资助:
国家级.国家自然科学基金;国家级.国家“863计划”项目

网格环境下基于基因表达式编程的粗糙集属性约简算法

Received:2009-02-15 Revised:2009-10-12 Online:2010-04-30 Published:2010-06-01

摘要/Abstract

摘要：

本文提出基于基因表达式编程的粗糙集属性约简算法(GEP-ARRS)，并针对属性约简的特点，设计了新的GEP编码形式，它可以很好地把属性约简问题编码成GEP能处理的表达树，同时设计了反映属性约简的新适应度函数。为了更快地求解出最优约简，GEP-ARRS算法利用动态种群生成策略，不断缩小GEP基因长度，加快GEP算法的求解速度。比较实验表明，无论对于大数据集或者高维数据集而言，GEP-ARRS算法比传统的基于智能计算的属性约简算法在求解属性约简的速度和质量上都有着明显的优势。

关键词:

智能计算

Abstract:

This paper presents gene expression programming for attribution reduction in rough set (GEP-ARRS), which designs a new gep code to convert attribution reduction into expression tree and a new fitness function. Meanwhile, to solve optimal reduction quickly, GEP-ARRS implements dynamic population creation strategy to reduce gene length of GEP constantly in order to accelerate solution efficiency of GEP. By extensive experiments, it is showed that for mass or high dimension data sets, in the matter of speed and quality which solves attribution reduction, GEP-ARRS has more apparent advantage by contrast with traditional attribution reduction algorithm on intelligence computing.

Key words:

intelligence computing

参考文献

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ene Expression Programming for Attribution Reduction in Rough Set on Grid Environment

网格环境下基于基因表达式编程的粗糙集属性约简算法

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