A Novel Minimum Attribute Reduction Algorithm Based on Hierarchical Elitist Role Model Combining Competitive and Cooperative Co-evolution  

作　　者：DING Weiping WANG Jiandong GUAN Zhijin 

机构地区：[1]College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China [2]School of Computer Science and Technology, Nantong University, Nantong 226019, China [3]State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China

出　　处：《Chinese Journal of Electronics》2013年第4期677-682,共6页电子学报（英文版）

基　　金：This work is supported by the National Natural Science Foundation of China （No.61139002, No.61171132）, the Open Project Program of State Key Laboratory for Novel Software Technology, Nanjing Uni- versity （No.KFKT2012B28）, the Natural Science Foundation of Jiangsu Education Department （No.12KJB520013）, the Fundamental Research Funds for the Central Universities and the Funding of Jiangsu Innovation Program for Graduate Education （No.CXZZ11_0219）, the Applying Study Foundation of Nantong （No.BK2011062）, and the Natural Science Pre-Research Foundation of Nantong University （No.12ZY016）.

摘　　要：Minimum attribute reduction in rough set theory is an NP-hard problem, which is difi^cult to use traditional evolution methods to solve. In this pa- per, a novel and efficient minimum Attribute reduction algorithm （named HERCo2AR） based on Hierarchical eli- tist role model combining competitive and cooperative co- evolution is proposed. Through such an iterative process of competitive and cooperative co-evolution, the various subpopulations are better optimized by different elitists, and reasonable decomposition of interacting attribute sets can co-adapt to emerge due to the evolutionary pressure of hierarchical elitist role. The hierarchical elitist role model is very effective in the protection and promotion of out- standing individuals, and it can accelerate to direct the global optimal attribute reduction. Experimental results demonstrate that HERCo2AR achieves the better feasibil- ity and effectiveness than existing state-of-the-art attribute reduction algorithms, and the quality of the global optimal solution can be significantly improved as well.

关 键 词：Minimum attribute reduction  Hierar-chical elitist role model Competitive and cooperative co-evolution Rough set theory. 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] Q111[自动化与计算机技术—控制科学与工程]