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

DING Weiping; WANG Jiandong; GUAN Zhijin

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Article Navigation > Chinese Journal of Electronics > 2013 > 22(4): 677-682

DING Weiping, WANG Jiandong, GUAN Zhijin, “A Novel Minimum Attribute Reduction Algorithm Based on Hierarchical Elitist Role Model Combining Competitive and Cooperative Co-evolution,” Chinese Journal of Electronics, vol. 22, no. 4, pp. 677-682, 2013,

Citation:

DING Weiping, WANG Jiandong, GUAN Zhijin, “A Novel Minimum Attribute Reduction Algorithm Based on Hierarchical Elitist Role Model Combining Competitive and Cooperative Co-evolution,” Chinese Journal of Electronics, vol. 22, no. 4, pp. 677-682, 2013,

Citation:

DING Weiping, WANG Jiandong, GUAN Zhijin, “A Novel Minimum Attribute Reduction Algorithm Based on Hierarchical Elitist Role Model Combining Competitive and Cooperative Co-evolution,” Chinese Journal of Electronics, vol. 22, no. 4, pp. 677-682, 2013,

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

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

Funds: 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 University (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).

More Information

Corresponding author: DING Weiping
Received Date: 2012-08-01
Rev Recd Date: 2012-11-01
Publish Date: 2013-09-25

Abstract

Abstract

Minimum attribute reduction in rough set theory is an NP-hard problem, which is difficult to use traditional evolution methods to solve. In this paper, a novel and efficient minimum Attribute reduction algorithm (named HERCo²AR) based on Hierarchical elitist role model combining competitive and cooperative coevolution is proposed. Through such an iterative process of competitive and cooperative coevolution, the various subpopulations are better optimized by different elitists, and reasonable decomposition of interacting attribute sets can coadapt 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 outstanding individuals, and it can accelerate to direct the global optimal attribute reduction. Experimental results demonstrate that HERCo²AR achieves the better feasibility and effectiveness than existing state-of-the-art attribute reduction algorithms, and the quality of the global optimal solution can be signi-cantly improved as well.
- Minimum attribute reduction,
- Hierarchical elitist role model,
- Competitive and cooperative coevolution,
- Rough set theory

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References(19)

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