DING Weiping, WANG Jiandong, GUAN Zhijin. A Novel Minimum Attribute Reduction Algorithm Based on Hierarchical Elitist Role Model Combining Competitive and Cooperative Co-evolution[J]. Chinese Journal of Electronics, 2013, 22(4): 677-682.
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[J]. Chinese Journal of Electronics, 2013, 22(4): 677-682.

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

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
  • 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.
  • loading
  • Z. Pawlak, "Rough set", International Journal of Computer and Information Science, Vol.11, No.5, pp.341-356, 1982.
    R.W. Swiniarski, A. Skowron, "Rough set methods in features election and recognition", Pattern Recognition Letters, Vol.24,N o.6, pp.833-849, 2003.
    G.L. Liu, "Rough set theory based on two universal sets and itsa pplication", Knowledge-Based Systems, Vol.23, No.2, pp.110-1 15, 2010.
    Y.Y. Yao, Y. Zhao, "Attribute reduction in decision-theoreticr ough set models", Information Sciences, Vol.178, No.17,p p.3356-3373, 2008.
    S.K.M. Wong, W. Ziarko, "On optimal decision rules in decisiont ables", Bulletin of Polish Academy of Science, Vol.33, No.11,p p.693-696, 1985.
    D. Slezak, J. Wroblewski, "Order based genetic algorithmsf or the search of approximate entropy reducts", Proceeding ofR SFDGr C2003, LNAI 2639, Chongqing, China, pp.308-311,2 003.
    Y.M. Chen, D.Q. Miao, R.Z. Wang, "A rough set approach tof eature selection based on ant colony optimization", PatternR ecognition Letters, Vol.31, pp.226-233, 2010.
    X.Y. Wang, J. Yang, X. Teng, W. Xia, R. Jensen, "Feature se-l ection based on rough sets and particle swarm optimization", Pattern Recognition Letters, Vol.28, No.4, pp.459-471, 2007.
    C. Bae, W.C. Yeh, Y.Y. Chu, "Feature selection with Intelligentd ynamic swarm and rough set", Expert Systems with Applications, Vol.37, No.10, pp.7026-7032, 2010.
    M.A. Potter, K.A. De Jong, "Cooperative coevolution: an archi-tecture for evolving coadapted subcomponents", Evolutionary Computation, Vol.8, No.1, pp.1-29, 2000.
    N. García-Pedrajas, C. Hervás-Martínez, J. Muz Pérez, "COV-N ET: a cooperative coevolutionary model for evolving articialn eural networks", IEEE Trans. on Neural Networks, Vol.14,N o.3, pp.575-596, 2003.
    K. Maneeratana, K. Boonlong, N. Chaiyaratana, "Co-operativec o-evolutionary genetic algorithms for multi-objective topologyd esign", Computer-Aided Design & Applications, Vol.2, No.1-4,p p.487-496, 2005.
    C.K. Goh, K.C. Tan, "A competitive-cooperative coevolution-a ry paradigm for dynamic multi objective optimization", IEEET ransactions on Evolutionary Computation, Vol.13, No.1,p p.103-127, 2009.
    J.W. Gu, M.Z. Gu, C.W. Cao, X.S. Gu, "A novel competitivec o-evolutionary quantum genetic algorithm for stochastic jobs hop scheduling problem", Computer & Operation Research,Vol.37, No.5, pp.927-937, 2010.
    J.H. Wu, J. Zhang, X.G. Zhang, Z.H. Liu, "Hierarchical co-e volution immune algorithm and its application on TSP", ActaE lectronica Sinica, Vol.39, No.2, pp.336-344, 2011. (in Chinese)
    L.C. Jiao, Y.Y. Li, M.G. Gong, X.R. Zhang, "Quantum-inspiredi mmune clonal algorithm for global optimization", IEEE Transactions on System, Man, and Cybernetics, Part B, Vol.38, No.5,p p.1234-1253, 2008.
    A. Asuncion, D.J. Newman, UCI Repository of Machine Learn-i ng Databases, http://www.ics.uci.edu/籱learn/mlrepository.h tml, Irvine, CA: University of California, Dept. of Information and Computer Science, 2007-06-15.
    G.I. Webb, J.R. Boughton, Z.J. Wang, "Not so naive Bayes:A ggregating one-dependence estimators", Machine Learning,Vol.58, No.1, pp.5-24, 2005.
    I.W. Tsang, J.T. Kwok, P.M. Cheung, "Core vector machines:F ast SVM training on very large data sets", Journal of MachineL earning Research, Vol.6, pp.363-392, 2005.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (360) PDF downloads(1216) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return