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[1] Ding Weiping, Wang Jiandong, Shi Quan, et al. Adaptive multicascade attribute reductionbased on quantum-inspired mixed co-evolution [J]. Journal of Southeast University (English Edition), 2012, 28 (2): 145-150. [doi:10.3969/j.issn.1003-7985.2012.02.003]
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Adaptive multicascade attribute reductionbased on quantum-inspired mixed co-evolution()
基于量子混合协同进化的自适应多级联属性约简
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
28
Issue:
2012 2
Page:
145-150
Research Field:
Computer Science and Engineering
Publishing date:
2012-06-30

Info

Title:
Adaptive multicascade attribute reductionbased on quantum-inspired mixed co-evolution
基于量子混合协同进化的自适应多级联属性约简
Author(s):
Ding Weiping1;  2;  Wang Jiandong1;  Shi Quan2;  Guan Zhijin2
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
丁卫平1;  2;  王建东1;  施佺2;  管致锦2
1 南京航空航天大学计算机科学与技术学院, 南京 210016; 2南通大学计算机科学与技术学院, 南通 226019
Keywords:
attribute reduction;  mixed co-evolution;  self-adaptive quantum rotation angle;  performance experience record;  elitist competition pool
属性约简;  混合协同进化;  自适应量子旋转角;  执行经验记录;  精英竞争池
PACS:
TP301.6
DOI:
10.3969/j.issn.1003-7985.2012.02.003
Abstract:
Due to the fact that conventional heuristic attribute reduction algorithms are poor in running efficiency and difficult in accomplishing the co-evolutionary reduction mechanism in the decision table, an adaptive multicascade attribute reduction algorithm based on quantum-inspired mixed co-evolution is proposed. First, a novel and efficient self-adaptive quantum rotation angle strategy is designed to direct the participating populations to mutual adaptive evolution and to accelerate convergence speed. Then, a multicascade model of cooperative and competitive mixed co-evolution is adopted to decompose the evolutionary attribute species into subpopulations according to their historical performance records, which can increase the diversity of subpopulations and select some elitist individuals so as to strengthen the sharing ability of their searching experience. So the global optimization reduction set can be obtained quickly. The experimental results show that, compared with the existing algorithms, the proposed algorithm can achieve a higher performance for attribute reduction, and it can be considered as a more competitive heuristic algorithm on the efficiency and accuracy of minimum attribute reduction.
针对传统生物启发式方法在决策表中属性约简求解效率不高和难以协同约简等问题, 提出一种基于量子混合协同进化的自适应多级联属性约简算法.首先设计了一种新型高效的自适应量子角旋转策略, 指导参与属性约简的进化种群自适应相互演进, 加速算法收敛.然后构建了合作和竞争混合的协同进化级联模型, 根据执行经验记录分割属性种群集, 提高约简子种群的多样性, 并产生种群精英以增强其寻优经验共享, 快速找到全局最小属性约简集.实验结果表明, 与同类典型算法相比, 该算法在最小属性约简效率和精度方面具有明显优势.

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Memo

Memo:
Biographies: Ding Weiping(1979—), male, graduate, lecturer, ding_wp@nuaa.edu.cn;Wang Jiandong(1945—), male, professor, aics@nuaa.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.61139002, 61171132), the Funding of Jiangsu Innovation Program for Graduate Education(No.CXZZ11_0219), the Natural Science Foundation of Jiangsu Province(No.BK2010280), the Open Project of Jiangsu Provincial Key Laboratory of Computer Information Processing Technology(No.KJS1023), the Applying Study Foundation of Nantong(No.BK2011062).
Citation: Ding Weiping, Wang Jiandong, Shi Quan, et al.Adaptive multicascade attribute reduction based on quantum-inspired mixed co-evolution[J].Journal of Southeast University(English Edition), 2012, 28(2):145-150.[doi:10.3969/j.issn.1003-7985.2012.02.003]
Last Update: 2012-06-20