Attribute Equilibrium Reduction with Quantum Game Based on Mixed Co-Evolutionary Populations' Collaboration

DING Wei-ping; WANG Jian-dong; SHI Quan; CHEN Sen-bo; SHEN Xue-hua

doi:10.3969/j.issn.0372-2112.2015.01.008

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Attribute Equilibrium Reduction with Quantum Game Based on Mixed Co-Evolutionary Populations' Collaboration

更新时间：2025-07-16

- Attribute Equilibrium Reduction with Quantum Game Based on Mixed Co-Evolutionary Populations' Collaboration
- Acta Electronica Sinica Vol. 43, Issue 1, Pages: 45-53(2015)
- 作者机构：
  
  1. 南通大学计算机科学与技术学院,江苏,南通,226019
  2. 计算机软件新技术国家重点实验室(南京大学),江苏,南京,210093
  3. 南京航空航天大学计算机科学与技术学院,江苏,南京,210016
  4. 南通大学计算机科学与技术学院,江苏,南通,226019
  5. 计算机软件新技术国家重点实验室(南京大学),江苏,南京,210093
  6. 南京航空航天大学计算机科学与技术学院,江苏,南京,210016
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61300167, No.61139002, No.61171132);Open Subject of State Key Laboratory for Novel Software Technology of Nanjing University (No.KFKT2012B28);Supported by Blue Project in Jiangsu Province;Natural Science Research Program of Universities in Jiangsu Province (No.12KJB520013);Application Research Project of Nantong Science and Technology Program (No.BK2011062, No.BK2012038);Preliminary Research Project of Natural Science Research Fund of Nantong University (No.12ZY016)
- DOI：10.3969/j.issn.0372-2112.2015.01.008
  CLC： TP301.6
- Published：2015
- 稿件说明：
移动端阅览
DING Wei-ping, WANG Jian-dong, SHI Quan, et al. Attribute Equilibrium Reduction with Quantum Game Based on Mixed Co-Evolutionary Populations' Collaboration[J]. Acta Electronica Sinica, 2015, 43(1): 45-53.
DOI：

DING Wei-ping, WANG Jian-dong, SHI Quan, et al. Attribute Equilibrium Reduction with Quantum Game Based on Mixed Co-Evolutionary Populations' Collaboration[J]. Acta Electronica Sinica, 2015, 43(1): 45-53. DOI： 10.3969/j.issn.0372-2112.2015.01.008.

摘要

为进一步提高进化种群在粗糙集属性演化约简中寻求最优解的协同性能

提出了一种基于种群混合协同联盟的属性量子博弈均衡约简算法.该算法建立一种基于自适应多层进化树的种群协同演化联盟模型

以种群内个体竞争和种群间精英合作的混合协同机制实现各种群协同演化

较好地达到属性协同演化约简中广度寻优和深度探索的有效平衡;然后将信任裕度报酬机制引入到多种群精英量子协同博弈模型

种群精英在每个划分的属性子集中通过量子协同博弈策略均能求得各自最优约简子集

从而稳定取得Nash均衡下全局最优属性约简集.实验结果表明本文算法具有较高的属性演化约简效能和精度

对不完备电子病历系统中脑组织核磁共振成像MRI的高效约简与分割进一步展示其具有较强的实用性和鲁棒性.

Abstract

In order to further improve the cooperative performance of evolutionary populations to solve out the optimal solution for attribute reduction in rough set theory

a novel attribute equilibrium reduction algorithm(named AERQG)with quantum game based on mixed co-evolutionary populations' collaboration is proposed.In this algorithm

the collaboration model of co-evolutionary populations based on the self-adaptive multilevel evolutionary tree is constructed.The mixed co-evolutionary mechanism

which is both competitive co-evolution for evolutionary individuals in populations and cooperative co-evolution for representative elitists between populations

is adopted to carry out the populations' co-evolution.This mechanism can better obtain the balance of attribute co-evolutionary reduction between exploitation in breadth and exploration in depth.Moreover

the compensation mechanism of trust margin is introduced into the quantum game model for multi-populations elites.The population elite can be sure to find out the respective optimal subset of attribute reduction by using the quantum game strategy

and thus the global optimal collaboration set of attribute reduction under the Nash equilibrium is achieved steadily.Experimental results demonstrate the proposed algorithm has achieved better high-performance on efficiency and accuracy than traditional algorithms.Further

the efficient reduction and segmentation for brain magnetic resonance imaging(MRI)in the incomplete electronic medical record system verifies its stronger practicality and robustness.

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