电子学报Issue(1)：45-53,9.DOI:10.3969/j.issn.0372-2112.2015.01.008
基于种群混合协同联盟的属性量子博弈均衡约简
Attribute Equilibrium Reduction with Quantum Game Based on Mixed Co-Evolutionary Populations’ Collaboration
摘要
Abstract
In order to further improve the cooperative performance of evolutionary populations to solve out the optimal solu-tion 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 be-tween 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 re-spective optimal subset of attribute reduction by using the quantum game strategy ,and thus the global optimal collaboration set of at-tribute 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 segmen-tation for brain magnetic resonance imaging(MRI)in the incomplete electronic medical record system verifies its stronger practicality and robustness .关键词
属性演化约简/自适应进化树/混合协同/信任裕度/量子博弈/纳什均衡Key words
attribute evolutionary reduction/self-adaptive evolutionary tree/mixed cooperation/trust margin/quantum game/Nash equilibrium分类
信息技术与安全科学引用本文复制引用
丁卫平,王建东,施全,陈森博,沈学华..基于种群混合协同联盟的属性量子博弈均衡约简[J].电子学报,2015,(1):45-53,9.基金项目
国家自然科学基金（No ．61300167，No ．61139002，No ．61171132）；计算机软件新技术国家重点实验室（南京大学）开放课题（No ． KFKT2012B28）；江苏省高校“青蓝工程”资助；江苏省高校自然科学研究项目（No ．12KJB520013）；南通市科技计划应用研究项目（No ．BK2011062， No ．BK2012038）；南通大学自然科学类科研基金前期预研项目 （）
