An Enhanced Attribute Co‐evolutionary Game Reduction Algorithm by Integrating Self‐adaptive Multi‐level Nash Equilibrium. Issue 1 (1st January 2016)

• Record Type: Journal Article
• Authors: Ding, Weiping; Guan, Zhijin; Shi, Quan
• Is Part Of: Chinese journal of electronics. Volume 25:Issue 1(2016)
• Page Start: 13
• Page End: 19
• Publication Date: 2016-01-01
• Subjects: Cross co‐evolutionary game -- Profit matrix of elitist energy -- Multi‐level Nash equilibrium -- Minimum attribute reduction; biomedical MRI -- brain -- evolutionary computation -- game theory -- image segmentation -- medical image processing; 3D brain MRI segmentation -- coevolutionary selection -- payoff mechanism -- elitist energy -- profit matrix -- cross co‐evolution -- EACGR -- minimum attribute reduction -- co‐evolutionary populations -- self‐adaptive multilevel Nash equilibrium -- enhanced attribute co‐evolutionary game reduction algorithm; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2016.01.003
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : In order to further analyze the dynamical behavior of co‐evolutionary populations in minimum attribute reduction, an Enhanced attribute co‐evolutionary game reduction (EACGR) algorithm by integrating selfadaptive multi‐level Nash equilibrium is proposed in this paper. First, a self‐adaptive multi‐level Nash game model with cross co‐evolution is designed to provide the better solution for the dynamical symmetric co‐evolution of multi‐populations. Second, the profit matrix of elitist energy is constructed to explore the payoff mechanism of co‐evolutionary selection. And then a novel Nash equilibrium strategy is adopted to perform attribute co‐evolutionary game reduction so that the admissible balance of attribute reduction can be well achieved. Experimental results indicate that EACGR has the higher performance of minimum attribute reduction, and the application into 3D brain MRI segmentation with promising results indicates its stronger robustness and practicability.