Open AccessAn Enhanced Attribute Co‐evolutionary Game Reduction Algorithm by Integrating Self‐adaptive Multi‐level Nash Equilibrium
Author(s) -
Ding Weiping,
Guan Zhijin,
Shi Quan
Publication year - 2016
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2016.01.003
Subject(s) - nash equilibrium , computer science , reduction (mathematics) , normal form game , robustness (evolution) , mathematical optimization , evolutionary algorithm , profit (economics) , stochastic game , game theory , mathematics , artificial intelligence , mathematical economics , repeated game , economics , chemistry , geometry , biochemistry , microeconomics , gene
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.