Game Model Based Co-evolutionary Algorithm and its Application for Multiobjective Optimization Problems

Author

Wang, Gaoping ; Wang, Yongji

Author_Institution

Sch. of Inf. Sci. & Eng., Henan Univ. of Technol., Zhengzhou

Volume

1

fYear

2006

fDate

Nov. 2006

Firstpage

274

Lastpage

277

Abstract

Sefrioui introduced the Nash genetic algorithm in 1998. This approach combines genetic algorithms with Nash´s idea. Another central achievement of game theory is the introduction of an evolutionary stable strategy, introduced by Maynard Smith in 1982. In this paper, we will try to find ESS as a solution of MOPs using our game model based co-evolutionary algorithm. We present A Game model based co-evolutionary algorithm (GMBCA) to solve this class of problems and its performance is analyzed in comparing its results with those obtained with four others algorithms. Finally, the GMBCA is applied to solve the nutrition decision making problem to map the Pareto-optimum front. The results in the problem show its effectiveness

Keywords

Pareto optimisation; game theory; genetic algorithms; Nash genetic algorithm; Pareto-optimum front; coevolutionary algorithm; game theory; multiobjective optimization; Constraint optimization; Decision making; Electronic switching systems; Game theory; Genetic algorithms; Genetic engineering; Information science; Nash equilibrium; Performance analysis; Space technology;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Security, 2006 International Conference on

Conference_Location

Guangzhou

Print_ISBN

1-4244-0605-6

Electronic_ISBN

1-4244-0605-6

Type

conf

DOI

10.1109/ICCIAS.2006.294136

Filename

4072089