A game model based co-evolutionary for constrained multiobjective optimization problems*

Wang, Gaoping; Wang, Yongji

doi:10.1109/iscit.2005.1566828

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…He applied a classic binary representation genetic algorithm figure out the optimization of nutritional meals after 4 years [2]. Wang Gaoping dealt optimization of nutrition policy-making with Multiobjective genetic algorithm NSGA-II in [3].…”

Section: Introductionmentioning

confidence: 99%

Nutritional Diet Decision Using Multi-objective Difference Evolutionary Algorithm

Pei¹,

Liu²

2009

2009 International Conference on Computational Intelligence and Natural Computing

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The nutrition diet decision problems on Multiobjective optimization are solved by using Compromise Difference Evolutionary (DE) algorithm. This method is equipped with a domination selection operator to enhance its performance by favoring non-dominated individuals in the populations. DE is a population based search algorithm, which is an improved version of Genetic Algorithm (GA). Simulations carried out involved solving nutrition decision using a method that relationships of dominant to determine the fitness, and finding Pareto optimum set for the nutrition decision problem. Compromise Difference Evolutionary found to be stable and more accurate in optimization compared to simple GA.

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Section: Introductionmentioning

confidence: 99%

Nutritional Diet Decision Using Multi-objective Difference Evolutionary Algorithm

Pei¹,

Liu²

2009

2009 International Conference on Computational Intelligence and Natural Computing

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“…Trading agents co-evolve their own populations' strategies using a Genetic Algorithm (GA). [10] proposes a game model based coevolutionary algorithm to solve multi-objective class of problems. It tries to find Evolutionary Stable Strategy (ESS) as a solution to multiobjective problems using game model based coevolutionary algorithm.…”

Section: Introductionmentioning

confidence: 99%

Colonial Competitive Algorithm as a Tool for Nash Equilibrium Point Achievement

Rajabioun

Atashpaz-Gargari

Lucas

Computational Science and Its Applications – ICCSA 2008

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Abstract. This paper presents an application of Colonial Competitive Algorithm (CCA) in game theory and multi-objective optimization problems. The recently introduced CCA has proven its excellent capabilities, such as faster convergence and better global optimum achievement. In this paper CCA is used to find Nash Equilibrium points of nonlinear non-cooperative games. The proposed method can also be used as an alternative approach to solve multiobjective optimization problems. The effectiveness of the proposed method, in comparison to Genetic Algorithm, is proven through several static and dynamic example games and also multi-objective problems.

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Notice of Retraction: Constrained multi-objective evolutionary algorithm for application decision-making in nutrition

Wang

Sun

Yueyan

2010

2010 International Conference on Computer and Communication Technologies in Agriculture Engineering

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A game model based co-evolutionary for constrained multiobjective optimization problems*

Cited by 3 publications

References 6 publications

Nutritional Diet Decision Using Multi-objective Difference Evolutionary Algorithm

Nutritional Diet Decision Using Multi-objective Difference Evolutionary Algorithm

Colonial Competitive Algorithm as a Tool for Nash Equilibrium Point Achievement

Notice of Retraction: Constrained multi-objective evolutionary algorithm for application decision-making in nutrition

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