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http://dx.doi.org/10.5391/JKIIS.2005.15.3.375

Co-Evolutionary Model for Solving the GA-Hard Problems  

Lee Dong-Wook (중앙대학교 전자전기공학부)
Sim Kwee-Bo (중앙대학교 전자전기공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.15, no.3, 2005 , pp. 375-381 More about this Journal
Abstract
Usually genetic algorithms are used to design optimal system. However the performance of the algorithm is determined by the fitness function and the system environment. It is expected that a co-evolutionary algorithm, two populations are constantly interact and co-evolve, is one of the solution to overcome these problems. In this paper we propose three types of co-evolutionary algorithm to solve GA-Hard problem. The first model is a competitive co-evolutionary algorithm that solution and environment are competitively co-evolve. This model can prevent the solution from falling in local optima because the environment are also evolve according to the evolution of the solution. The second algorithm is schema co-evolutionary algorithm that has host population and parasite (schema) population. Schema population supply good schema to host population in this algorithm. The third is game model-based co-evolutionary algorithm that two populations are co-evolve through game. Each algorithm is applied to visual servoing, robot navigation, and multi-objective optimization problem to verify the effectiveness of the proposed algorithms.
Keywords
GA-hard problem; Competitive co-evolution; Schema co-evolution; Game model-based co-evolution;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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