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A Bayesian Sampling Algorithm for Evolving Random Hypergraph Models Representing Higher-Order Correlations  

Lee, Si-Eun (백석대학교 정보통신학부)
Lee, In-Hee (서울대학교 컴퓨터공학부)
Zhang, Byoung-Tak (서울대학교 컴퓨터공학부)
Publication Information
Journal of KIISE:Software and Applications / v.36, no.3, 2009 , pp. 208-216 More about this Journal
Abstract
A number of estimation of distribution algorithms have been proposed that do not use explicitly crossover and mutation of traditional genetic algorithms, but estimate the distribution of population for more efficient search. But because it is not easy to discover higher-order correlations of variables, lower-order correlations are estimated most cases under various constraints. In this paper, we propose a new estimation of distribution algorithm that represents higher-order correlations of the data and finds global optimum more efficiently. The proposed algorithm represents the higher-order correlations among variables by building random hypergraph model composed of hyperedges consisting of variables which are expected to be correlated, and generates the next population by Bayesian sampling algorithm Experimental results show that the proposed algorithm can find global optimum and outperforms the simple genetic algorithm and BOA(Bayesian Optimization Algorithm) on decomposable functions with deceptive building blocks.
Keywords
Bayesian Sampling Algorithm; Bayesian evolutionary computation; EDA; random hypergraph;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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