SGEGC: A Selfish Gene Theory Based Optimization Method by Exchanging Genetic Components

Authors:
Cheng Yang;Yuanxiang Li;Zhiyi Lin
Affiliations:
State Key Lab of Software Engineering, Wuhan University, Wuhan, P.R. China 430072;State Key Lab of Software Engineering, Wuhan University, Wuhan, P.R. China 430072;State Key Lab of Software Engineering, Wuhan University, Wuhan, P.R. China 430072
Venue:
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Year:
2009

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Abstract

In this paper, a new algorithm named SGEGC was proposed. Inspired by selfish gene theory, SGEGC uses a vector of survival rate to model the condition distribution, which serves as a virtual population that is used to generate new individuals. While the present Estimation of Distribution Algorithms (EDAs) require much time to learn the complex relationships among variables, SGEGC employs an approach that exchanges the relevant genetic components. Experimental results show that the proposed approach is more efficient in convergent reliability and convergent velocity in comparison with BMDA, COMIT and MIMIC in the test functions.