SGMIEC: using selfish gene theory to construct mutualinformation and entropy based cluster for optimization

Authors:
Feng Wang;Zhiyi Lin;Cheng Yang;Yuanxiang Li
Affiliations:
State Key Lab of Software Engineering, Wuhan University, Wuhan, China;State Key Lab of Software Engineering, Wuhan University, Wuhan, China;State Key Lab of Software Engineering, Wuhan University, Wuhan, China;State Key Lab of Software Engineering, Wuhan University, Wuhan, China
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

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Abstract

This paper proposes a new approach named SGMIEC in the field of Estimation of Distribution Algorithm (EDA). While the current EDAs require much time in the statistical learning process as the relationships among the variables are too complicated, the Selfish Gene Theory (SG) is deployed in this approach and a Mutual Information and Entropy based Cluster (MIEC) model with an incremental learning and resample scheme is also set to optimize the probability distribution of the virtual population. Experimental results on several benchmark problems demonstrate that, compared with BMDA and COMIT , SGMIEC often performs better in convergent reliability, convergent velocity and convergent process.