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01.05.2011 | Focus

Using selfish gene theory to construct mutual information and entropy based clusters for bivariate optimizations

verfasst von: Feng Wang, Zhiyi Lin, Cheng Yang, Yuanxiang Li

Erschienen in: Soft Computing | Ausgabe 5/2011

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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, COMIT and MIMIC, SGMIEC often performs better in convergent reliability, convergent velocity and convergent process.

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Titel: Using selfish gene theory to construct mutual information and entropy based clusters for bivariate optimizations
verfasst von: Feng Wang
Zhiyi Lin
Cheng Yang
Yuanxiang Li
Publikationsdatum: 01.05.2011
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 5/2011
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-010-0557-3

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