Journal
SOFT COMPUTING
Volume 15, Issue 5, Pages 907-915Publisher
SPRINGER
DOI: 10.1007/s00500-010-0557-3
Keywords
Estimation of distribution algorithm; Selfish gene theory; Mutual information; Incremental learning
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Funding
- Wuhan University [6082018]
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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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