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GECCO
2007
Springer

Cross entropy and adaptive variance scaling in continuous EDA

9 years 5 months ago
Cross entropy and adaptive variance scaling in continuous EDA
This paper deals with the adaptive variance scaling issue in continuous Estimation of Distribution Algorithms. A phenomenon is discovered that current adaptive variance scaling method in EDA suffers from imprecise structure learning. A new type of adaptation method is proposed to overcome this defect. The method tries to measure the difference between the obtained population and the prediction of the probabilistic model, then calculate the scaling factor by minimizing the cross entropy between these two distributions. This approach calculates the scaling factor immediately rather than adapts it incrementally. Experiments show that this approach extended the class of problems that can be solved, and improve the search efficiency in some cases. Moreover, the proposed approach features in that each decomposed subspace can be assigned an individual scaling factor, which helps to solve problems with special dimension property. Categories and Subject Descriptors
Yunpeng Cai, Xiaomin Sun, Hua Xu, Peifa Jia
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where GECCO
Authors Yunpeng Cai, Xiaomin Sun, Hua Xu, Peifa Jia
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