Adaptive Variance Scaling in Clayton Copula EDA

  • Ru Yang
  • Li-Fang Wang
  • Jian-Chao Zeng
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 113)


Estimation of Distribution Algorithms based on copula (cEDA) divide the estimated probabilistic model about the promising population into two parts, the marginal distribution of each variable and a copula function. The copula function combines the marginal distribution of each variable together as the joint distribution. The selection of marginal distribution can affect the optimization results. In the research on Clayton cEDA, empirical distribution and normal distribution are used as the marginal distribution respectively, the results are compared with each other, then the lack of using normal distribution is simply analyzed, an adaptive variance scaling strategy introduced to the algorithms to improve the optimization efficiency, and the experiments are used to illustrate the results.


Estimation of Distribution Algorithms (EDAs) Copula theory Clayton copula function Marginal distribution Empirical distribution Normal distribution 



This work is supported by the Youth Research Fund of ShanXi Province (No. 2010021017-2), the Science Fund of Shanxi Institution of Higher Learning (No. 2010015) and the Nature Science Fund of ShanXi Province (No. 2009011017-3).


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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Complex System and Computational Intelligence LaboratoryTaiyuan University of Science and TechnologyTaiyuanChina
  2. 2.Colloge of Electrical and Information EngineeringLanzhou University of TechnologyLanzhouChina

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