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Estimation of Distribution Algorithm Based on Copula Theory

  • Chapter
Exploitation of Linkage Learning in Evolutionary Algorithms

Part of the book series: Evolutionary Learning and Optimization ((ALO,volume 3))

Abstract

Estimation of Distribution Algorithms (EDAs) is the hot topic of evolutionary computation currently. EDAs model the selected population using a distribution model, which is latter sampled to generate the population for the next generation. This chapter introduces a new way to estimate the distribution model and sample from it according to copula theory. The multivariate joint is decomposed into the univariate margins and a function called copula. In the EDAs based on copula theory (copula-EDAs), only the margins are estimated, and the next generation is sampled from the copula and the inverse function of the margins. The framework of the copula-EDAs is discussed in the chapter. Two 2-dimensional copula-EDAs and a high-dimensional copula-EDA are described in detail as the examples.

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Authors and Affiliations

  1. Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, China P.R.

    Li-Fang Wang & Jian-Chao Zeng

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  1. Li-Fang Wang
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  2. Jian-Chao Zeng
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Editors and Affiliations

  1. Natural Computing Laboratory Department of Computer Science, National Chiao Tung University, 1001 Ta Hsueh Road, 300, HsinChu City, Taiwan

    Ying-ping Chen

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Wang, LF., Zeng, JC. (2010). Estimation of Distribution Algorithm Based on Copula Theory. In: Chen, Yp. (eds) Exploitation of Linkage Learning in Evolutionary Algorithms. Evolutionary Learning and Optimization, vol 3. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12834-9_7

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  • DOI: https://doi.org/10.1007/978-3-642-12834-9_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12833-2

  • Online ISBN: 978-3-642-12834-9

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