The Gaussian Polytree EDA with Copula Functions and Mutations

  • Ignacio Segovia Domínguez
  • Arturo Hernández Aguirre
  • Enrique Villa Diharce
Part of the Studies in Computational Intelligence book series (SCI, volume 447)


This chapter introduces the Gaussian Poly-Tree Estimation Distribution Algorithm, and two extensions: i) with Gaussian copula functions, and ii) with local optimizers. The new construction and simulation algorithms, and its application to estimation of distribution algorithms with continuous Gaussian variables are also introduced. The algorithm for the construction of the structure and for edge orientation is based on information theoretic concepts such as mutual information and conditional mutual information. The three models are tested on a benchmark of 20 unimodal and multimodal functions. The version with copula function and mutations excels in most problems achieving near optimal success rate.


Mutual Information Multivariate Normal Distribution Joint Probability Density Function Copula Function Copula Modeling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer Berlin Heidelberg 2013

Authors and Affiliations

  • Ignacio Segovia Domínguez
    • 1
  • Arturo Hernández Aguirre
    • 1
  • Enrique Villa Diharce
    • 1
  1. 1.Center for Research in MathematicsGuanajuatoMéxico

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