Abstract
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.
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Domínguez, I.S., Aguirre, A.H., Diharce, E.V. (2013). The Gaussian Polytree EDA with Copula Functions and Mutations. In: Tantar, E., et al. EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation. Studies in Computational Intelligence, vol 447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32726-1_3
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DOI: https://doi.org/10.1007/978-3-642-32726-1_3
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