Titlebook: Markov Networks in Evolutionary Computation; Siddhartha Shakya,Roberto Santana Book 2012 Springer Berlin Heidelberg 2012 Estimation of Dis

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Convergence Theorems of Estimation of Distribution Algorithmsdditively decomposed (ADF). The interaction graph of the ADF function is used to create exact or approximate factorizations of the Boltzmann distribution. Convergence of the algorithmMN-GIBBS is proven.MN-GIBBS uses a Markov network easily derived from the ADF and Gibbs sampling. We discuss differen

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Adaptive Evolutionary Algorithm Based on a Cliqued Gibbs Sampling over Graphical Markov Model Structation of the searching sample complexity through an index based on the sample entropy. The searching sample algorithm learns a tree, and then, uses a sample complexity index to prognose the missing edges to obtain the cliques of the structure of the estimating distribution adding more edges if neces

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Fast Fitness Improvements in Estimation of Distribution Algorithms Using Belief Propagationes such as belief propagation. In this paper we introduce a flexible implementation of belief propagation on factor graphs in the context of estimation of distribution algorithms (EDAs). By using a transformation from Bayesian networks to factor graphs, we show the way in which belief propagation ca

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Applications of Distribution Estimation Using Markov Network Modelling (DEUM)us on several applications of Markov Network EDAs classified under the DEUM framework which estimates the overall distribution of fitness from a bitstring population. In Section 1 we briefly review the main features of the DEUM framework and highlight the principal features that havemotivated the se

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Vine Estimation of Distribution Algorithms with Application to Molecular Docking to address the molecular docking problem. The simplest algorithms considered are built on top of the product and normal copulas. The other two construct high-dimensional dependence models using the powerful and flexible concept of vine-copula. Empirical investigation with a set of molecular complex

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EDA-RL: EDA with Conditional Random Fields for Solving Reinforcement Learning Problemsodel of the EDA-RL, the Conditional Random Fields proposed by Lafferty .. are employed. The Conditional Random Fields can estimate conditional probability distributions by using Markov Network. Moreover, the structural search of probabilistic model by using ..-test, and data correction method are ex