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Model-based template-recombination in Markov network estimation of distribution algorithms for problems with discrete representation

While estimation of distribution algorithms (EDAs) based on Markov networks usually incorporate efficient methods to learn undirected probabilistic graphical models (PGMs) from data, the methods they use for sampling the PGMs are computationally costly. In addition, methods for generating solutions in Markov network based EDA frequently discard information contained in the model to gain in efficiency. In this paper we propose a new method for generating solutions that uses the Markov network structure as a template for crossover. The new algorithm is evaluated on discrete deceptive functions of various degrees of difficulty and Ising instances.