Regularized k-order markov models in EDAs

k-order Markov models have been introduced to estimation of distribution algorithms (EDAs) to solve a particular class of optimization problems in which each variable depends on its previous k variables in a given, fixed order. In this paper we investigate the use of regularization as a way to approximate k-order Markov models when $k$ is increased. The introduced regularized models are used to balance the complexity and accuracy of the k-order Markov models. We investigate the behavior of the EDAs in several instances of the hydrophobic-polar (HP) protein problem, a simplified protein folding model. Our preliminary results show that EDAs that use regularized approximations of the k-order Markov models offer a good compromise between complexity and efficiency, and could be an appropriate choice when the number of variables is increased.

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