“Regularized
k-order Markov models in EDAs”
by
R. Santana,
H. Karshenas,
C. Bielza,
and
P. Larrañaga.
In *Proceedings of the 2011 Genetic and Evolutionary Computation
Conference GECCO-2011*, (Dublin, Ireland), 2011, pp. 593-600.

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.

**BibTeX entry:**

@inproceedings{Santana_et_al:2011c, author = {R. Santana and H. Karshenas and C. Bielza and P. Larra{\~n}aga}, title = {Regularized k-order {Markov} models in {EDAs}}, booktitle = {Proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO-2011}, pages = {593-600}, address = {Dublin, Ireland}, year = {2011}, url = {http://dl.acm.org/citation.cfm?id=2001658} }

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