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Abstract
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| 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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International
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Si |
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Congress
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13th Annual Conference on Genetic and Evolutionary Computation (GECCO'11) |
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960 |
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Place
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Dublin, Ireland |
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Reviewers
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Si |
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ISBN/ISSN
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978-1-4503-0557-0 |
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Start Date
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12/07/2011 |
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End Date
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16/07/2011 |
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From page
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593 |
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To page
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600 |
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Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO'11) |