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Abstract
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| Because of their intrinsic properties, the majority of the estimation of distribution algorithms proposed for continuous optimization problems are based on the Gaussian distribution assumption for the variables. This paper looks over the relation between the general multivariate Gaussian distribution and the popular undirected graphical model of Markov networks and discusses how they can be employed in estimation of distribution algorithms for continuous optimization. A number of learning and sampling techniques for these models, including the promising regularized model learning, are also reviewed and their application for function optimization in the context of estimation of distribution algorithms is studied. | |
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International
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Si |
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http://dx.doi.org/10.1007/978-3-642-28900-2_10 |
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Book Edition
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14 |
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Book Publishing
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Siddhartha Shakya and Roberto Santana |
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ISBN
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978-3-642-28900-2 |
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Series
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Adaptation, Learning, and Optimization |
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Book title
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Markov Networks in Evolutionary Computation |
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From page
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157 |
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To page
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173 |