Continuous estimation of distribution algorithms based on factorized Gaussian Markov networks

Continuous estimation of distribution algorithms based on factorized Gaussian Markov networks” by H. Karshenas, R. Santana, C. Bielza, and P. Larrañaga. In Markov Networks in Evolutionary Computation, (S. Shakya and R. Santana, eds.), 2012, pp. 157-173.

Abstract

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 thesemodels, 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.

BibTeX entry:

@incollection{Karshenas_et_al:2012,
   author = {H. Karshenas and R. Santana and C. Bielza and P. Larra{\~n}aga},
   editor = {S. Shakya and R. Santana},
   title = {Continuous estimation of distribution algorithms based on
	factorized {G}aussian {M}arkov networks},
   booktitle = {Markov Networks in Evolutionary Computation},
   pages = {157-173},
   publisher = {Springer},
   year = {2012},
   url = {http://dx.doi.org/10.1007/978-3-642-28900-2_10}
}

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