The impact of probabilistic learning algorithms in EDAs based on Bayesian networks

The impact of probabilistic learning algorithms in EDAs based on Bayesian networks” by C. Echegoyen, R. Santana, J. A. Lozano, and P. Larrañaga. In Linkage in Evolutionary Computation, 2008, pp. 109-139.

Abstract

This paper discusses exact learning of Bayesian networks in estimation of distribution algorithms. The estimation of Bayesian network algorithm (EBNA) is used to analyze the impact of learning the optimal (exact) structure in the search. By applying recently introduced methods that allow learning optimal Bayesian networks, we investigate two important issues in EDAs. First, we analyze the question of whether learning more accurate (exact) models of the dependencies implies a better performance of EDAs. Secondly, we are able to study the way in which the problem structure is translated into the probabilistic model when exact learning is accomplished. The results obtained reveal that the quality of the problem information captured by the probability model can improve when the accuracy of the learning algorithm employed is increased. However, improvements in model accuracy do not always imply a more efficient search.

BibTeX entry:

@inproceedings{Echegoyen_et_al:2008,
   author = {C. Echegoyen and R. Santana and J. A. Lozano and P.
	Larra{\~n}aga},
   title = {The impact of probabilistic learning algorithms in {EDAs}
	based on {B}ayesian networks},
   booktitle = {Linkage in Evolutionary Computation},
   series = {Studies in Computational Intelligence},
   pages = {109-139},
   publisher = {Springer},
   year = {2008},
   url = {http://dx.doi.org/10.1007/978-3-540-85068-7_6}
}

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