A review on evolutionary algorithms in Bayesian network learning and inference tasks

Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intellig...

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Bibliographic Details
Published in:Information sciences Vol. 233; pp. 109 - 125
Main Authors: Larrañaga, Pedro, Karshenas, Hossein, Bielza, Concha, Santana, Roberto
Format: Journal Article
Language:English
Published: Elsevier Inc 01.06.2013
Subjects:
Bayesian network
Evolutionary computation
Inference
Learning from data
Probabilistic graphical model
Learning from data
Probabilistic graphical model
Inference
Bayesian network
Evolutionary computation
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary:Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Bayesian networks are one of the most widely used class of these models. Some of the inference and learning tasks in Bayesian networks involve complex optimization problems that require the use of meta-heuristic algorithms. Evolutionary algorithms, as successful problem solvers, are promising candidates for this purpose. This paper reviews the application of evolutionary algorithms for solving some NP-hard optimization tasks in Bayesian network inference and learning.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2012.12.051