Learning Bayesian network structures by searching for the best ordering with genetic algorithms

P. Larrañaga, Cindy Kuijpers, R.H. Murga, Y. Yurramendi

Research output: Contribution to journalArticleScientificpeer-review

178 Citations (Scopus)

Abstract

Presents a new methodology for inducing Bayesian network structures from a database of cases. The methodology is based on searching for the best ordering of the system variables by means of genetic algorithms. Since this problem of finding an optimal ordering of variables resembles the traveling salesman problem, the authors use genetic operators that were developed for the latter problem. The quality of a variable ordering is evaluated with the structure-learning algorithm K2. The authors present empirical results that were obtained with a simulation of the ALARM network.
Original languageEnglish
Pages (from-to)487-493
JournalIEEE Transaction on Systems, Man and Cybernetics - Part A: Systems and Humans
Volume26
Issue number4
DOIs
Publication statusPublished - Jul 1996
Externally publishedYes

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