Decomposing Bayesian Networks: Triangulation of Moral Graph with Genetic Algorithms

In this paper we consider the optimal decomposition of Bayesian networks. More concretely, we examine -- empirically --, the applicability of genetic algorithms to the problem of the triangulation of moral graphs. This problem constitutes the only difficult step in the evidence propagation algorithm of Lauritzen and Spiegelhalter (1988) and is known to be NP-hard (Wen, 1991). We carry out experiments with distinct crossover and mutation operators and with different population sizes, mutation rates and selection biasses. The results are analyzed statistically. They turn out to improve the results obtained with most other known triangulation methods (Kjaerulff, 1990) and are comparable to the ones obtained with simulated annealing (Kjaerulff, 1990; Kjaerulff, 1992). Keywords: Bayesian networks, genetic algorithms, optimal decomposition, graph triangulation, moral graph, NP-hard problems, statistical analysis. 1 Introduction The Bayesian networks constitute a reasoning method based on p..