P. Larranaga et al., STRUCTURE LEARNING OF BAYESIAN NETWORKS BY GENETIC ALGORITHMS - A PERFORMANCE ANALYSIS OF CONTROL PARAMETERS, IEEE transactions on pattern analysis and machine intelligence, 18(9), 1996, pp. 912-926
We present a new approach to structure learning in the field of Bayesi
an networks: We tackle the problem of the search for the best Bayesian
network structure, given a database of cases, using the genetic algor
ithm philosophy for searching among alternative structures. We start b
y assuming an ordering between the nodes of the network structures. Th
is assumption is necessary to guarantee that the networks that are cre
ated by the genetic algorithms are legal Bayesian network structures.
Next, we release the ordering assumption by using a ''repair operator'
' which converts illegal structures into legal ones. We present empiri
cal results and analyze them statistically. The best results are obtai
ned with an elitist genetic algorithm that contains a local optimizer.