Advances in Probabilistic Graphical Models
volume 214/2007 of Studies in Fuzziness and Soft Computing, chapter A Study on the Evolution of Bayesian Network Graph Structures, page 193--213. Springer Berlin / Heidelberg, (2007)DOI: 10.1007/978-3-540-68996-6_9
Abstract
Bayesian Networks (BN) are often sought as useful descriptive and
predictive models for the available data. Learning algorithms trying
to ascertain automatically the best BN model (graph structure) for
some input data are of the greatest interest for practical reasons.
In this paper we examine a number of evolutionary programming algorithms
for this network induction problem. Our algorithms build on recent
advances in the field and are based on selection and various kinds
of mutation operators (working at both the directed acyclic and essential
graph level). A review of related evolutionary work is also provided.
We analyze and discuss the merit and computational toll of these
EP algorithms in a couple of benchmark tasks. Some general conclusions
about the most efficient algorithms, and the most appropriate search
landscapes are presented.