A chain-model genetic algorithm for Bayesian network structure learning

Bayesian Networks are today used in various fields and domains due to their inherent ability to deal with uncertainty. Learning Bayesian Networks, however is an NP-Hard task [7]. The super exponential growth of the number of possible networks given the number of factors in the studied problem domain has meant that more often, approximate and heuristic rather than exact methods are used. In this paper, a novel genetic algorithm approach for reducing the complexity of Bayesian network structure discovery is presented. We propose a method that uses chain structures as a model for Bayesian networks that can be constructed from given node orderings. The chain model is used to evolve a small number of orderings which are then injected into a greedy search phase which searches for an optimal structure. We present a series of experiments that show a significant reduction can be made in computational cost although with some penalty in success rate. Categories and Subject Descriptors I.2.8 [A...

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Ratiba Kabli, Frank Herrmann, John McCall
• Keywords: Bayesian Network Structure, Bayesian Networks, GECCO 2007, Genetic Algorithms