Towards a hybrid approach for evolving bayesian networks using genetic algorithms


Learning the structure of a Bayesian network from data is complex because the number of possible structures increases super-exponentially with the increase in the number of nodes. To address this problem, we propose a hybrid approach comprised of two phases: The constraint-based phase that identifies dependencies among variables to minimize the search space, followed by a score-and-search phase which employs a genetic algorithm to evolve the Bayesian network from the reduced search space. We evaluate the performance of our approach by comparing it with existing algorithms on a limited amount of data sets generated from three benchmark networks. The results illustrate that the proposed algorithm achieves good performance in learning the structure particularly for medium to large networks. Next, we apply our method to a new data set generated from a hand-designed network - the RoRSS (Rules of the Road Ship Simulator). The preliminary results indicate that our method is also satisfactory for small networks with a limited amount of data. The work presented here is a proof-of-concept for our proposed approach aimed at discovering knowledge from data samples of varying sizes and in the presence of small to high number of nodes. Based on the results obtained so far, we are confident that our method is suitable to efficiently learn the structure of the RoRSS network from a large data set.

Document Type

Conference Proceeding




Bayesian networks, Genetic algorithms, Probabilistic models, Structure learning

Publication Date


Journal Title

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI