Structural Fusion of Bayesian Networks with Limited Treewidth Using Genetic Algorithms

Abstract:This paper introduces an evolutionary computation approach for consensus in structural Bayesian Network (BN) fusion under the constraint of limited treewidth. The consensus BN aims to reconcile multiple input BNs into a single one that retains key structural features present in the original networks. Treewidth, a graph-based parameter associated with computationally tractable inference, is utilized to restrict the complexity of the resulting network. A genetic algorithm is proposed to look for a BN that codifies as much information about the unrestricted fusion as possible while ensuring the treewidth restriction. Experimental evaluation demonstrates the genetic algorithm's ability to obtain consensus BNs with limited treewidth, providing a valuable tool for aggregating information from diverse sources while returning a computationally actionable model.

8 pages. Presented at the 2024 IEEE Congress on Evolutionary Computation (CEC 2024)

Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)

arXiv:2609.10276 [cs.NE]

(or arXiv:2609.10276v1 [cs.NE] for this version)

https://doi.org/10.48550/arXiv.2609.10276 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

2024 IEEE Congress on Evolutionary Computation (CEC), pp. 1-8, 2024

https://doi.org/10.1109/CEC60901.2024.10611976 Focus to learn more DOI(s) linking to related resources

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.