# Structural Fusion of Bayesian Networks with Limited Treewidth Using Genetic Algorithms

arXiv: https://arxiv.org/abs/2609.10276 · HTML: https://mlsanity.com/paper/2609.10276/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 27 references that are in the map.
- First posted on arXiv 9 Sep 2026.

## Builds on (most cited first, 2 of 2)

- [Computational Advantages of Relevance Reasoning in Bayesian Belief Networks](https://mlsanity.com/paper/1302.1558/) (2013)
- [Rank Aggregation Using Scoring Rules](https://mlsanity.com/paper/2209.08856/) (2022)

## Abstract (arXiv)

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.