aiqbench analysis
This research addresses the computational scalability of Bayesian Network fusion by employing genetic algorithms to prune edges while preserving shared dependency structures. The approach effectively balances treewidth constraints with consensus accuracy, offering a robust solution for integrating complex probabilistic models without sacrificing inference speed.
Genetic Algorithms for Tractable Bayesian Network Fusion via Pre-Fusion Edge Pruning
arXiv:2609.03724v1 Announce Type: cross Abstract: Bayesian Network (BN) fusion combines multiple input networks into a single structure, balancing dependency preservation with computational tractability. While unrestricted fusion retains all dependencies, it often results in overly complex networks with high treewidth, which affects inference scalability. Limited fusion mitigates this by pruning edges to control treewidth but risks overfitting to input-specific noise and omitting dependencies from the original BNs. This paper introduces a consensus framework that prioritizes shared structures among input networks while enforcing treewidth constraints, ensuring a good consensus. We propose genetic algorithms with advanced initialization, specialized operators, and a tailored fitness function. Additionally, we adapt existing methods to this problem and implement greedy baselines for benchmarking and further optimization. Experiments on synthetic and real-world BNs show the superiority of the proposed genetic algorithms over the adapted methods and greedy baselines.
Abstract:Bayesian Network (BN) fusion combines multiple input networks into a single structure, balancing dependency preservation with computational tractability. While unrestricted fusion retains all dependencies, it often results in overly complex networks with high treewidth, which affects inference scalability. Limited fusion mitigates this by pruning edges to control treewidth but risks overfitting to input-specific noise and omitting dependencies from the original BNs. This paper introduces a consensus framework that prioritizes shared structures among input networks while enforcing treewidth constraints, ensuring a good consensus. We propose genetic algorithms with advanced initialization, specialized operators, and a tailored fitness function. Additionally, we adapt existing methods to this problem and implement greedy baselines for benchmarking and further optimization. Experiments on synthetic and real-world BNs show the superiority of the proposed genetic algorithms over the adapted methods and greedy baselines.
9 pages. Presented at the Genetic and Evolutionary Computation Conference (GECCO 2025)
Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)
arXiv:2609.03724 [cs.NE]
(or arXiv:2609.03724v1 [cs.NE] for this version)
https://doi.org/10.48550/arXiv.2609.03724 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '25), pp. 481-489, 2025
https://doi.org/10.1145/3712256.3726333 Focus to learn more DOI(s) linking to related resources
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