A Genetic Algorithm for Tuning Variable Orderings in Bayesian Network Structure Learning

Haipeng Guo, Benjamin B. Perry, Julie A. Stilson, and William H. Hsu, Kansas State University

Learning a Bayesian network from data is NP-hard even without considering unobserved or irrelevant variables. Many previous Bayesian network learning algorithms require that a node ordering is available before learning. Unfortunately, this is usually not the case in many real-world applications. To make greedy search usable when node orderings are unknown, we have developed a permutation genetic algorithm (GA) wrapper to tune the variable ordering given as input to K2, a score-based BN learning algorithm. We have used a probabilistic inference criterion as the GA’s fitness function and we are also trying some other criterion to evaluate the learning result such as the learning fixed-point property.


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