Analysis of Bayesian Network Learning Techniques for a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm: a case study on MNK Landscape

• Published in: Journal of heuristics Vol. 27; no. 4; pp. 549 - 573
• Main Authors: Martins, Marcella S. R., Yafrani, Mohamed El, Delgado, Myriam, Lüders, Ricardo, Santana, Roberto, Siqueira, Hugo V., Akcay, Huseyin G., Ahiod, Belaïd
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Bayesian analysis; Calculus of Variations and Optimal Control; Optimization; Case studies; Combinatorial analysis; Machine learning; Management Science; Mathematical programming; Mathematics; Mathematics and Statistics; Objectives; Operations Research; Operations Research/Decision Theory; Optimization; Performance evaluation; Variables; Estimation of distribution algorithms; Robustness; Structure learning techniques; Many-objective optimization
• ISSN: 1381-1231; 1572-9397
• DOI: 10.1007/s10732-021-09469-x

This work investigates different Bayesian network structure learning techniques by thoroughly studying several variants of Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm (HMOBEDA), applied to the MNK Landscape combinatorial problem. In the experiments, we evaluate the performance considering three different aspects: optimization abilities, robustness and learning efficiency. Results for instances of multi- and many-objective MNK-landscape show that, score-based structure learning algorithms appear to be the best choice. In particular, HMOBEDA k 2  was capable of producing results comparable with the other variants in terms of the runtime of convergence and the coverage of the final Pareto front, with the additional advantage of providing solutions that are less sensible to noise while the variability of the corresponding Bayesian network models is reduced.