This work investigates different Bayesian network structure learning techniques by thoroughly studying several variants ofHybrid Multi-objectiveBayesian Estimation of Distribution Algorithm (HMOBEDA), applied to the M...
详细信息
This work investigates different Bayesian network structure learning techniques by thoroughly studying several variants ofHybrid Multi-objectiveBayesian 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 structurelearning algorithms appear to be the best choice. In particular, HMOBEDA(k2) 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.
暂无评论