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

Martins M. S. R., Yafrani M. E., Delgado M., Luders R., Santana R., Siqueira H. V., ...Daha Fazla

JOURNAL OF HEURISTICS, cilt.27, sa.4, ss.549-573, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 27 Sayı: 4
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1007/s10732-021-09469-x
  • Dergi Adı: JOURNAL OF HEURISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Sayfa Sayıları: ss.549-573
  • Anahtar Kelimeler: Many-objective optimization, Estimation of distribution algorithms, Structure learning techniques, Robustness
  • Akdeniz Üniversitesi Adresli: Evet

Özet

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 structure learning 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.