Shahab Wahhab KareemMehmet Cudi OkurKareem, Shahab WahhabOkur, Mehmet Cudi2025-10-06202115082806, 230070361508-28062300-703610.7494/csci.2021.22.4.37732-s2.0-85121821458https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121821458&doi=10.7494%2Fcsci.2021.22.4.3773&partnerID=40&md5=d0f727fa9858645c41cad3698fe29165https://gcris.yasar.edu.tr/handle/123456789/9053https://doi.org/10.7494/csci.2021.22.4.3773In machine-learning some of the helpful scientific models during the production of a structure of knowledge are Bayesian networks. They can draw the relationships of probabilistic dependency among many variables. The score and search method is a tool that is used as a strategy for learning the structure of a Bayesian network. The authors apply the falcon optimization algorithm (FOA) to the learning structure of a Bayesian network. This paper has employed reversing deleting moving and inserting to obtain the FOA for approaching the optimal solution of a structure. Essentially the falcon prey search strategy is used in the FOA algorithm. The result of the proposed technique is associated with pigeon-inspired optimization greedy search and simulated annealing that apply the BDeu score function. The authors have also examined the performances of the confusion matrix of these techniques by utilizing several benchmark data sets. As shown by the experimental evaluations the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values). © 2021 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/openAccessBayesian Network, Falcon Optimization Algorithm, Global Search, Search And Score, Structure LearningBayesian NetworkSearch and ScoreGlobal SearchFalcon Optimization AlgorithmStructure LearningArticle