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One of the useful knowledge representation tools, which can describe the joint probability distribution between some random variables with a graphical model and can be trained by a dataset, is the Bayesian network (BN). A BN is composed of a network structure and a conditional probability distribution table for each node. Discovering an optimal BN structure is an NP‐hard optimization problem that various meta‐heuristic algorithms are applied to solve this problem by researchers. The genetic algorithms, ant colony optimization, evolutionary programming, artificial bee colony, and bacterial foraging optimization are some of the meta‐heuristic methods to solve this problem using a dataset. Most of these methods are applying a scoring metric to generate the best network structure from a set of candidates. A Fixed Structure Learning Automata‐Based (FSLA‐B) algorithm is presented in this paper to solve the structure learning problem of BNs. There is a fixed structure learning automaton for each pair of vertices in the BN's graph structure in the proposed algorithm. The action of this automaton determines the presence and direction of an edge between the vertices. The proposed algorithm performs a guided search procedure using the FSLA and escapes from local optimums. Several datasets are utilised in this paper to evaluate the performance of the proposed algorithm. By performing various experiments, multiple meta‐heuristic algorithms are compared with the introduced new one. The obtained results represented that the proposed algorithm could produce competitive results and find the near‐optimal solution for the BN structure learning problem. [ABSTRACT FROM AUTHOR]

In 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). [ABSTRACT FROM AUTHOR]