01859nas a2200229   4500000000100000000000100001008004100002260001200043653002100055653003400076653001100110653002300121100002300144700002500167700002400192245010500216856007900321300001000400490000600410520119900416022001401615       2023                            d  c06/202310aBayesian Network10aOptimization Search Algorithm10aSearch10aStructure Learning1 aHoshang Qasim Awla1 aShahab Wahhab Kareem1 aAmin Salih Mohammed00aA Comparative Evaluation of Bayesian Networks Structure Learning Using Falcon Optimization Algorithm  uhttps://www.ijimai.org/journal/sites/default/files/2023-05/ijimai8_2_8.pdf  a81-870 v83 aBayesian networks are analytical models that may represent probabilistic dependent connections among variables and are useful in machine learning for generating knowledge structure. Due to the vastness of the solution space, learning Bayesian network (BN) structures from data is an NP-hard problem. The score and search technique is one Bayesian Network structure learning strategy. In Bayesian network structure learning the Falcon Optimization Algorithm (FOA) is presented and evaluated by the authors. Inserting, Reversing, Moving, and Deleting, are used in the method to create the FOA for finding the best structural solution. The FOA algorithm is based on the falcon's searching technique during drought conditions. The suggested technique is compared to the score metric function of Pigeon Inspired search algorithm, Greedy Search, and Antlion optimization search algorithm. The performance of these techniques in terms of confusion matrices was further evaluated by the authors using a variety of benchmark data sets. The Falcon optimization algorithm outperforms the previous algorithms and generates higher scores and accuracy values, as evidenced by the results of our experiments.   a1989-1660