R Source-code and experiment data of paper "On the performance of the Bayesian Optimization Algorithm with combined scenarios of Search Algorithms and Scoring Metrics" submitted to Genetic Programming and Evolvable Machines - Springer. The Bayesian Optimization Algorithm (BOA) is one of the most prominent Estimation of Distribution Algorithms (EDAs). It can detect the correlation between multiple variables and extract knowledge about regular patterns in solutions. Bayesian Networks (BNs) are used in BOA to represent the probability distributions of the best individuals. The BN's construction is challenging since there is a trade-off between acuity and computational cost to generate it. This commitment is determined by combining a Search Algorithm (SA) and a Scoring Metric (SM). Some studies have already analyzed how this relationship affects the learning process of a BN. However, such investigation had not yet been performed to determine the bond linking the selection of SA and SM and the BOA's output quality. Acting in this research gap, a detailed comparative analysis involving two constructive heuristics and four scoring metrics is presented in this work. The classic version of BOA with binary and floating-point representations was applied to discrete and continuous optimization problems. The scenarios were compared through graphical analyses, statistical metrics, and difference detection tests. The results showed that the choice of the SA and the SM affects the quality of BOA results. Additionally, scoring metrics that penalize complex BN models present better performance. This study contributes to a discussion on this metaheuristic's practical use, assisting users with implementation decisions.

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