The Bayesian Optimization Algorithm (BOA) is one of the most prominent Estimation of Distribution Algorithms. It can detect the correlation between multiple variables and extract knowledge on 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 trade-off is determined by combining a search algorithm (SA) and a scoring metric (SM). The SA is responsible for generating a promising BN and the SM assesses the quality of such networks. 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 on 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 was applied to discrete and continuous optimization problems using binary and floating-point representations. The scenarios were compared through graphical analyses, statistical metrics, and difference detection tests. The results showed that the selection of SA and SM affects the quality of the BOA results since scoring metrics that penalize complex BN models perform better than metrics that do not consider the complexity of the networks. This study contributes to a discussion on this metaheuristic’s practical use, assisting users with implementation decisions.

贝叶斯优化算法 (BOA) 是最突出的分布估计估计算法之一。它可以检测多个变量之间的相关性并提取解决方案中规则模式的知识。BOA 中使用贝叶斯网络 (BN) 来表示最佳个体的概率分布。BN 的构建具有挑战性，因为在生成它时需要在敏锐度和计算成本之间进行权衡。这种权衡是通过结合搜索算法 (SA) 和评分度量 (SM) 来确定的。SA 负责生成有前途的 BN，SM 评估此类网络的质量。一些研究已经分析了这种关系如何影响 BN 的学习过程。然而，尚未进行此类调查以确定 SA 和 SM 的选择与 BOA 的输出质量之间的联系。针对这一研究空白，在这项工作中提出了涉及两个建设性启发式和四个评分指标的详细比较分析。BOA 的经典版本适用于使用二进制和浮点表示的离散和连续优化问题。通过图形分析、统计指标和差异检测测试对场景进行了比较。结果表明，SA 和 SM 的选择会影响 BOA 结果的质量，因为惩罚复杂 BN 模型的评分指标比不考虑网络复杂性的指标表现更好。这项研究有助于讨论这种元启发式的实际用途，