We present mlr3mbo, a comprehensive and modular toolbox for Bayesian optimization in R. mlr3mbo supports single- and multi-objective optimization, multi-point proposals, batch and asynchronous parallelization, input and output transformations, and robust error handling. While it can be used for many standard Bayesian optimization variants in applied settings, researchers can also construct custom BO algorithms from its flexible building blocks. In addition to an introduction to the software, its design principles, and its building blocks, the paper presents two extensive empirical evaluations of the software on the surrogate-based benchmark suite YAHPO Gym. To identify robust default configurations for both numeric and mixed-hierarchical optimization regimes, and to gain further insights into the respective impacts of individual settings, we run a coordinate descent search over the mlr3mbo configuration space and analyze its results. Furthermore, we demonstrate that mlr3mbo achieves state-of-the-art performance by benchmarking it against a wide range of optimizers, including HEBO, SMAC3, Ax, and Optuna.

翻译：本文介绍mlr3mbo，一个用于R语言贝叶斯优化的综合性模块化工具箱。mlr3mbo支持单目标和多目标优化、多点建议、批量与异步并行化、输入输出变换以及鲁棒的错误处理。该工具既可应用于多种标准贝叶斯优化变体的实际场景，研究者也能利用其灵活的模块构建自定义BO算法。除介绍软件的设计原则与构成模块外，本文在基于代理模型的基准测试套件YAHPO Gym上进行了两项广泛的实证评估：通过坐标下降法搜索mlr3mbo配置空间并分析结果，以确定数值优化和混合分层优化场景下的稳健默认配置；同时通过对比HEBO、SMAC3、Ax和Optuna等广泛的优化器，证明mlr3mbo达到了当前最优性能。