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We present mlrMBO, a flexible and comprehensive R toolbox for model-basedoptimization (MBO), also known as Bayesian optimization, which addresses theproblem of expensive black-box optimization by approximating the givenobjective function through a surrogate regression model. It is designed forboth single- and multi-objective optimization with mixed continuous,categorical and conditional parameters. Additional features include multi-pointbatch proposal, parallelization, visualization, logging and error-handling.mlrMBO is implemented in a modular fashion, such that single components can beeasily replaced or adapted by the user for specific use cases, e.g., anyregression learner from the mlr toolbox for machine learning can be used, andinfill criteria and infill optimizers are easily exchangeable. We empiricallydemonstrate that mlrMBO provides state-of-the-art performance by comparing iton different benchmark scenarios against a wide range of other optimizers,including DiceOptim, rBayesianOptimization, SPOT, SMAC, Spearmint, andHyperopt.

mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions