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