mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions
This provides a flexible and comprehensive tool for researchers and practitioners needing efficient optimization of complex functions, though it is incremental as it builds on existing model-based optimization methods.
The authors tackled the problem of optimizing expensive black-box functions by developing mlrMBO, a modular R toolbox for model-based optimization, and demonstrated that it achieves state-of-the-art performance in benchmark comparisons against other optimizers.
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.