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Bischl/etal/2017a: mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions

Bibtype Misc
Bibkey Bischl/etal/2017a
Author Bischl, Bernd and Richter, Jakob and Bossek, Jakob and Horn, Daniel and Thomas, Janek and Lang, Michel
Title {mlrMBO}: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions
Journal arXiv preprint arXiv:1703.03373
Abstract 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.
Year 2017
Projekt SFB876,SFB876-A3
Url http://arxiv.org/abs/1703.03373
Bibtex Here you can get this literature entry as BibTeX format.