mlrMBO

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Model-based optimization with mlr.

Installation

We reccomend to install the official release version:

install.packages("mlrMBO")

For experimental use you can install the latest development version:

devtools::install_github("mlr-org/mlrMBO")

Introduction

MBO demo
MBO demo

mlrMBO is a highly configurable R toolbox for model-based / Bayesian optimization of black-box functions.

Features:

For the surrogate, mlrMBO allows any regression learner from mlr, including: * Kriging aka. Gaussian processes (i.e. DiceKriging) * random Forests (i.e. randomForest) * and many moreā€¦

Various infill criteria (aka. acquisition functions) are available: * Expected improvement (EI) * Upper/Lower confidence bound (LCB, aka. statistical lower or upper bound) * Augmented expected improvement (AEI) * Expected quantile improvement (EQI) * API for custom infill criteria

Objective functions are created with package smoof, which also offers many test functions for example runs or benchmarks.

Parameter spaces and initial designs are created with package ParamHelpers.

mlrMBO - How to Cite and Citing Publications

Please cite our arxiv paper (Preprint). You can get citation info via citation("mlrMBO") or copy the following BibTex entry:

@article{mlrMBO,
  title = {{{mlrMBO}}: {{A Modular Framework}} for {{Model}}-{{Based Optimization}} of {{Expensive Black}}-{{Box Functions}}},
  url = {http://arxiv.org/abs/1703.03373},
  shorttitle = {{{mlrMBO}}},
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1703.03373},
  primaryClass = {stat},
  author = {Bischl, Bernd and Richter, Jakob and Bossek, Jakob and Horn, Daniel and Thomas, Janek and Lang, Michel},
  date = {2017-03-09},
}

Some parts of the package were created as part of other publications. If you use these parts, please cite the relevant work appropriately: