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

Flexible and comprehensive R toolbox for model-based optimization ('MBO'), also known as Bayesian optimization. It is designed for both single- and multi-objective optimization with mixed continuous, categorical and conditional parameters. The machine learning toolbox 'mlr' provide dozens of regression learners to model the performance of the target algorithm with respect to the parameter settings. It provides many different infill criteria to guide the search process. Additional features include multi-point batch proposal, parallel execution as well as visualization and sophisticated logging mechanisms, which is especially useful for teaching and understanding of algorithm behavior. 'mlrMBO' is implemented in a modular fashion, such that single components can be easily replaced or adapted by the user for specific use cases.

Version: 1.1.1
Depends: mlr (≥ 2.10), ParamHelpers (≥ 1.10), smoof (≥ 1.5.1)
Imports: backports (≥ 1.1.0), BBmisc (≥ 1.11), checkmate (≥ 1.8.2), data.table, lhs, parallelMap (≥ 1.3)
Suggests: akima, cmaesr (≥ 1.0.3), ggplot2, RColorBrewer, DiceKriging, DiceOptim, earth, emoa, GGally, gridExtra, kernlab, kknn, knitr, mco, nnet, party, randomForest, rmarkdown, rpart, testthat, eaf, covr
Published: 2018-01-02
Author: Bernd Bischl [aut], Jakob Bossek [aut], Jakob Richter [aut, cre], Daniel Horn [aut], Michel Lang [aut], Janek Thomas [aut]
Maintainer: Jakob Richter <code at>
License: BSD_2_clause + file LICENSE
NeedsCompilation: yes
Citation: mlrMBO citation info
Materials: README NEWS
In views: Optimization
CRAN checks: mlrMBO results


Reference manual: mlrMBO.pdf
Vignettes: Quick introduction
Package source: mlrMBO_1.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: mlrMBO_1.1.1.tgz
OS X Mavericks binaries: r-oldrel: not available
Old sources: mlrMBO archive

Reverse dependencies:

Reverse depends: tuneRanger
Reverse suggests: mlr


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