mlrMBO: Bayesian Optimization and Model-Based Optimization of Expensive Black-Box Functions (original) (raw)

Flexible and comprehensive R toolbox for model-based optimization ('MBO'), also known as Bayesian optimization. It implements the Efficient Global Optimization Algorithm and 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: 1.1.5.1
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: cmaesr (≥ 1.0.3), ggplot2, DiceKriging, earth, emoa, GGally, gridExtra, kernlab, kknn, knitr, mco, nnet, party, randomForest, reshape2, rmarkdown, rgenoud, rpart, testthat, covr
Published: 2022-07-04
DOI: 10.32614/CRAN.package.mlrMBO
Author: Bernd Bischl ORCID iD [aut], Jakob Richter ORCID iD [aut, cre], Jakob Bossek ORCID iD [aut], Daniel Horn [aut], Michel Lang ORCID iD [aut], Janek Thomas ORCID iD [aut]
Maintainer: Jakob Richter
BugReports: https://github.com/mlr-org/mlrMBO/issues
License: BSD_2_clause + file
URL: https://github.com/mlr-org/mlrMBO
NeedsCompilation: yes
Citation: mlrMBO citation info
Materials: README NEWS
In views: Optimization
CRAN checks: mlrMBO results

Documentation:

Downloads:

Reverse dependencies:

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=mlrMBOto link to this page.