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 multipoint 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.
|Depends:||mlr (≥ 2.10), ParamHelpers (≥ 1.10), smoof (≥ 1.4)|
|Imports:||backports, 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|
|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 jakob-r.de>|
|License:||BSD_2_clause + file LICENSE|
|CRAN checks:||mlrMBO results|
Mixed Space Optimization
|Windows binaries:||r-devel: mlrMBO_1.0.0.zip, r-release: mlrMBO_1.0.0.zip, r-oldrel: mlrMBO_1.0.0.zip|
|OS X El Capitan binaries:||r-release: mlrMBO_1.0.0.tgz|
|OS X Mavericks binaries:||r-oldrel: not available|
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