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 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.


Reference manual

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1.1.0 by Jakob Richter, a month ago


Report a bug at https://github.com/mlr-org/mlrMBO/issues

Browse source code at https://github.com/cran/mlrMBO

Authors: Bernd Bischl [aut], Jakob Bossek [aut], Jakob Richter [aut, cre], Daniel Horn [aut], Michel Lang [aut], Janek Thomas [aut]

Documentation:   PDF Manual  

BSD_2_clause + file LICENSE license

Imports backports, BBmisc, checkmate, data.table, lhs, parallelMap

Depends on mlr, ParamHelpers, smoof

Suggests akima, cmaesr, ggplot2, RColorBrewer, DiceKriging, DiceOptim, earth, emoa, GGally, gridExtra, kernlab, kknn, knitr, mco, nnet, party, randomForest, rmarkdown, rpart, testthat, eaf, covr

Suggested by mlr.

See at CRAN