doi:10.1023/A:1008306431147>, ParEGO by Knowles (2006) <doi:10.1109/TEVC.2005.851274> and SMS-EGO by Ponweiser et al. (2008) <doi:10.1007/978-3-540-87700-4_78>.">

mlr3mbo: Flexible Bayesian Optimization (original) (raw)

A modern and flexible approach to Bayesian Optimization / Model Based Optimization building on the 'bbotk' package. 'mlr3mbo' is a toolbox providing both ready-to-use optimization algorithms as well as their fundamental building blocks allowing for straightforward implementation of custom algorithms. Single- and multi-objective optimization is supported as well as mixed continuous, categorical and conditional search spaces. Moreover, using 'mlr3mbo' for hyperparameter optimization of machine learning models within the 'mlr3' ecosystem is straightforward via 'mlr3tuning'. Examples of ready-to-use optimization algorithms include Efficient Global Optimization by Jones et al. (1998) <doi:10.1023/A:1008306431147>, ParEGO by Knowles (2006) <doi:10.1109/TEVC.2005.851274> and SMS-EGO by Ponweiser et al. (2008) <doi:10.1007/978-3-540-87700-4_78>.

Version: 0.2.8
Depends: mlr3tuning (≥ 1.1.0), R (≥ 3.1.0)
Imports: bbotk (≥ 1.2.0), checkmate (≥ 2.0.0), data.table, lgr (≥ 0.3.4), mlr3 (≥ 0.21.1), mlr3misc (≥ 0.11.0), paradox (≥ 1.0.1), spacefillr, R6 (≥ 2.4.1)
Suggests: DiceKriging, emoa, fastGHQuad, lhs, mlr3learners (≥ 0.5.4), mlr3pipelines (≥ 0.4.2), nloptr, ranger, rgenoud, rpart, redux, rush, stringi, testthat (≥ 3.0.0)
Published: 2024-11-21
DOI: 10.32614/CRAN.package.mlr3mbo
Author: Lennart Schneider ORCID iD [cre, aut], Jakob Richter ORCID iD [aut], Marc Becker ORCID iD [aut], Michel Lang ORCID iD [aut], Bernd Bischl ORCID iD [aut], Florian Pfisterer ORCID iD [aut], Martin Binder [aut], Sebastian Fischer ORCID iD [aut], Michael H. Buselli [cph], Wessel Dankers [cph], Carlos Fonseca [cph], Manuel Lopez-Ibanez [cph], Luis Paquete [cph]
Maintainer: Lennart Schneider <lennart.sch at web.de>
BugReports: https://github.com/mlr-org/mlr3mbo/issues
License: LGPL-3
URL: https://mlr3mbo.mlr-org.com, https://github.com/mlr-org/mlr3mbo
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: mlr3mbo results

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