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: |
0.2.6 |
| Depends: |
R (≥ 3.1.0) |
| Imports: |
bbotk (≥ 1.1.1), checkmate (≥ 2.0.0), data.table, lgr (≥
0.3.4), mlr3 (≥ 0.21.0), mlr3misc (≥ 0.11.0), mlr3tuning (≥
1.0.2), paradox (≥ 1.0.0), spacefillr, R6 (≥ 2.4.1) |
| Suggests: |
DiceKriging, emoa, fastGHQuad, lhs, mlr3learners (≥ 0.5.4), mlr3pipelines (≥ 0.4.2), nloptr, ranger, rgenoud, rpart, stringi, testthat (≥ 3.0.0) |
| Published: |
2024-10-16 |
| DOI: |
10.32614/CRAN.package.mlr3mbo |
| Author: |
Lennart Schneider 
[cre, aut],
Jakob Richter
[aut],
Marc Becker [aut],
Michel Lang [aut],
Bernd Bischl
[aut],
Florian Pfisterer
[aut],
Martin Binder [aut],
Sebastian Fischer
[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 |