Package: mlr3mbo 1.1.1

mlr3mbo: Flexible Bayesian Optimization

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>.

Authors:Marc Becker [cre, aut], Lennart Schneider [aut], Jakob Richter [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]