loop_function

Loop functions determine the behavior of the Bayesian Optimization algorithm on a global level.
For an overview of readily available loop functions, see <code>as.data.table(mlr_loop_functions)</code>.
In general, a loop function is simply a decorated member of the S3 class <code>loop_function</code>.
Attributes must include: <code>id</code> (id of the loop function), <code>label</code> (brief description), <code>instance</code> ("single-crit" and
or "multi_crit"), and <code>man</code> (link to the manual page).
As an example, see, e.g., bayesopt_ego.

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>.

Marc Becker

mlr3mbo

Flexible Bayesian Optimization

Lennart Schneider

Jakob Richter

Michel Lang

Bernd Bischl

Florian Pfisterer

Martin Binder

Sebastian Fischer

Michael H. Buselli

Wessel Dankers

Carlos Fonseca

Manuel Lopez-Ibanez

Luis Paquete

loop_function function

Loop Functions for Bayesian Optimization — loop_function

Loop Functions for Bayesian Optimization

loop_function: Loop Functions for Bayesian Optimization

Description

Arguments

See Also