default_loop_function

Chooses a default loop_function, i.e. the Bayesian Optimization flavor to be used for optimization.
For single-objective optimization, defaults to bayesopt_ego.
For multi-objective optimization, defaults to bayesopt_smsego.

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

default_loop_function function

<dl><dt>instance</dt>
<dd>(bbotk::OptimInstance) 
An object that inherits from bbotk::OptimInstance.</dd></dl>

Arguments

Default Loop Function — default_loop_function

<dl>

<dt>instance</dt>
<dd>(bbotk::OptimInstance) 
An object that inherits from bbotk::OptimInstance.</dd>

</dl>

Default Loop Function

default_loop_function: Default Loop Function

Description

Usage

Value

Arguments

See Also