mbo_defaults

The following defaults are set for OptimizerMbo during optimization if the
respective fields are not set during initialization.<ul>
<li>Optimization Loop: default_loop_function </li>
<li>Surrogate: default_surrogate </li>
<li>Acquisition Function: default_acqfunction </li>
<li>Acqfun Optimizer: default_acqoptimizer </li>
<li>Result Assigner: default_result_assigner </li>
</ul>

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>.

Lennart Schneider

mlr3mbo

Flexible Bayesian Optimization

Jakob Richter

Marc Becker

Michel Lang

Bernd Bischl

Florian Pfisterer

Martin Binder

Sebastian Fischer

Michael H. Buselli

Wessel Dankers

Carlos Fonseca

Manuel Lopez-Ibanez

Luis Paquete

mbo_defaults function

The following defaults are set for OptimizerMbo during optimization if the
respective fields are not set during initialization.<ul>
<li>Optimization Loop: default_loop_function </li>
<li>Surrogate: default_surrogate </li>
<li>Acquisition Function: default_acqfunction </li>
<li>Acqfun Optimizer: default_acqoptimizer </li>
<li>Result Assigner: default_result_assigner </li>
</ul>

Defaults for OptimizerMbo — mbo_defaults

Defaults for OptimizerMbo

mbo_defaults: Defaults for OptimizerMbo

Description

Arguments

See Also