default_acqoptimizer

Chooses a default acquisition function optimizer.
Defaults to AcqOptimizerLocalSearch with <code>n_searches = 10</code>, <code>n_steps = ceiling(100 * D^2 / 300)</code>, and <code>n_neighs = 30</code>, where <code>D</code> is the dimension of the search space.

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_acqoptimizer function

<dl><dt>acq_function</dt>
<dd>(AcqFunction).</dd>
<dt>instance</dt>
<dd>(bbotk::OptimInstance).</dd></dl>

Arguments

Default Acquisition Function Optimizer — default_acqoptimizer

<dl>

<dt>acq_function</dt>
<dd>(AcqFunction).</dd>


<dt>instance</dt>
<dd>(bbotk::OptimInstance).</dd>

</dl>

Default Acquisition Function Optimizer

default_acqoptimizer: Default Acquisition Function Optimizer

Description

Usage

Value

Arguments

See Also