getGlobalOpt

Helper function which returns the (estimated) global optimum.

Flexible and comprehensive R toolbox for model-based
optimization ('MBO'), also known as Bayesian optimization. It
implements the Efficient Global Optimization Algorithm and is designed
for both single- and multi- objective optimization with mixed
continuous, categorical and conditional parameters. The machine
learning toolbox 'mlr' provide dozens of regression learners to model
the performance of the target algorithm with respect to the parameter
settings. It provides many different infill criteria to guide the
search process. Additional features include multi-point batch
proposal, parallel execution as well as visualization and
sophisticated logging mechanisms, which is especially useful for
teaching and understanding of algorithm behavior. 'mlrMBO' is
implemented in a modular fashion, such that single components can be
easily replaced or adapted by the user for specific use cases.

Martin Binder

mlrMBO

Bayesian Optimization and Model-Based Optimization of Expensive
Black-Box Functions

Bernd Bischl

Jakob Richter

Jakob Bossek

Daniel Horn

Michel Lang

Janek Thomas

getGlobalOpt function

<dl><dt>run</dt>
<dd>[<code>MBOExampleRun</code>] 
Object of type <code>MBOExampleRun</code>.</dd></dl>

Arguments

Helper function which returns the (estimated) global optimum. — getGlobalOpt

<dl>

<dt>run</dt>
<dd>[<code>MBOExampleRun</code>] 
Object of type <code>MBOExampleRun</code>.</dd>

</dl>

getGlobalOpt: Helper function which returns the (estimated) global optimum.

Description

Usage

Value

Arguments