initCrit

[<code><a rd-options="" href="/link/MBOInfillCrit?package=mlrMBO&version=1.1.5" data-mini-rdoc="mlrMBO::MBOInfillCrit">MBOInfillCrit</a></code>]
Uninitialized infill criterion.

crit

[<code>smoof_function</code>]
Fitness function to optimize.

design

[<code><a rd-options="mlr" href="/link/Learner?package=mlrMBO&version=1.1.5&to=mlr" data-mini-rdoc="mlr::Learner">Learner</a></code>]
Regression learner from mlr, which is used as a surrogate to model our fitness function.

learner

[<code><a rd-options="" href="/link/MBOControl?package=mlrMBO&version=1.1.5" data-mini-rdoc="mlrMBO::MBOControl">MBOControl</a></code>]
MBO control object.

control

Some infill criteria have parameters that are dependent on values in the parameter set, design,
 used learner or other control settings.
 To actually set these default values, this function is called, which returns a fully
 initialized [<code><a rd-options="" href="/link/MBOInfillCrit?package=mlrMBO&version=1.1.5" data-mini-rdoc="mlrMBO::MBOInfillCrit">MBOInfillCrit</a></code>].
 This function is mainly for internal use. If a custom infill criterion is created, it may be
 required to create a separate method <code>initCrit.InfillCritID</code> where <code>ID</code> is the
 <code>id</code> of the custom <a rd-options="" href="/link/MBOInfillCrit?package=mlrMBO&version=1.1.5" data-mini-rdoc="mlrMBO::MBOInfillCrit">MBOInfillCrit</a>.

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.

Jakob Richter

mlrMBO

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

Bernd Bischl

Jakob Bossek

Daniel Horn

Michel Lang

Janek Thomas

initCrit function

[<code><a rd-options='' href='MBOInfillCrit'>MBOInfillCrit</a></code>]
Uninitialized infill criterion.

[<code><a rd-options='mlr' href='Learner'>Learner</a></code>]
Regression learner from mlr, which is used as a surrogate to model our fitness function.

[<code><a rd-options='' href='MBOControl'>MBOControl</a></code>]
MBO control object.

Some infill criteria have parameters that are dependent on values in the parameter set, design,
 used learner or other control settings.
 To actually set these default values, this function is called, which returns a fully
 initialized [<code><a rd-options='' href='MBOInfillCrit'>MBOInfillCrit</a></code>].
 This function is mainly for internal use. If a custom infill criterion is created, it may be
 required to create a separate method <code>initCrit.InfillCritID</code> where <code>ID</code> is the
 <code>id</code> of the custom <a rd-options='' href='MBOInfillCrit'>MBOInfillCrit</a>.

initCrit: Initialize an MBO infill criterion.

Description

Usage

Arguments

Value