updateSMBO

[<code><a rd-options="" href="/link/OptState?package=mlrMBO&version=1.1.5" data-mini-rdoc="mlrMBO::OptState">OptState</a></code>]
The optimization state.
Generated by <code><a rd-options="" href="/link/initSMBO?package=mlrMBO&version=1.1.5" data-mini-rdoc="mlrMBO::initSMBO">initSMBO</a></code>, this function or an <code><a rd-options="" href="/link/mbo?package=mlrMBO&version=1.1.5" data-mini-rdoc="mlrMBO::mbo">mbo</a></code> run.

opt.state

[<code>data.frame</code>]
Named x values.
One row per set of x values.

[<code>numeric|list</code>]
Outcome of the optimization.
For multiple results use a list.
For a result of a multi-objective function use a numeric vector.

After a function evaluation you want to update the <code><a rd-options="" href="/link/OptState?package=mlrMBO&version=1.1.5" data-mini-rdoc="mlrMBO::OptState">OptState</a></code> to get new proposals.

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

updateSMBO function

[<code><a rd-options='' href='OptState'>OptState</a></code>]
The optimization state.
Generated by <code><a rd-options='' href='initSMBO'>initSMBO</a></code>, this function or an <code><a rd-options='' href='mbo'>mbo</a></code> run.

After a function evaluation you want to update the <code><a rd-options='' href='OptState'>OptState</a></code> to get new proposals.

updateSMBO: Updates SMBO with the new observations

Description

Usage

Arguments

Value