OptState

<p>The OptState is the central component of the mbo iterations.
This environment contains every necessary information needed during optimization in MBO.
It also links to the <code><a rd-options="" href="/link/OptProblem?package=mlrMBO&version=1.1.2" data-mini-rdoc="mlrMBO::OptProblem">OptProblem</a></code> and to the <code><a rd-options="" href="/link/OptResult?package=mlrMBO&version=1.1.2" data-mini-rdoc="mlrMBO::OptResult">OptResult</a></code>.</p>

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

OptState function

<p>The OptState is the central component of the mbo iterations.
This environment contains every necessary information needed during optimization in MBO.
It also links to the <code><a rd-options='' href='OptProblem'>OptProblem</a></code> and to the <code><a rd-options='' href='OptResult'>OptResult</a></code>.</p>

OptState object. — OptState

OptState object.

OptState: OptState object.

Description

Arguments