mbo_parallel

In mlrMBO you can parallelize the tuning on two different levels to speed up computation:<ul>
<li><code>mlrMBO.feval</code>Multiple evaluations of the target function.</li>
<li><code>mlrMBO.propose.points</code>Optimization of the infill criteria if multiple are used (e.g. ParEGO and ParallelLCB)</li>
</ul>Internally the evaluation of the target function is realized with the R package parallelMap.
See the mlrMBO tutorial and the Github project pages of parallelMap for instructions on how to set up parallelization.
The different levels of parallelization can be specified in <code>parallelStart*</code>.
Details for the levels mentioned above are given below:<ul>
<li>Evaluation of the objective function can be parallelized in cases multiple points are to be evaluated at once. These are: evaluation of the initial design, multiple proposed points per iteration and evaluation of the target function in <code><a rd-options="" href="/link/exampleRun?package=mlrMBO&version=1.1.5" data-mini-rdoc="mlrMBO::exampleRun">exampleRun</a></code>. (Level: <code>mlrMBO.feval</code>)</li>
<li>Model fitting / point proposal - in some cases where independent, expensive operations are performed. (Level: <code>mlrMBO.propose.points</code>)</li>
</ul>Details regarding the latter:<dl class="dl-horizontal">
<dt>single-objective MBO with LCB multi-point</dt><dd>Parallel optimization of LCBs for the lambda-values.</dd>
<dt>Multi-objective MBO with ParEGO</dt><dd>Parallel optimization of scalarization functions.</dd>
</dl>

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

mbo_parallel function

In mlrMBO you can parallelize the tuning on two different levels to speed up computation:<ul>
<li><code>mlrMBO.feval</code>Multiple evaluations of the target function.</li>
<li><code>mlrMBO.propose.points</code>Optimization of the infill criteria if multiple are used (e.g. ParEGO and ParallelLCB)</li>
</ul>Internally the evaluation of the target function is realized with the R package parallelMap.
See the mlrMBO tutorial and the Github project pages of parallelMap for instructions on how to set up parallelization.
The different levels of parallelization can be specified in <code>parallelStart*</code>.
Details for the levels mentioned above are given below:<ul>
<li>Evaluation of the objective function can be parallelized in cases multiple points are to be evaluated at once. These are: evaluation of the initial design, multiple proposed points per iteration and evaluation of the target function in <code><a rd-options='' href='exampleRun'>exampleRun</a></code>. (Level: <code>mlrMBO.feval</code>)</li>
<li>Model fitting / point proposal - in some cases where independent, expensive operations are performed. (Level: <code>mlrMBO.propose.points</code>)</li>
</ul>Details regarding the latter:<dl class='dl-horizontal'>
<dt>single-objective MBO with LCB multi-point</dt><dd>Parallel optimization of LCBs for the lambda-values.</dd>
<dt>Multi-objective MBO with ParEGO</dt><dd>Parallel optimization of scalarization functions.</dd>
</dl>

Parallelization in mlrMBO — mbo_parallel

Parallelization in mlrMBO

mbo_parallel: Parallelization in mlrMBO

Description

Arguments