ecr_parallelization

In ecr it is possible to parallelize different levels of computation
to make use, e.g., of multiple CP cores or nodes in a HPC cluster.
For maximal flexibility this is realized by means of the parallelMap package
(see the <a href="https://github.com/berndbischl/parallelMap">official
GitHub page</a> for instructions on how to set up parallelization).
The different levels of parallelization can be specified in the
<code>parallelStart*</code> function. At them moment the following levels are
available:
<dl>
 
 
</dl>
Keep in mind that parallelization comes along with some overhead. Thus activating
parallelization, e.g., for evaluation a fitness function which is evaluated
lightning-fast, may result in higher computation time. However, if the function
evaluations are computationally more expensive, parallelization leads to
impressive running time benefits.

Provides a powerful framework for evolutionary
computing in R. The user can easily construct powerful evolutionary algorithms
for tackling both single- and multi-objective problems by plugging in different
predefined evolutionary building blocks, e. g., operators for mutation,
recombination and selection with just a few lines of code. Your problem
cannot be easily solved with a standard EA which works on real-valued vectors,
permutations or binary strings? No problem, 'ecr' has been developed with that in
mind. Extending the framework with own operators is also possible. Additionally
there are various comfort functions, like monitoring, logging and more.

Jakob Bossek

Evolutionary Computing in R

ecr_parallelization function

In ecr it is possible to parallelize different levels of computation
to make use, e.g., of multiple CP cores or nodes in a HPC cluster.
For maximal flexibility this is realized by means of the parallelMap package
(see the <a href = 'https://github.com/berndbischl/parallelMap'>official
GitHub page</a> for instructions on how to set up parallelization).
The different levels of parallelization can be specified in the
<code>parallelStart*</code> function. At them moment the following levels are
available:
<dl>
 
 
</dl>


Keep in mind that parallelization comes along with some overhead. Thus activating
parallelization, e.g., for evaluation a fitness function which is evaluated
lightning-fast, may result in higher computation time. However, if the function
evaluations are computationally more expensive, parallelization leads to
impressive running time benefits.

ecr_parallelization: Parallelization in ecr

Description

Arguments