makeFilterStrategy

<code>[matrix]</code> see <code>reset.dists</code> in <a rd-options="" href="/link/mutUniformMetaReset?package=mosmafs&version=0.1.2" data-mini-rdoc="mosmafs::mutUniformMetaReset">mutUniformMetaReset</a>.

reset.dists

<code>[character(1)]</code> name of parameter to use as
<code>reset.dist.weights</code> in <a rd-options="" href="/link/mutUniformMetaReset?package=mosmafs&version=0.1.2" data-mini-rdoc="mosmafs::mutUniformMetaReset">mutUniformMetaReset</a>.

weight.param.name

Creates a strategy function that uses the <code>weight.param.name</code> entry of
individuals as a weighting vector <code>reset.dist.weights</code> and <code>reset.dists</code>
for <code>mutUniformMetaReset</code> and <code>mutUniformMetaResetSHW</code>.

Performs simultaneous hyperparameter tuning and
feature selection through both single-objective and multi-objective
optimization as described in Binder, Moosbauer et al. (2019) <arXiv:1912.12912>.
Uses the 'ecr'-package as basis but adds mixed integer
evolutionary strategies and multi-fidelity functionality as well as operators
specific for the problem of feature selection.

Martin Binder

mosmafs

Multi-Objective Simultaneous Model and Feature Selection

Susanne Dandl

Julia Moosbauer

makeFilterStrategy function

<dl><dt>reset.dists</dt>
<dd><code>[matrix]</code> see <code>reset.dists</code> in mutUniformMetaReset.</dd>
<dt>weight.param.name</dt>
<dd><code>[character(1)]</code> name of parameter to use as
<code>reset.dist.weights</code> in mutUniformMetaReset.</dd></dl>

Arguments

Create a Filter Strategy Function — makeFilterStrategy

<dl>

<dt>reset.dists</dt>
<dd><code>[matrix]</code> see <code>reset.dists</code> in mutUniformMetaReset.</dd>


<dt>weight.param.name</dt>
<dd><code>[character(1)]</code> name of parameter to use as
<code>reset.dist.weights</code> in mutUniformMetaReset.</dd>

</dl>

makeFilterStrategy: Create a Filter Strategy Function

Description

Usage

Value

Arguments