mutBitflipCHW

<code>[integer]</code> binary individual.

<code>[numeric]</code> average flip probability, must be between 0
and 0.5.

further arguments passed on to the method.

If a given bitvector has <code>m</code> 1s and <code>n</code> 0s, then a bit is flipped from
0 to 1 with probability <code>2p(m+1)/(m+n+2)</code> and from
1 to 0 with probability <code>2p(n+1)/(m+n+2)</code>. This is
equivalent with choosing bits uniformly at random with probability
2 * <code>p</code> and drawing them from a bernoulli-distribution with parameter
<code>(m+1)/(m+n+2)</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

mutBitflipCHW function

<dl><dt>ind</dt>
<dd><code>[integer]</code> binary individual.</dd>
<dt>p</dt>
<dd><code>[numeric]</code> average flip probability, must be between 0
and 0.5.</dd>
<dt>...</dt>
<dd>further arguments passed on to the method.</dd></dl>

Arguments

Bitflip (Approximately, in Expectation) Conserving Hamming Weight — mutBitflipCHW

<dl>

<dt>ind</dt>
<dd><code>[integer]</code> binary individual.</dd>


<dt>p</dt>
<dd><code>[numeric]</code> average flip probability, must be between 0
and 0.5.</dd>


<dt>...</dt>
<dd>further arguments passed on to the method.</dd>

</dl>

mutBitflipCHW: Bitflip (Approximately, in Expectation) Conserving Hamming Weight

Description

Usage

Value

Arguments