mutGauss

Default Gaussian mutation operator known from Evolutionary Algorithms.
This mutator is applicable only for <code>representation="float"</code>. Given
an individual \(\mathbf{x} \in R^l\) this mutator adds a Gaussian
distributed random value to each component of \(\mathbf{x}\), i.~e.,
\(\tilde{\mathbf{x}}_i = \mathbf{x}_i + \sigma \mathcal{N}(0, 1)\).

Framework for building evolutionary algorithms for both single- and multi-objective continuous or discrete optimization problems. A set of predefined evolutionary building blocks and operators is included. Moreover, the user can easily set up custom objective functions, operators, building blocks and representations sticking to few conventions. The package allows both a black-box approach for standard tasks (plug-and-play style) and a much more flexible white-box approach where the evolutionary cycle is written by hand.

Jakob Bossek

Evolutionary Computation in R

Michael H. Buselli

Wessel Dankers

Carlos M. Fonseca

Manuel Lopez-Ibanez

Luis Paquete

Joshua Knowles

Eckart Zitzler

Olaf Mersmann

mutGauss function

<dl><dt>ind</dt>
<dd>[<code>numeric</code>] 
Numeric vector / individual to mutate.</dd>
<dt>p</dt>
<dd>[<code>numeric(1)</code>] 
Probability of mutation for the gauss mutation operator.</dd>
<dt>sdev</dt>
<dd>[<code>numeric(1)</code> 
Standard deviance of the Gauss mutation, i. e., the mutation strength.</dd>
<dt>lower</dt>
<dd>[<code>numeric</code>] 
Vector of minimal values for each parameter of the decision space.</dd>
<dt>upper</dt>
<dd>[<code>numeric</code>] 
Vector of maximal values for each parameter of the decision space.</dd></dl>

Arguments

Gaussian mutator. — mutGauss

<dl>

<dt>ind</dt>
<dd>[<code>numeric</code>] 
Numeric vector / individual to mutate.</dd>


<dt>p</dt>
<dd>[<code>numeric(1)</code>] 
Probability of mutation for the gauss mutation operator.</dd>


<dt>sdev</dt>
<dd>[<code>numeric(1)</code> 
Standard deviance of the Gauss mutation, i. e., the mutation strength.</dd>


<dt>lower</dt>
<dd>[<code>numeric</code>] 
Vector of minimal values for each parameter of the decision space.</dd>


<dt>upper</dt>
<dd>[<code>numeric</code>] 
Vector of maximal values for each parameter of the decision space.</dd>

</dl>

mutGauss: Gaussian mutator.

Description

Usage

Value

Arguments

References

See Also