mutPolynomial

Performs an polynomial mutation as used in the SMS-EMOA algorithm.
Polynomial mutation tries to simulate the distribution of the offspring of 
binary-encoded bit flip mutations based on real-valued decision variables. 
Polynomial mutation favors offspring nearer to the parent.

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

mutPolynomial 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 each gene of an offspring. In other words, 
the probability that the value (allele) of a given gene will change.
Default is 0.2</dd>
<dt>eta</dt>
<dd>[<code>numeric(1)</code> 
Distance parameter to control the shape of the mutation distribution. 
Larger values generate offspring closer to the parents.
Default is 10.</dd>
<dt>lower</dt>
<dd>[<code>numeric</code>] 
Vector of minimal values for each parameter of the decision space.
Must have the same length as <code>ind</code>.</dd>
<dt>upper</dt>
<dd>[<code>numeric</code>] 
Vector of maximal values for each parameter of the decision space.
Must have the same length as <code>ind</code>.</dd></dl>

Arguments

Polynomial mutation. — mutPolynomial

<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 each gene of an offspring. In other words, 
the probability that the value (allele) of a given gene will change.
Default is 0.2</dd>


<dt>eta</dt>
<dd>[<code>numeric(1)</code> 
Distance parameter to control the shape of the mutation distribution. 
Larger values generate offspring closer to the parents.
Default is 10.</dd>


<dt>lower</dt>
<dd>[<code>numeric</code>] 
Vector of minimal values for each parameter of the decision space.
Must have the same length as <code>ind</code>.</dd>


<dt>upper</dt>
<dd>[<code>numeric</code>] 
Vector of maximal values for each parameter of the decision space.
Must have the same length as <code>ind</code>.</dd>

</dl>

mutPolynomial: Polynomial mutation.

Description

Usage

Value

Arguments

References

See Also