selDomHV

Performs non-dominated sorting and drops the individual from the last front
with minimal hypervolume contribution. This selector is the basis of the
S-Metric Selection Evolutionary Multi-Objective Algorithm, termed SMS-EMOA
(see <code>smsemoa</code>).

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

selDomHV function

<dl><dt>fitness</dt>
<dd>[<code>matrix</code>] 
Matrix of fitness values (each column contains the fitness value(s) of one
individual).</dd>
<dt>n.select</dt>
<dd>[<code>integer(1)</code>] 
Number of elements to select.</dd>
<dt>ref.point</dt>
<dd>[<code>numeric</code>] 
Reference point for hypervolume computation.</dd></dl>

Arguments

Dominated Hypervolume selector. — selDomHV

<dl>

<dt>fitness</dt>
<dd>[<code>matrix</code>] 
Matrix of fitness values (each column contains the fitness value(s) of one
individual).</dd>


<dt>n.select</dt>
<dd>[<code>integer(1)</code>] 
Number of elements to select.</dd>


<dt>ref.point</dt>
<dd>[<code>numeric</code>] 
Reference point for hypervolume computation.</dd>

</dl>

selDomHV: Dominated Hypervolume selector.

Description

Usage

Value

Arguments

See Also