selNondom

Applies non-dominated sorting of the objective vectors and subsequent crowding
distance computation to select a subset of individuals. This is the selector used
by the famous NSGA-II EMOA (see <code>nsga2</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

selNondom 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></dl>

Arguments

Non-dominated sorting selector. — selNondom

<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>

</dl>

selNondom: Non-dominated sorting selector.

Description

Usage

Value

Arguments

See Also