evaluateFitness

This function expects a list of individuals, computes the fitness and always
returns a matrix of fitness values; even in single-objective optimization a
(1 x n) matrix is returned for consistency, where n is the number of individuals.
This function makes use of <code>parallelMap</code> to
parallelize the fitness evaluation.

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

evaluateFitness function

<dl><dt>control</dt>
<dd>[<code>ecr_control</code>] 
Control object.</dd>
<dt>inds</dt>
<dd>[<code>list</code>] 
List of individuals.</dd>
<dt>...</dt>
<dd>[any] 
Optional parameters passed down to fitness function.</dd></dl>

Arguments

Computes the fitness value(s) for each individual of a given set. — evaluateFitness

<dl>

<dt>control</dt>
<dd>[<code>ecr_control</code>] 
Control object.</dd>


<dt>inds</dt>
<dd>[<code>list</code>] 
List of individuals.</dd>


<dt>...</dt>
<dd>[any] 
Optional parameters passed down to fitness function.</dd>

</dl>

evaluateFitness: Computes the fitness value(s) for each individual of a given set.

Description

Usage

Value

Arguments