normalize

Normalization is done by subtracting the <code>min.value</code> for each dimension
and dividing by the difference <code>max.value - min.value</code> for each dimension
Certain EMOA indicators require all elements to be strictly positive. Hence, an optional
<code>offset</code> is added to each element which defaults to zero.

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

normalize function

<dl><dt>x</dt>
<dd>[<code>matrix</code> | <code>data.frame</code>] 
Either a numeric <code>matrix</code> (each column corresponds to a point) or a
<code>data.frame</code> with columns at least <code>obj.cols</code>.</dd>
<dt>obj.cols</dt>
<dd>[<code>character(&gt;= 2)</code>] 
Column names of the objective functions.</dd>
<dt>min.value</dt>
<dd>[<code>numeric</code>] 
Vector of minimal values of length <code>nrow(x)</code>.
Only relevant if <code>x</code> is a matrix.
Default is the row-wise minimum of <code>x</code>.</dd>
<dt>max.value</dt>
<dd>[<code>numeric</code>] 
Vector of maximal values of length <code>nrow(x)</code>.
Only relevant if <code>x</code> is a matrix.
Default is the row-wise maximum of <code>x</code>.</dd>
<dt>offset</dt>
<dd>[<code>numeric</code>] 
Numeric constant added to each normalized element.
Useful to make all objectives strictly positive, e.g., located in \([1,2]\).</dd></dl>

Arguments

Normalize approximations set(s). — normalize

<dl>

<dt>x</dt>
<dd>[<code>matrix</code> | <code>data.frame</code>] 
Either a numeric <code>matrix</code> (each column corresponds to a point) or a
<code>data.frame</code> with columns at least <code>obj.cols</code>.</dd>


<dt>obj.cols</dt>
<dd>[<code>character(&gt;= 2)</code>] 
Column names of the objective functions.</dd>


<dt>min.value</dt>
<dd>[<code>numeric</code>] 
Vector of minimal values of length <code>nrow(x)</code>.
Only relevant if <code>x</code> is a matrix.
Default is the row-wise minimum of <code>x</code>.</dd>


<dt>max.value</dt>
<dd>[<code>numeric</code>] 
Vector of maximal values of length <code>nrow(x)</code>.
Only relevant if <code>x</code> is a matrix.
Default is the row-wise maximum of <code>x</code>.</dd>


<dt>offset</dt>
<dd>[<code>numeric</code>] 
Numeric constant added to each normalized element.
Useful to make all objectives strictly positive, e.g., located in \([1,2]\).</dd>

</dl>

normalize: Normalize approximations set(s).

Description

Usage

Value

Arguments

See Also