mcMST

Pareto-front approximations for some graph problems obtained by several
algorithms for the multi-criteria minimum spanning tree (mcMST) problem.

datasets

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

mcMST function

A data frame with four variables:<dl>
 <dt><code>f1</code></dt>
<dd>First objective (to be minimized).</dd> <dt><code>f2</code></dt>
<dd>Second objective (to be minimized).</dd> <dt><code>algorithm</code></dt>
<dd>Short name of algorithm used.</dd> <dt><code>prob</code></dt>
<dd>Short name of problem instance.</dd> <dt><code>repl</code></dt>
<dd>Algorithm run.</dd>
</dl>The data is based on the mcMST package.

Format

mcMST — mcMST

A data frame with four variables:<dl>
 <dt><code>f1</code></dt>
<dd>First objective (to be minimized).</dd>

 <dt><code>f2</code></dt>
<dd>Second objective (to be minimized).</dd>

 <dt><code>algorithm</code></dt>
<dd>Short name of algorithm used.</dd>

 <dt><code>prob</code></dt>
<dd>Short name of problem instance.</dd>

 <dt><code>repl</code></dt>
<dd>Algorithm run.</dd>


</dl>

The data is based on the mcMST package.

mcMST: mcMST

Description

Usage

Arguments

Format