EAF differences between two bi-objective optimizers

EAF Graphical Tools

Maintainer: Manuel López-Ibáñez.

Contributors:
Carlos M. Fonseca, Luís Paquete, Thomas Stützle, Manuel López-Ibáñez and Marco Chiarandini.

Introduction

The empirical attainment function (EAF) describes the probabilistic distribution of the outcomes obtained by a stochastic algorithm in the objective space. This R package implements plots of summary attainment surfaces and differences between the first-order EAFs. These plots may be used for exploring the performance of stochastic local search algorithms for biobjective optimization problems and help in identifying certain algorithmic behaviors in a graphical way.

The corresponding book chapter explains the use of these visualization tools and illustrate them with examples arising from practice.

Keywords: empirical attainment function, summary attainment surfaces, EAF differences, multi-objective optimization, bi-objective optimization, performance measures, performance assessment, graphical analysis, visualization.

Relevant literature:

[1]
Manuel López-Ibáñez, Luís Paquete, and Thomas Stützle. Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, editors, Experimental Methods for the Analysis of Optimization Algorithms, pages 209–222. Springer, Berlin, Germany, 2010.
(This chapter is also available in a slightly extended form as Technical Report TR/IRIDIA/2009-015).
[ bibtex | doi: 10.1007/978-3-642-02538-9_9 ] [ Presentation ]

Description

Once installed, the following R commands will give more information :

   library(eaf)
   ?eaf
   ?eafplot
   ?eafdiffplot
   ?read.data.sets
   example(eafplot)
   example(eafdiffplot) # This one takes some time

Apart from the main R package, the source code contains the following extras (after installation, these files can be found at system.file(package="eaf")):

For more information, consult the README files at each subdirectory.

License

This software is Copyright (C) 2011 Carlos M. Fonseca, Luís Paquete, Thomas Stützle, Manuel López-Ibáñez and Marco Chiarandini.

This program is free software (software libre); you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

IMPORTANT NOTE: Please be aware that the fact that this program is released as Free Software does not excuse you from scientific propriety, which obligates you to give appropriate credit! If you write a scientific paper describing research that made substantive use of this program, it is your obligation as a scientist to (a) mention the fashion in which this software was used in the Methods section; (b) mention the algorithm in the References section. The appropriate citation is:

Moreover, as a personal note, I would appreciate it if you would email manuel.lopez-ibanez@ulb.ac.be with citations of papers referencing this work so I can mention them to my funding agent and tenure committee.

Download

If you wish to be notified of bugfixes and new versions, please subscribe to the low-volume emo-list, where announcements will be made.

The software is provided as an R package available from CRAN. This means that it can be installed by invoking R and within R calling:

   install.packages("modeltools", "eaf")

Or it can also be installed by downloading the package and invoking at the command-line:

   R CMD INSTALL <package>

where <package> is one of the three versions available: .tar.gz (Unix/BSD/GNU/Linux), .tgz (MacOS X), or .zip (Windows).

[ Download eaf package from CRAN ] [ Download Reference manual ]