moocore: Core Algorithms for Multi-Objective Optimization

Contributors: Manuel López-Ibáñez, Carlos M. Fonseca, Luís Paquete, and Mickaël Binois.

Introduction

The goal of moocore is to collect fast implementations of core mathematical functions and algorithms for multi-objective optimization. These functions include:

Identifying and filtering dominated vectors.
Quality metrics such as (weighted) hypervolume, epsilon, IGD, etc.
Computation of the Empirical Attainment Function. The empirical attainment function (EAF) describes the probabilistic distribution of the outcomes obtained by a stochastic algorithm in the objective space.

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

Relevant literature:

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 ]

Download and installation

The moocore package is implemented in R. Therefore, a basic knowledge of R is recommended to make use of all features.

The first step before installing the moocore package is to install R. Once R is installed in the system, there are two methods for installing the moocore package:

Install within R (automatic download, internet connection required). Invoke R, then
```
    install.packages("moocore")
```
Download the moocore package from CRAN (you may also need to download and install first the package matrixStats), and invoke 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).

Search the R documentation if you need more help to install an R package on your system.

Compiled command-line executables for various purposes can be found under system.file(package="moocore", "bin").

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

[ Download moocore package from CRAN ] [ Documentation ] [ Development version (GitHub) ]

GitHub (Development version)

If you wish to try the development version, you can install it by executing the following commands within the R console:

    R> install.packages("devtools")
    R> devtools::install_github("multi-objective/moocore")

Usage

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

    library(moocore)
    ?eaf
    ?read_datasets

Python

There is also a Python moocore package: https://multi-objective.github.io/moocore/python

License

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:

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. doi: 10.1007/978-3-642-02538-9_9

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