Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Edzer Pebesma & Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Mateda-2.0: A MATLAB Package for the Implementation and Analysis of Estimation of Distribution Algorithms | Santana | Journal of Statistical Software
Authors: Roberto Santana, Concha Bielza, Pedro Larrañaga, Jose A. Lozano, Carlos Echegoyen, Alexander Mendiburu, Rubén Armañanzas, Siddartha Shakya
Title: Mateda-2.0: A MATLAB Package for the Implementation and Analysis of Estimation of Distribution Algorithms
Abstract: This paper describes Mateda-2.0, a MATLAB package for estimation of distribution algorithms (EDAs). This package can be used to solve single and multi-objective discrete and continuous optimization problems using EDAs based on undirected and directed probabilistic graphical models. The implementation contains several methods commonly employed by EDAs. It is also conceived as an open package to allow users to incorporate different combinations of selection, learning, sampling, and local search procedures. Additionally, it includes methods to extract, process and visualize the structures learned by the probabilistic models. This way, it can unveil previously unknown information about the optimization problem domain. Mateda-2.0 also incorporates a module for creating and validating function models based on the probabilistic models learned by EDAs.

Page views:: 7583. Submitted: 2009-04-15. Published: 2010-07-26.
Paper: Mateda-2.0: A MATLAB Package for the Implementation and Analysis of Estimation of Distribution Algorithms     Download PDF (Downloads: 7550)
Supplements:
Mateda2.0.zip: MATLAB source package Download (Downloads: 1687; 1MB)
v35i07.m: MATLAB example code from the paper Download (Downloads: 820; 4KB)
FindClassifier.m: Supplementary MATLAB code Download (Downloads: 800; 1KB)
lungcancer.dat: Example data set Download (Downloads: 738; 3KB)

DOI: 10.18637/jss.v035.i07

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.