Mateda-2.0: Estimation of distribution algorithms in MATLAB

Roberto Santana, Concha Bielza, Pedro Larrañaga, Jose A. Lozano, Carlos Echegoyen, Alexander Mendiburu, Rubén Armañanzas, Siddartha Shakya

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


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.

Original languageBritish English
Pages (from-to)1-30
Number of pages30
JournalJournal of Statistical Software
Issue number7
StatePublished - Jul 2010


  • Estimation of distribution algorithms
  • Optimization
  • Probabilistic models
  • Statistical learning


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