Matlab toolbox for Estimation of Distribution Algorithms  (MATEDA-3.0)

The new version of MATEDA is now available from github. It has been updated for Matlab2020 and new functionalities have been added, including the probabilistic models for permutation representation as presented in Algorithm 989: perm_mateda: A Matlab Toolbox of Estimation of Distribution Algorithms for Permutation-based Combinatorial Optimization Problems.

The description, program documentation, and examples described below for MATEDA-2.0, are still useful to understand and apply the new version of MATEDA. However, all dependencies are now included within the release and the installation process has been made simpler.

  (MATEDA-2.0) [Not compatible with most recent versions of Matlab]

The package allows the optimization of single and multi-objective problems with estimation of distribution algorithms (EDAs) based on undirected graphical models and Bayesian networks. The implementation is conceived for allowing the incorporation by the user of different combinations of selection, learning, sampling, and local search procedures. Other included methods allow the analysis of the structures learned by the probabilistic models, the visualization of particular features of these structures and the use of the probabilistic models as fitness modeling tools.

        Back to main page