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.
