## Description of MATEDA

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MATEDA comprises a set of matlab programs that implement different EDAs. Each program is commented and some examples of their use are available from the code. Current implementation includes EDAs for discrete and continuous problems. EDAs based on Bayesian and undirected graphical models have been included. The software is not warranteed in any way.
There are three main classes of EDAs implemented:

1. EDAs based on undirected graphical models for discrete problems
2. EDAs based on Bayesian networks for discrete problems
3. EDAs based on Gaussian models for continous problems

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## EDAs based on undirected graphical models

All the EDAs in this class do only parametric learning. The structure of the probability model is fixed in all the iterations. This structure can be given to the algorithm by the user. The model of choice is a junction graph where every set of interacting variables (definition sets of the function when it is additive function) is represented by a clique.
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RunUMDA.m`
This is the simplest EDA. It uses a univariate probabilistic model where all the variables are independent.
`RunMarkovFDA.m
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EDA that uses a junction graph where each variables depends on the previous k variables (k-Markovian model). k is a parameter of the algorithm. For k=0, RunMarkovFDA.m behaves as RunUMDA.m
`RunFDA.m`
Implementation of the Factorized Distribution Algorithm (FDA). The set of cliques where variables are defined is given. The cliques has to form a junction graph. This is a generalization of the FDA where cliques are constrained to form a junction graph (i.e. a junction graph without cycles).

## EDAs based on Bayesian networks

`RunBNEDA.m`
EDA that uses as a probabilistic model a Bayesian network. Different BN structure learning algorithms are used. The instalation of the BNT and the BNT structure learning software packages are needed.
Current implementations include the maximum weight spanning tree and K2 learning algorithms.

## EDAs based on Gaussian models

`RunGaussianEDA.m`
EDA that uses univariate and multivariate Gaussian models to approximate probability distributions for problems with continuous representation.