Learning weighted linguistic fuzzy rules with estimation of distribution algorithms

The main feature of Estimation of Distribution Algorithms is the way they evolve by gathering the information about the best elements of each population into a probability distribution. This work studies the application of these algorithms to the learning of weighted linguistic fuzzy-rule-based systems with the wCOR method. For this purpose, we propose the use of two different probabilistic models: One which does not assume any dependence between the rule consequents and their weights, and other whose structure is fixed from these dependences.