Learning weighted linguistic fuzzy rules with estimation of distribution algorithms
L. delaOssa, José Antonio Gámez, Jose Miguel Puerta · 2006
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.