A genetic learning of fuzzy relational rules

Two basic requirements of fuzzy modeling are the accuracy and simplicity of the knowledge obtained. In this study, we propose a genetic learning algorithm of fuzzy relational rules, that is, fuzzy rules that include fuzzy relations. Fuzzy relational rules allow us to obtain fuzzy models with a good interpretability-accuracy trade-off. Since, the inclusion of relations increases the accuracy keeping the interpretability but increasing the number of features to be considered in the learning process. We also present a model to reduce the additional complexity that occurs when using this new type of rules. Finally, we also present an experimental study that demonstrated the advantage of the use of relational fuzzy rules.