Genetic Learning of Serial Hierarchical Fuzzy Systems for Large-Scale Problems

When we face a problem with a high number of variables by a standard fuzzy system, the number of rules increases exponentially and then the obtained fuzzy system is scarcely interpretable. This problem can be handled by arranging the inputs in hierarchical ways. The paper presents a multi-objective Genetic Algorithm that learns Serial Hierarchical Fuzzy Systems with the aim of coping the curse of dimensionality. By means of an experimental study, we have observed that our algorithm obtains good results of interpretability and precision with problems in which the number of variables is relatively high. Keywords—Curse of dimensionality, hierarchical fuzzy systems, multi-objective genetic algorithms, variable selection.