A multi-objective genetic algorithm for tuning and rule selection to obtain accurate and compact linguistic fuzzy rule-based systems


This work proposes the application of Multi-Objective Genetic Algorithms to obtain Fuzzy Rule-Based Systems with a better trade-off between interpretability and accuracy in linguistic fuzzy modelling problems. To do that, we present a new post-processing method that by considering selection of rules together with the tuning of membership functions gets solutions only in Pareto zone with the highest accuracy. This method is based on the well-known SPEA2 algorithm, applying approriate genetic operators and including some modifications to concentrate the search in the desired Pareto zone.