A multi-objective evolutionary algorithm for rule selection and tuning on fuzzy rule-based systems


Recently, multi-objective evolutionary algorithms have been also applied to improve the difficult tradeoff between interpretability and accuracy of fuzzy rule-based systems. It is know that both requirements are usually contradictory, however, a multi-objective genetic algorithm can obtain a set of solutions with different degrees of trade-off. This contribution presents a multi-objective evolutionary algorithm to obtain linguistic models with improved accuracy and the least number of possible rules. In order to minimize the number of rules and the system error, this model performs a rule selection and a tuning of the membership functions of an initial set of candidate linguistic fuzzy rules.