One of the problems associated to linguistic fuzzy modeling is its lack of accuracy when modeling some complex systems. To overcome this problem, many different possibilities of improving the accuracy of linguistic fuzzy modeling have been considered in the specialized literature, maintaining the desired trade-off between accuracy and interpretability. Recently, a new linguistic rule representation model was presented to perform a genetic lateral tuning of membership functions. It is based on the linguistic 2-tuples representation model that allows the lateral displacement of a label considering a unique parameter. It involves a reduction of the search space that eases the derivation of optimal models. Based on the linguistic 2-tuples representation model, in this work, we present a new method to obtain linguistic fuzzy systems by means of an evolutionary learning of the data base a priori (granularity and lateral displacements of the membership functions) and on the use of a basic rule generation method to obtain the whole knowledge base. In this way, the search space reduction provided by the linguistic 2-tuples representation helps to the evolutionary search technique to obtain more precise and compact knowledge bases. Moreover, we analyze this approach considering 21 real-world problems
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