  In regression problems, the use of TSK fuzzy systems is widely extended due
to the precision of the obtained models. Moreover, the use of simple linear TSK
models is a good choice in many real problems due to the easy understanding of
the relationship between the output and input variables. In this paper we
present FRULER, a new genetic fuzzy system for automatically learning accurate
and simple linguistic TSK fuzzy rule bases for regression problems. In order to
reduce the complexity of the learned models while keeping a high accuracy, the
algorithm consists of three stages: instance selection, multi-granularity fuzzy
discretization of the input variables, and the evolutionary learning of the
rule base that uses the Elastic Net regularization to obtain the consequents of
the rules. Each stage was validated using 28 real-world datasets and FRULER was
compared with three state of the art enetic fuzzy systems. Experimental results
show that FRULER achieves the most accurate and simple models compared even
with approximative approaches.
