Learning Type-2 Fuzzy Rule-Based Systems through Memetic Algorithms

Fuzzy Rule-Based Systems (FRBSs) are powerful tools for handling uncertainty in complex real world problems. Unfortunately, designing an optimal set of fuzzy membership functions and rules is not an easy task. The complexity of this task increases furthermore when the design involves type-2 fuzzy memberships because of the higher number of parameters to identify. For this reason, researches have been carried out to automatically extract the most suitable parameters and the hidden rules for type-2 FRBSs by using historical data. However, very few approaches exist based on a hybrid optimization between global and local search. This paper is aimed at overcoming this gap by proposing to automatically generate type-2 FRBSs through the so-called memetic algorithms. As shown in the experiments, the exploitation of memetic algorithms leads to generate type-2 FRBSs more accurate than those obtained by well-known approaches in literature.