Hybrid self-configuring evolutionary algorithm for automated design of fuzzy classifier

Abstract

For a fuzzy classifier automated design the hybrid self-configuring evolutionary algorithm is proposed. The self-configuring genetic programming algorithm is suggested for the choice of effective fuzzy rule bases. For the tuning of linguistic variables the self-configuring genetic algorithm is used. An additional feature of the proposed approach allows the use of genetic programming for the selection of the most informative combination of problem inputs. The usefulness of the proposed algorithm is demonstrated on benchmark tests and real world problems.