Fuzzy Rule-Based Classifier Design with Co-operation of Biology Related Algorithms

Abstract

A meta-heuristic called Co-Operation of Biology Related Algorithms (COBRA) is applied to the design of a fuzzy rule-based classifier. The basic idea consists in the representation of a fuzzy classifier rule base as a binary string and the use of the binary modification of COBRA with a biogeography migration operator for the selection of the fuzzy classifier rule base. The parameters of the membership functions of the fuzzy classifier, represented as a string of real-valued variables, are adjusted with the original version of COBRA. Two medical diagnostic problems are solved with this approach. Experiments showed that the modification of COBRA demonstrates high performance and reliability in spite of the complexity of the optimization problems solved. Fuzzy classifiers developed in this way outperform many alternative methods at the given classification problems. The workability and usefulness of the proposed algorithms are confirmed.