Fuzzy functions via genetic programming

Fuzzy rule base systems which were originally proposed by Zadeh are one of the most influential approaches with many practical applications in the literature. On the other hand fuzzy rule bases remain incapable for many practical problems due to the dependence of expert knowledge and complexity of operators utilized. Fuzzy functions concept which was first introduced by Turks, en in 2004 and further enhanced by him and his colleagues provide an alternative to fuzzy rule bases. This approach is not depended on expert knowledge where data is available. In their studies Turks, en and his colleagues generally used Least Square Estimation (LSE) and Support Vector machines (SVM) in generating fuzzy functions. In the present study, we employed genetic programming approach in order to generate fuzzy functions with better prediction ability. We tested the proposed approach on several benchmark problems with very promising results. In the present paper results of four example applications are reported and results were discussed. It is shown that genetic programming approach considerable improved the prediction ability of fuzzy functions approach.