A novel approach for designing adaptive fuzzy classifiers based on the combination of an artificial immune network and a memetic algorithm

In this study, we propose a novel approach for designing fuzzy classifiers. The first part of our approach is a new preprocess algorithm called SPP (silhouette cluster validity index aided pre-process via k-means). The SPP algorithm has been performed on the data set to determine the numbers of the membership functions and their initial boundaries. Then, the Mopt-aiNetLS algorithm (modified version of opt-aiNet combined with local search strategy of memetic algorithm), the second part of the approach, examines search space to find the optimal values of fuzzy rules and membership functions for the system. The Mopt-aiNetLS is the combination of the memetic algorithm and a modified version of the opt-aiNet algorithm, in which some changes were made in the suppression and hypermutation mechanisms of the original opt-aiNet algorithm. These two new mechanisms are called the intelligent suppression mechanism and the adaptive hypermutation operator. Combining the modified version of opt-aiNet with the local search strategy of the memetic algorithm improves the accuracy of the classification rate. An effective search process has been realized using the Mopt-aiNetLS because the global search capability of opt-aiNet is complemented by the local search strategy of the memetic algorithm. To test the performance of this new approach, twenty different well-known classification dataset benchmark problems from the UCI dataset were used. The average 3 × 10 cross-fold validation results obtained from these datasets are presented and compared with the results of certain classification algorithms reported in the literature. The Wilcoxon Signed-Rank Test was also used for statistical comparisons. The obtained results demonstrate the effectiveness of the proposed approach.