Optimizing fuzzy classifiers by evolutionary algorithms

In this paper a methodology for optimizing fuzzy classifiers based on B-splines by evolutionary algorithms is presented. The algorithm proposed maximizes the performance and minimizes the size of the classifier. On a well-known classification problem the algorithm using only part of the features has a recognition rate comparable to an LDA on the total feature space.