Fuzzy Clustering with Grouping Genetic Algorithms

This paper presents a novel approach to fuzzy clustering based on Grouping Genetic Algorithms GGAs. Our approach consists of a GGA devised for fuzzy clustering by means of a novel encoding of individuals containing elements and clusters sections, a new fitness function a superior modification of the Davies-Boudin index and specially tailored crossover and mutation operators. The overall performance of our approach has been tested in a variety of fuzzy clustering problems, showing very good performance in all cases.