An Island Grouping Genetic Algorithm for Fuzzy Partitioning Problems

• Main Authors: S. Salcedo-Sanz, J. Del Ser, Z. W. Geem
• Format: Article
• Language: English
• Published: Hindawi Limited 2014-01-01
• Series: The Scientific World Journal
• Online Access: http://dx.doi.org/10.1155/2014/916371

This paper presents a novel fuzzy clustering technique based on grouping genetic algorithms (GGAs), which are a class of evolutionary algorithms especially modified to tackle grouping problems. Our approach hinges on 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 Bouldin index), specially tailored crossover and mutation operators, and the use of a scheme based on a local search and a parallelization process, inspired from an island-based model of evolution. The overall performance of our approach has been assessed over a number of synthetic and real fuzzy clustering problems with different objective functions and distance measures, from which it is concluded that the proposed approach shows excellent performance in all cases.