title: An island grouping genetic algorithm for fuzzy partitioning problems.
abstract: 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.
authors: Department of Signal Processing and Communications, Universidad de Alcalá, 28871 Madrid, Spain.
authors: S
authors: S
authors: Salcedo-Sanz
authors: OPTIMA Area, Tecnalia Research & Innovation, 48170 Bizkaia, Spain.
authors: J
authors: J
authors: Del Ser
authors: Department of Energy IT, Gachon University, Seongnam 461-701, Republic of Korea.
authors: Z W
authors: ZW
authors: Geem
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D032701:Biomimetics / Q000379:methods*; D016000:Cluster Analysis; D017143:Fuzzy Logic; D062312:Islands; D008957:Models, Genetic; D010363:Pattern Recognition, Automated / Q000379:methods*