An Island Grouping Genetic Algorithm for Fuzzy Partitioning Problems
- Authors
- Salcedo-Sanz, S.; Del Ser, J.; Geem, Z. W.
- Issue Date
- 2014
- Publisher
- HINDAWI PUBLISHING CORP
- Citation
- SCIENTIFIC WORLD JOURNAL
- Journal Title
- SCIENTIFIC WORLD JOURNAL
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/14005
- DOI
- 10.1155/2014/916371
- ISSN
- 1537-744X
- 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.
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