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.
KeywordsMutation Operator Fuzzy Cluster Cluster Problem Rand Index Partition Matrix
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