Novel crossover and mutation operation in Genetic Algorithm for clustering

Abul Hashem Beg, Md Zahidul Islam

Research output: Book chapter/Published conference paperConference paperpeer-review

10 Citations (Scopus)
5 Downloads (Pure)


In this paper we propose a Genetic Algorithm-based clustering technique called GMC that produces high-quality chromosomes in the initial population. The proposed technique also introduces two phases of crossover operation with extensive chromosomes generation aiming to produce high-quality offspring chromosomes and prevent degeneracy. The proposed technique also introduces three steps of mutation operation in order to improve chromosome quality. GMC uses a probabilistic selection approach in order to gradually improve the chromosomes quality of a population. We compare the proposed technique GMC with five existing techniques on 10 publicly available data sets in terms of two well-known evaluation criteria: Silhouette Coefficient and DB Index. Our experimental results demonstrate statistically significant superiority of GMC over the existing techniques, and the effectiveness of the proposed components.
Original languageEnglish
Title of host publication2016 IEEE Congress on Evolutionary Computation (CEC)
Place of PublicationUnited Kingdom
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781509006236, 9781509006229
ISBN (Print)9781509006243 (PoD)
Publication statusPublished - 2016
EventIEEE Congress on Evolutionary Computation - Vancouver, Canada, Canada
Duration: 24 Jul 201629 Jul 2016


ConferenceIEEE Congress on Evolutionary Computation


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