Genetic algorithm with Novel Crossover, selection and health check for clustering

Abul Hashem Beg, Md Zahidul Islam

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

3 Citations (Scopus)
1 Downloads (Pure)

Abstract

We propose a genetic algorithm for clustering records, where the algorithm contains new approaches for various genetic operations including crossover and selection. We also propose a health check operation that finds sick chromosomes of a population and probabilistically replaces them with healthy chromosomes found in the previous generations. The proposed approaches improve the chromosome quality within a population, which then contribute in achieving good clustering solution. We use fifteen datasets to compare our technique with five existing techniques in terms of two cluster evaluation criteria. The experimental results indicate a clear superiority of the proposed technique over the existing techniques.
Original languageEnglish
Title of host publicationProceedings of the 24th European Symposium on artificial neural networks, computational intelligence and machine learning
Place of PublicationBelgium
Publisheri6doc.com publication
ChapterES2016-37
Pages575-580
Number of pages6
ISBN (Electronic)9782875870278
Publication statusPublished - 2016
EventEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2016) - Novotel Hotel, Bruges, Belgium
Duration: 27 Apr 201629 Apr 2016
https://web.archive.org/web/20160314003553/https://www.elen.ucl.ac.be/esann/ (Archived page)

Conference

ConferenceEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2016)
Country/TerritoryBelgium
CityBruges
Period27/04/1629/04/16
Internet address

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