Beg, Abul HashemIslam, ZahidulEstivill-Castro, V. (Vladimir)2021-05-262020Beg AH, Islam Z, Estivill-Castro V. HeMI ++: a genetic algorithm based clustering technique for sensible clusters. In: 2020 IEEE Congress on Evolutionary Computation (CEC); 2020 Jul 19-24; Glasgow, UK. New Jersey: IEEE; 2020. p.495-504. DOI: 10.1109/CEC48606.2020.9185882http://hdl.handle.net/10230/47660Comunicació presentada al IEEE Congress on Evolutionary Computation (CEC 2020), celebrat del 19 al 24 de juliol de 2020 a Glasgow, Escòcia.We propose a new clustering technique called HeMI++. It uses cleansing and cloning operations that help to produce sensible clusters. HeMI++ learns necessary properties of a good clustering solution for a dataset from a high-quality initial population, without requiring any user input. It then disqualifies the chromosomes that do not satisfy the properties through its cleansing operation. In the cloning operation, HeMI++ replaces the chromosomes by high-quality chromosomes already found in the initial population. We compare HeMI++ with six (6) existing techniques on twenty (20) publicly available datasets using the Tree Index metric. Our experimental results indicate a clear superiority of HeMI++ over existing methods. We also apply HeMI++ on a brain dataset and demonstrate its ability to produce sensible clusters.application/pdfeng© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/CEC48606.2020.9185882info:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/CEC48606.2020.9185882Biological cellsSociologyStatisticsGenetic algorithmsIndexesCloningComplexity theoryinfo:eu-repo/semantics/openAccess