Reference : Single and Multiobjective Evolutionary Algorithms for Clustering Biomedical Informati...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/35740
Single and Multiobjective Evolutionary Algorithms for Clustering Biomedical Information with Unknown Number of Clusters
English
Curi, María Eugenia [> >]
Carozzi, Lucía [> >]
Massobrio, Renzo [> >]
Nesmachnow, Sergio [> >]
Danoy, Grégoire mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)]
Ostaszewski, Marek mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) >]
Bouvry, Pascal mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)]
2018
Bioinspired Optimization Methods and Their Applications
Springer International Publishing
Yes
No
International
978-3-319-91641-5
Cham
8th International Conference on Bioinspired Optimization Methods and Their Applications (BIOMA)
16-05-2018
[en] This article presents single and multiobjective evolutionary approaches for solving the clustering problem with unknown number of clusters. Simple and ad-hoc operators are proposed, aiming to keep the evolutionary search as simple as possible in order to scale up for solving large instances. The experimental evaluation is performed considering a set of real problem instances, including a real-life problem of analyzing biomedical information in the Parkinson's disease map project. The main results demonstrate that the proposed evolutionary approaches are able to compute accurate trade-off solutions and efficiently handle the problem instance involving biomedical information.
http://hdl.handle.net/10993/35740
100--112

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