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Title
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Single and multiobjective evolutionary algorithms for clustering biomedical information with unknown number of clusters
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Author
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Abstract
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| 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. |
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Language
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English
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Source (journal)
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Lecture notes in computer science. - Berlin, 1973, currens
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Source (book)
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Bioinspired Optimization Methods and Their Applications : 8th International Conference, BIOMA 2018, May 16-18, 2018, Paris, France
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Publication
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Cham
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2018
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ISBN
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978-3-319-91640-8
978-3-319-91641-5
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DOI
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10.1007/978-3-319-91641-5_9
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Volume/pages
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10835
(2018)
, p. 100-112
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ISI
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000554401600009
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Full text (Publisher's DOI)
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