Single and Multiobjective Evolutionary Algorithms for Clustering Biomedical Information with Unknown Number of Clusters

Curi, María Eugenia; Carozzi, Lucía; Massobrio, Renzo; Nesmachnow, Sergio; Danoy, Grégoire; Ostaszewski, Marek; Bouvry, Pascal

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Single and Multiobjective Evolutionary Algorithms for Clustering Biomedical Information with Unknown Number of Clusters

Curi, María Eugenia; Carozzi, Lucía; Massobrio, Renzo et al.

2018 • In Bioinspired Optimization Methods and Their Applications

Peer reviewed

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https://hdl.handle.net/10993/35740

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Abstract :

[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.

Disciplines :

Computer science

Author, co-author :

Curi, María Eugenia

Carozzi, Lucía

Massobrio, Renzo

Nesmachnow, Sergio

Danoy, Grégoire ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)

Ostaszewski, Marek ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)

Bouvry, Pascal ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)

External co-authors :

yes

Language :

English

Title :

Single and Multiobjective Evolutionary Algorithms for Clustering Biomedical Information with Unknown Number of Clusters

Publication date :

2018

Event name :

8th International Conference on Bioinspired Optimization Methods and Their Applications (BIOMA)

Event date :

16-05-2018

Audience :

International

Main work title :

Bioinspired Optimization Methods and Their Applications

Publisher :

Springer International Publishing, Cham, Unknown/unspecified

ISBN/EAN :

978-3-319-91641-5

Peer reviewed :

Peer reviewed

Commentary :

100--112

Available on ORBilu :

since 25 May 2018

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