Parallel Hybrid Metaheuristic for ...
Document type :
Communication dans un congrès avec actes
DOI :
Title :
Parallel Hybrid Metaheuristic for Multi-objective Biclustering in Microarray Data
Author(s) :
Seridi, Khedidja [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Jourdan, Laetitia [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Jourdan, Laetitia [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Conference title :
IPDPSW 2012 - 26th IEEE International Parallel and Distributed Processing Symposium Workshops
City :
Shanghai
Country :
Chine
Start date of the conference :
2012-05-21
Book title :
Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2012 IEEE 26th International
Publisher :
IEEE
Publication date :
2012
HAL domain(s) :
Sciences du Vivant [q-bio]/Bio-Informatique, Biologie Systémique [q-bio.QM]
English abstract : [en]
To deeper examine the gene expression data, a new data mining task is more more used: the biclustering. Biclustering consists in extracting genes that behave similarly under some experimental conditions. As the Biclustering ...
Show more >To deeper examine the gene expression data, a new data mining task is more more used: the biclustering. Biclustering consists in extracting genes that behave similarly under some experimental conditions. As the Biclustering problem is NP-Complete in most of its variants, many heuristics and meta-heuristics have been deisgned to solve it. Proposed algorithms in literature allow the extraction of interesting biclusters but are often time consuming. In this work, we propose a new parallel hybrid multi-objective metaheuristic based on the well known multi objective metaheuristic NSGA-II (Non-dominated Sorting Genetic Algorithm II), CC (Cheng and Church) heuristic and a multi-objective local search, PLS-1 (Pareto Local Search I). Experimental results on real data sets show that our approach can find significant biclusters of high quality. The speed-up of our algorithm is important with regard to the sequential version.Show less >
Show more >To deeper examine the gene expression data, a new data mining task is more more used: the biclustering. Biclustering consists in extracting genes that behave similarly under some experimental conditions. As the Biclustering problem is NP-Complete in most of its variants, many heuristics and meta-heuristics have been deisgned to solve it. Proposed algorithms in literature allow the extraction of interesting biclusters but are often time consuming. In this work, we propose a new parallel hybrid multi-objective metaheuristic based on the well known multi objective metaheuristic NSGA-II (Non-dominated Sorting Genetic Algorithm II), CC (Cheng and Church) heuristic and a multi-objective local search, PLS-1 (Pareto Local Search I). Experimental results on real data sets show that our approach can find significant biclusters of high quality. The speed-up of our algorithm is important with regard to the sequential version.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :