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Parallel Hybrid Metaheuristic for Multi-objective Biclustering in Microarray Data

Khedidja Seridi 1 Laetitia Jourdan 1 El-Ghazali Talbi 1
1 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : 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.
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Submitted on : Friday, September 14, 2012 - 4:20:57 PM
Last modification on : Thursday, May 28, 2020 - 9:22:09 AM



Khedidja Seridi, Laetitia Jourdan, El-Ghazali Talbi. Parallel Hybrid Metaheuristic for Multi-objective Biclustering in Microarray Data. IPDPSW 2012 - 26th IEEE International Parallel and Distributed Processing Symposium Workshops, May 2012, Shanghai, China. pp.625-633, ⟨10.1109/IPDPSW.2012.78⟩. ⟨hal-00732450⟩



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