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Conference Papers Year : 2012

Parallel Hybrid Metaheuristic for Multi-objective Biclustering in Microarray Data

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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Dates and versions

hal-00732450 , version 1 (14-09-2012)

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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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