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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 : Biclustering is a well-known data mining problem in the field of gene expression data. It 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 metaheuristics are defined to solve for it. Classical algorithms allow the extraction of some biclusters in reasonable time, however most of them remain time consuming. In this work, we propose a new hybrid multi-objective meta-heuristic H-MOBI based on NSGA-II (Non-dominated Sorting Genetic Algorithm II), CC (Cheng and Church) heuristic and a multi-objective local search PLS-1 (Pareto Local Search 1). Experimental results on real data sets show that our approach can find significant biclusters of high quality.
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Conference papers
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https://hal.inria.fr/hal-00732459
Contributor : Laetitia Jourdan <>
Submitted on : Friday, September 14, 2012 - 4:31:52 PM
Last modification on : Thursday, May 28, 2020 - 9:22:09 AM

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Khedidja Seridi, Laetitia Jourdan, El-Ghazali Talbi. Hybrid metaheuristic for multi-objective biclustering in microarray data. CIBCB 2012 - IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, May 2012, San Diego, United States. pp.222-228, ⟨10.1109/CIBCB.2012.6217234⟩. ⟨hal-00732459⟩

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