Parallel Hybrid Metaheuristic for Multi-objective Biclustering in Microarray Data

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.