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 metaheuristics 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.