Abstract
The hybridization of metaheuristics got a lot of interest lately. The crucial step lies in the choice of the hybrid methods. The major purpose is to make a tradeoff between exploitation and exploration concepts to create a more robust method. Hybrid metaheuristics are used as a solution to many optimization problems such as feature selection. In this paper we propose an hybridization between metaheuristics (PeSOA, FA, DE, AIS, BAT) for a best gene selection in microarray datasets. The main objective is to prove the efficiency of the proposed hybridization compared to the hybrid methods. The experimentations showed that PeSOA-C and HFA were competitive to their hybrid methods, on the other side, AIS-BAT was less promising compared to AIS. As results, we obtained a perfect 100% in case of Leukemia, Ovarian Cancer, Lymphoma and MLL-Leukemia datasets by the HFA hybridization with only 2%–4% of selected genes.
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