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Gene selection, which is a well-known NP-hard problem, is a challenging task that has been the subject of a large amount of research, especially in relation to classification tasks. This problem addresses the identification of the smallest possible set of genes that could achieve good predictive performance. Many gene selection algorithms have been proposed; however, because the search space increases exponentially with the number of genes, finding the best possible approach for a solution that would limit the search space is crucial. Metaheuristic approaches have the ability to discover a promising area without exploring the whole solution space. Hence, we propose a new method that hybridises the Harmony Search Algorithm (HSA) and the Markov Blanket (MB), called HSA-MB, for gene selection in classification problems. In this proposed approach, the HSA (as a wrapper approach) improvises a new …