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The Horse Herd Optimization Algorithm (HOA) is a new meta-heuristic algorithmbased on the behaviors of horses at different ages. The HOA was introducedrecently to solve complex and high-dimensional problems. This paper proposes abinary version of the Horse Herd Optimization Algorithm (BHOA) in order tosolve discrete problems and select prominent feature subsets. Moreover, thisstudy provides a novel hybrid feature selection framework based on the BHOA anda minimum Redundancy Maximum Relevance (MRMR) filter method. This hybridfeature selection, which is more computationally efficient, produces abeneficial subset of relevant and informative features. Since feature selectionis a binary problem, we have applied a new Transfer Function (TF), calledX-shape TF, which transforms continuous problems into binary search spaces.Furthermore, the Support Vector Machine (SVM) is utilized to examine theefficiency of the proposed method on ten microarray datasets, namely Lymphoma,Prostate, Brain-1, DLBCL, SRBCT, Leukemia, Ovarian, Colon, Lung, and MLL. Incomparison to other state-of-the-art, such as the Gray Wolf (GW), ParticleSwarm Optimization (PSO), and Genetic Algorithm (GA), the proposed hybridmethod (MRMR-BHOA) demonstrates superior performance in terms of accuracy andminimum selected features. Also, experimental results prove that the X-ShapedBHOA approach outperforms others methods.

An Efficient High-Dimensional Gene Selection Approach based on Binary Horse Herd Optimization Algorithm for Biological Data Classification