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Cancer classification is one of the main applications of gene expression data (microarray data) and is essential for a comprehensive diagnosis of cancer treatment. Therefore, bio-inspired algorithms have developed several effective applications in the analysis of gene selection, which are one of the most effective applied in this domain. Harris Hawks optimization is a novel and recent algorithm that has an excellent balance between exploration and exploitation. This paper presents the first study on multi-objective binary Harris Hawks optimization (MOBHHO) for gene selection. We define gene selection as a problem, including two main conflicting objectives: minimizing the number of genes and maximizing the classification accuracy. MOBHHO uses two fitness functions to solve competing objectives. The first function based on SVM with LOOCV classifier and the second function also depends on KNN with K-fold …