Identifying disease-related genes is an ongoing study issue in biomedical analysis. Many research has recently presented various strategies for predicting disease-related genes. However, only a handful of them were capable of identifying or selecting relevant genes with a low computational burden. In order to tackle this issue, we introduce a new filter–wrapper-based gene selection (GS) method based on metaheuristic algorithms (MHAs) in conjunction with the k-nearest neighbors (\({k{\hbox {-NN}}}\)) classifier. Specifically, we hybridize two MHAs, bat algorithm (BA) and JAYA algorithm (JA), embedded with perturbation as a new perturbation-based exploration strategy (PES), to obtain JAYA–bat algorithm (JBA). The fact that JBA outperforms 10 state-of-the-art GS methods on 12 high-dimensional microarray datasets (ranging from 2000 to 22,283 features or genes) is impressive. It is also noteworthy that relevant genes are first selected via a filter-based method called mutual information (MI), and then further optimized by JBA to select the near-optimal genes in a timely fashion. Comparing the performance analysis of 11 well-known original MHAs, including BA and JA, the proposed JBA achieves significantly better results with improvement rates of 12.36%, 12.45%, 97.88%, 9.84%, 12.45%, and 12.17% in terms of fitness, accuracy, gene selection ratio, precision, recall, and F1-score, respectively. The results of Wilcoxon’s signed-rank test at a significance level of \(\alpha =0.05\) further validate the superiority of JBA over its peers on most of the datasets. The use of PES and the combination of BA and JA’s strengths appear to enhance JBA’s exploration and exploitation capabilities. This gives it a significant advantage in gene selection ratio, while also ensuring the highest classification accuracy and the lowest computational time among all competing algorithms. Thus, this research could potentially make a significant contribution to the field of biomedical analysis.
