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DNA microarray technology relies on a chip containing thousands of DNA probes to simultaneously quantify the activity levels of many genes. The resulting gene expression data, typically arranged in matrix form, serves as the foundation for pattern-recognition techniques that separate samples taken from healthy subjects from those obtained from cancer patients. Nonetheless, extracting genuinely informative biomarkers from such datasets remains extremely difficult due to the curse of dimensionality and the substantial presence of uninformative or noisy genes. This study presents an effective gene selection framework that combines the powerful Minimum Redundancy Maximum Relevance (MRMR) filter with a newly designed hybrid metaheuristic. The proposed optimizer merges an enhanced version of the Marine Predator Algorithm (MPA) with a crossover mechanism, yielding the so-called MPAC algorithm. MPAC is tailored to identify a small yet highly discriminative set of genes that dramatically improves cancer classification accuracy. Classification is performed using the k-nearest neighbors (k-NN) classifier. The main strength of MPAC resides in its marked improvement of the exploration and exploitation capabilities of the original MPA population. It systematically identifies optimal gene subsets suitable as cancer biomarkers while carefully balancing global search (exploration) and local refinement (exploitation). The proposed method was comprehensively benchmarked on nine widely recognized microarray benchmark datasets. Its classification performance was rigorously compared with several standard and recently published optimization-based approaches. Results indicate that MPAC achieves superior accuracy across most datasets and delivers very competitive results on the remaining datasets.