## 3 Proposed RBAVO-DE for GS
### The Complete RBAVO-DE Algorithm
SVM and \(k\)-NN, two widely used classification models, were employed to evaluate the efficacy of each set of selected genes. The proposed RBAVO-DE algorithm's capability was compared to binary variants of eleven widely used meta-heuristic techniques to assess it on different tumor classes with various instances. The assessment was executed employing a combination of evaluation measures, such as average fitness, classification accuracy, the number of selected genes, precision, recall, and F1-score. The proposed RBAVO-DE algorithm using the SVM and \(k\)-NN classifiers achieved more promising results than other optimizers in handling GS issues. Despite the promising outcomes, this research opens several avenues for future exploration to further advance the field:

* Algorithmic Enhancements: Improving the algorithm's efficiency and reducing computational complexity. * Cross-disease Applicability: Testing the proposed algorithm on genetic data from various diseases beyond cancer. * Comparative Analyses with Deep Learning Models: Evaluating RBAVO-DE against advanced deep learning models for genetic data analysis. * Real-world Clinical Validation: Collaborating with clinical experts to validate the practical utility of selected genes in cancer treatment. * Scalability and Parallelization: Enhancing the algorithm's scalability through parallel computing to handle larger genetic datasets efficiently. * Interdisciplinary Applications: Exploring the algorithm's potential in other fields dealing with high-dimensional data, such as finance and environmental modeling. In conclusion, while the RBAVO-DE algorithm represents a significant step forward in the field of gene selection for cancer classification, the paths outlined for future research highlight the potential for further advancements and broader applications of this work. Continued interdisciplinary collaboration and innovation will be crucial for unlocking the full capacity of gene selection methodologies to enhance healthcare outcomes and advance our understanding of complex diseases. **Author Contributions:** M.G.G. : Resources, Validation, and Writing--review and editing. A.A.A. : Resources, Formal analysis, Data Curation, Validation, Writing--original draft, and Writing--review and editing. A.E.E. : Investigation, Visualization, Data Curation, Validation, Writing--original draft, and Writing--review and editing. A.A.A.E.-M.: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data Curation, Validation, Writing--original draft, and Writing--review and editing. All authors have read and agreed to the published version of the manuscript. **Funding:** Prince Sattam bin Abdulaziz University funded this research work under project number PSAU/2023/01/25612. **Data Availability Statement:** For enhancing the applicability and reproducibility of this research, the developed software and relevant Python code of this research are publicly available and obtainable in [61]. **Acknowledgments:** The authors express their gratitude to Prince Sattam bin Abdulaziz University for providing financial support for this research project. **Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ## Abbreviations

The following abbreviations are used in this research: \begin{tabular}{l l} AVO & African Cultures Optimization \\ HNSC & Head and Neck Squamous Cell Carcinoma \\ DE & Differential Evolution \\ KICH & Kidney Chromophone \\ RBAVO-DE & Relief Binary African Cultures Optimization based on Differential Evolution \\ KIRP & Kidney Renal Papillary \\ GS & Gene Selection \\ LIHC & Liver Hepatocellular Carcinoma \\ RNA-Seq & RNA Sequencing \\ \end{tabular}