## 3 Proposed RBAVO-DE for GS
### The Complete RBAVO-DE Algorithm
This can then be calculated in big-O notation after eliminating all constants in the following way:

\[O_{memory\_usage}(RBAVO-DE)=O_{memory\_usage}(\text{Input values memory usage})+\] \[O_{memory\_usage}(\text{Additional memory usage})=\] \[O_{memory\_usage}(1)+\left(O_{memory\_usage}(N\times D)+O_{ memory\_usage}(1)+O_{memory\_usage}(D)\right)=\] \[O_{memory\_usage}(N\times D).\]It can be difficult to compile a thorough comparison of the memory usage and execution time complexity of various meta-heuristic algorithms since these complexities might differ based on the particular implementation, size of the problem, and other operators. Furthermore, not all of the algorithms listed have comprehensive evaluations of the execution time and memory usage complexity available, and their properties may vary depending on the problem at hand. ## 5 Benefits and Drawbacks of the RBAVO-DE Algorithm

This part presents a balanced discussion of the benefits and drawbacks of the RBAVO-DE algorithm. The benefits of the RBAVO-DE algorithm can be listed as follows:

* **High Accuracy in Classification**: The RBAVO-DE algorithm has demonstrated high classification accuracy, achieving up to 100% in some cases. This is a noteworthy achievement, particularly regarding cancer datasets, as accurate gene selection can directly impact diagnostic and therapeutic outcomes. * **Effective Feature Size Reduction**: The algorithm has shown remarkable capability in reducing the feature size by up to 98% while maintaining or improving classification accuracy. This dimensionality reduction is crucial for processing high-dimensional datasets efficiently. * **Robustness Across Diverse Datasets**: The effectiveness of the RBAVO-DE algorithm across twenty-two cancer datasets indicates its robustness and adaptability to various genetic data characteristics. This versatility is beneficial for broader applications in genomics research. * **Superior Performance Over Competitors**: When compared with binary variants of widely recognized meta-heuristic algorithms, RBAVO-DE stands out for its outstanding performance in accuracy and feature reduction, highlighting its innovative approach to gene selection. On the other hand, the drawbacks of the proposed methodology can be listed as follows:
* **Computational Complexity**: The high dimensionality of the datasets and the iterative nature of meta-heuristic algorithms suggest that RBAVO-DE may have a significant computational cost. This aspect could limit its applicability in environments with constrained computational resources. * **Generalizability Concerns**: Despite its success across various cancer datasets, the algorithm's generalizability to other types of biological data or diseases remains to be thoroughly investigated. It is crucial to test RBAVO-DE in broader contexts to confirm its applicability beyond the datasets examined. * **Parameter Sensitivity**: Like many meta-heuristic algorithms, RBAVO-DE might be sensitive to its parameter settings, affecting performance and efficiency. Detailed studies on the impact of different parameter configurations and strategies for their optimization could enhance the algorithm's utility. In summary, the RBAVO-DE algorithm represents a significant advancement in gene selection for cancer classification, with notable strengths in accuracy and efficiency. However, addressing its potential drawbacks through further research could broaden its applicability and improve its performance, making it an even more valuable tool in genomics and personalized medicine. ## 6 Conclusions and Future Directions

The RBAVO-DE approach proposed in this paper was the first to be implemented for handling GS issues in RNA-Seq gene expression data and determining potential biomarkers for different tumor classes to enhance the best solution discovered. The outcomes were promising, revealing that the proposed RBAVO-DE algorithm's effectiveness and capabilities were significantly improved.