###### Abstract  
This study presents a novel method, termed RBAVO-DE (Relief Binary African Vultures Optimization based on Differential Evolution), aimed at addressing the Gene Selection (GS) challenge in high-dimensional RNA-Seq data, specifically the maseqv2 Illumina HiSeq rnaseqv2 un edu Level 3 RSEM genes normalized dataset, which contains over 20,000 genes. RNA Sequencing (RNA-Seq) is a transformative approach that enables the comprehensive quantification and characterization of gene expressions, surpassing the capabilities of micro-array technologies by offering a more detailed view of RNA-Seq gene expression data. Quantitative gene expression analysis can be pivotal in identifying genes that differentiate normal from malignant tissues. However, managing these high-dimensional dense matrix data presents significant challenges. The RBAVO-DE algorithm is designed to meticulously select the most informative genes from a dataset comprising more than 20,000 genes and assess their relevance across twenty-two cancer datasets. To determine the effectiveness of the selected genes, this study employs the Support Vector Machine (SVM) and k-Nearest Neighbor (\(k\)-NN) classifiers. Compared to binary versions of widely recognized meta-heuristic algorithms, RBAVO-DE demonstrates superior performance. According to [PERSON]'s rank-sum test, with a 5% significance level, RBAVO-DE achieves up to 100% classification accuracy and reduces the feature size by up to 98% in most of the twenty-two cancer datasets examined. This advancement underscores the potential of RBAVO-DE to enhance the precision of gene selection for cancer research, thereby facilitating more accurate and efficient identification of key genetic markers. R 2024 17342024 16 July 20241 August 202416 July  
illnesses and find ways to manage them. Gene expression profiling technologies have significantly developed over the years [2]. Two popular techniques are micro-array, which uses a hybridization-based approach, and RNA-Seq, which is based on next-generation sequencing. These technologies have demonstrated their value in advancing our understanding of gene expression [3]. Notable studies, such as those by [PERSON] et al. [4] and [PERSON] et al. [5], have highlighted the potential of these techniques, while [PERSON] et al. [6] have provided valuable insights into the RNA-Seq approach. The two methods under consideration serve the purpose of quantifying gene expression for classification and statistical investigation. This paper has chosen the quantification data obtained through the next-generation sequencing (NGS)-based RNA-Seq approach because it offers more accuracy in detecting RNA quantification levels compared to microarray data [7]. RNA-Seq has overcome several limitations of micro-array analysis, such as dependence on prior sequencing knowledge, which restricted the detection range [8; 9]. Unlike micro-arrays, RNA-Seq does not require any previous knowledge and significantly expands the dynamic detection range. This enhancement improves the accuracy of results and enables the identification of a comprehensive set of genes, providing a precise understanding of disease biomarkers. As a result, RNA-Seq has emerged as a potent tool for researchers, offering valuable insights into the underlying molecular mechanisms of various diseases [10]. The \"dimensionality curse\" [11] has become a common challenge in contemporary times owing to the abundance of available data. This has caused a surge in the development of feature selection (FS) techniques and algorithms. FS algorithms can be classified into four distinct methods: filter, wrapper, embedded, and hybrid [12; 13]. These approaches aim to identify the most informative features that can distinguish between different classes. In our case, we are concerned with identifying the genes linked to tumors. The filter approach is a widely used technique in FS that involves evaluating the relevance of individual genes based on statistical scores.