## 2 Related Works
They followed a two-phase approach--data pre-processing and utilizing a convolutional neural network--to classify the type of tumor. In the second phase, they created heat maps for each category to identify genes related to pixels with the highest intensities in the heat maps. They then evaluated the selected genes' pathways. During pre-processing, they removed the levels of gene expression that had not been modified during the GS phase, using a variance threshold of 1.19, which decreased the number of genes from 19,531 to 10,381. The final classification accuracy obtained was 95.59, which is good but could still be improved using a more effective FS methodology to further decrease data dimensionality. [PERSON] et al. [37] built on the aforementioned paper [36]. The focus of their study was on five types of cancer data: Uterine Corpus Endometrial Carcinoma (UCEC), Lung Squamous Cell Carcinoma (LUSC), Lung Adenocarcinoma (LUAD), Kidney Renal Clear Cell Carcinoma (KIRC), and Breast Invasive Carcinoma (BRCA). The benchmark dataset used for the study comprised 2086 records and 972 attributes. Each record provided detailed sample information, while each attribute included the RNA-Seq values for a specific gene, represented as RPKM (Reads Per Kilobase per Million) [38]. The researchers employed a mixed approach using binary PSO with the decision trees (BPSO-DT) method to pre-process the data. Out of 971 attributes, 615 were selected as the best attributes of RNA-SEquation The proposed method achieved an overall testing classification accuracy of 96.90%, as demonstrated by the suggested outcomes and evaluation measures used. [PERSON] et al. [39] assessed their methodology using three RNA-Seq datasets: Stomach Adenocarcinoma (STAD), BRCA, and LUAD. Their approach relied on DL techniques, wherein they employed five classifiers and subsequently utilized the DL technique to ensemble each output of the five classifiers. This led to an improvement in the classification accuracy of all the predictions, with the BRCA dataset achieving 98.4% accuracy, STAD achieving 98.78% accuracy, and LUAD achieving 99.20% accuracy. [PERSON] et al. [40] used micro-array data and a hybrid approach to address the problem. Unlike the previously mentioned papers, they studied each cancer class separately. To assess performance, they employed four gene benchmarks related to small round blue cell tumors, colon cancer, lung cancer, and leukemia. Their proposed method used Relief as the pre-processing technique to eliminate genes with a lower correlation with the specific cancer class, followed by PSO as the search technique. Finally, they employed the SVM model to evaluate the classification accuracy of the selected subset of genes and obtain the conclusive optimum gene subset for each cancer type. Based on the existing research, it seems that most studies using RNA-Seq GED are still in their early stages, with researchers attempting to implement and test various ideasin this promising area. While the literature contains a plethora of experiments employing multiple techniques, such as FS and DL recent methods, no single technique is perfect due to the high dimensionality of RNA-Seq GED. FS of RNA-Seq GED plays a crucial role in determining the relationship between a gene and its category. It is a critical pre-processing task for validating gene biomarkers of cancer and overcoming the dimensionality curse. Consequently, this study aims to introduce a new wrapper approach, the RBAVO-DE algorithm, and apply it to RNA-Seq data for the first time. It also compares the proposed algorithm's effectiveness with that of other FS techniques.