Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study, our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization (GWO) with Harris Hawks Optimization (HHO) for feature selection. The motivation for utilizing GWO and HHO stems from their bio-inspired nature and their demonstrated success in optimization problems. We aim to leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification. We selected leave-one-out cross-validation (LOOCV) to evaluate the performance of both two widely used classifiers, k-nearest neighbors (KNN) and support vector machine (SVM), on high-dimensional cancer microarray data. The proposed method is extensively tested on six publicly available cancer microarray datasets, and a comprehensive comparison with recently published methods is conducted. Our hybrid algorithm demonstrates its effectiveness in improving classification performance, Surpassing alternative approaches in terms of precision. The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification, thereby advancing the development of more efficient treatment strategies. The proposed hybrid method offers a promising solution to the gene selection problem in microarray-based cancer classification. It improves the accuracy and efficiency of cancer diagnosis and treatment, and its superior performance compared to other methods highlights its potential applicability in real-world cancer classification tasks. By harnessing the complementary search mechanisms of GWO and HHO, we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.