Improved Binary Elk Herd Optimizer with Fitness Balance Distance for Feature Selection Using Gene Expression Data

Mohamed Wajdi Ouertani, Raja Oueslati, Ghaith Manita

Abstract: This research paper introduces an enhanced version of the Binary Elk Herd Optimizer (BEHO), integrated with a Fitness Distance Balance (FDB) mechanism called FDB-BEHO, tailored for high-dimensional optimization tasks. This study evaluates the performance of FDB-BEHO across multiple gene expression datasets, focusing on feature selection in bioinformatics—a domain characterized by complex, high-dimensional data. The FDB mechanism is designed to prevent premature convergence by maintaining an optimal balance between exploration and exploitation, utilizing a diversity measure that adjusts dynamically based on the fitness-distance correlation among solutions. Comparative analyses demonstrate that FDB-BEHO surpasses traditional meta-heuristic algorithms in fitness values and classification accuracy and reduces the number of selected features, thereby enhancing model simplicity and interpretability. These results validate the effectiveness of FDB-BEHO in navigating complex solution spaces