An Efficient Parallel Reptile Search Algorithm and Snake Optimizer Approach for Feature Selection

Feature Selection (FS) is a major preprocessing stage which aims to improve Machine Learning (ML) models’ performance by choosing salient features, while reducing the computational cost. Several approaches are presented select most Optimal Features Subset (OFS) in given dataset. In this paper, we introduce an FS-based approach named Reptile Search Algorithm–Snake Optimizer (RSA-SO) that employs both RSA and SO methods parallel mechanism determine OFS. This decreases chance of two stuck local optima it boosts capability them balance exploration explication. Numerous experiments performed on ten datasets taken from UCI repository real-world engineering problems evaluate RSA-SO. The obtained results RSA-SO also compared with seven popular Meta-Heuristic (MH) for FS prove its superiority. show developed has comparative tested MH can provide practical accurate solutions optimization problems.