Dual Memory Matrix Gannet Optimization with Dynamic Weight Adjustment for Robust Feature Selection

Overview

This study introduces an improved gannet-based feature-selection method that keeps two complementary memories of promising solutions. It adapts the scoring weights to balance predictive performance with selecting fewer, more useful features. The authors evaluate it on benchmark functions and 17 datasets, comparing its results with established optimization methods. The approach aims to search more reliably, avoid premature convergence, and produce compact feature sets.