Feature selection is indispensable for reducing dimensionality and improving performance and interpretability in high-dimensional domains such as gene expression analysis. Traditional binary adaptations of the Grey Wolf Optimizer (BGWO) often suffer from premature convergence and tend to select feature subsets containing high redundancy when relying on simple single-objective fitness functions. To overcome these limitations, we introduce the Adaptive Redundancy-aware Binary Grey Wolf Optimizer (AR-BGWO), a novel single-objective metaheuristic specifically tailored for binary feature selection. AR-BGWO incorporates two principal innovations: a non-linear, stagnation-responsive adaptation of the exploration-exploitation parameter \(a\) , which enables more effective navigation of the search space, and an implicit redundancy penalty within the fitness function that discourages highly correlated feature selections, thereby promoting both accuracy and conciseness without resorting to multi-objective formulations. Experimental analyses indicate that AR-BGWO consistently identifies smaller, less redundant feature sets while achieving higher or comparable classification accuracy relative to standard BGWO and other state-of-the-art methods.