DEF-SAC:Differential evolution framework based on multi-mutation strategies and self-adaptive configuration for feature selection

Feature selection is a critical preprocessing technique in machine learning that helps reduce data redundancy and noise while improving model performance. In high-dimensional datasets with large search spaces, effective feature selection is essential for identifying valuable information. Most existing methods rely heavily on hyperparameter tuning and exhibit poor generalization, limiting their applicability across diverse datasets. To address these issues, a Differential Evolution Framework with multi-mutation strategies fusion and self-adaptive configuration (DEF-SAC) is proposed for high-dimensional feature selection problems. The main innovations are: 1) We design a self-configuring framework for multi-mutation strategies to enhance diversity and search efficiency; 2) A novel dual Hamming distance-based population initialization method is developed. Using randomly binary reference individuals as the reference core and leveraging Hamming distance to quantify feature correlations for achieving population initialization and diversity; 3) We additionally introduce a feature-weighted evaluation model that uses entropy values to guide population updating and accelerate convergence. By comparing DEF-SAC with seven representative meta-heuristic algorithms and five single-mutation variants of DEF-SAC, we validated its performance across 18 high-dimensional datasets: DEF-SAC achieved higher classification accuracy and smaller feature subset sizes than other methods on 16 datasets, demonstrating exceptional optimization capabilities.