CORAL: A Rank-Memory Search Framework for Multi-Objective Feature Selection

High-dimensional feature selection aims to identify compact and discriminative feature subsets from large feature spaces. In multi-objective feature selection (MOFS), this task remains challenging because the search space grows exponentially with dimensionality, and conventional binary evolutionary operators may generate ineffective perturbations in sparse high-dimensional spaces. To address these issues, this paper proposes CORAL, a rank-memory search framework for MOFS. CORAL uses a joint continuous score–cardinality representation to model feature priorities and subset sizes and applies Top-K decoding to obtain binary feature subsets. A rank-memory mechanism is introduced to extract feature occurrence information from elite solutions and guide score-space variation. In addition, elite local refinement and feature-number-stratified environmental selection are used to refine candidate subsets and maintain solutions across different sparsity regions. Experiments on 18 benchmark classification datasets show that CORAL achieves balanced performance in terms of solution-set quality, test classification performance, feature compactness, and computational efficiency. Ablation results further demonstrate the complementary roles of rank memory, elite local refinement, and stratified environmental selection.