A Prior-Guided Structure-Aware Multi-Objective Differential Evolution Method for High-Dimensional Feature Selection

Abstract

Multi-objective feature selection provides an effective framework for high-dimensional data analysis by jointly considering classification performance and feature subset size. However, redundant and irrelevant features may degrade the quality of the initial search distribution, and population aggregation may reduce the structural diversity of non-dominated feature subsets. To address these issues, this paper proposes PGS-MODE-FS, a prior-guided and structure-aware multi-objective differential evolution method for high-dimensional feature selection. Specifically, feature–class relevance and feature redundancy are integrated into a unified feature importance measure to guide the generation of candidate solutions with different sparsity levels. The same feature-priority information is further used to construct a Top-k activated subspace, in which individuals are assigned to multiple islands according to their structural differences on informative features, thereby promoting diverse evolutionary search. Experiments on 17 public and biomedical datasets, including parameter analysis, comparative experiments, and ablation studies, demonstrate that PGS-MODE-FS achieves competitive performance in solution-set quality, classification accuracy, feature reduction, and computational efficiency. Further diversity analysis shows that the proposed multi-island mechanism effectively preserves structural diversity during evolution.