Dual-Population Multi-Objective Optimization with Multi-Scale Co-expression Modeling for Medical Gene Expression Feature Selection

Medical gene expression feature selection is challenged by the high-dimensional small-sample regime and strong co-expression redundancy, which often yields unstable subsets and brittle trade-offs between predictive performance and compactness. This paper proposes a two-stage framework, Class-guided Joint Hybrid Multi-objective Optimizer (CJHMO), that integrates structural candidate generation with wrapper-based multi-objective optimization. In stage one, a Multi-Scale Co-Expression Attention Network (MSCANet) constructs correlation graphs under multiple thresholds and extracts connected components as co-expression modules. Each module is summarized by its eigengene (the first principal component), and the correlation ratio is used to quantify the association between eigengenes and class labels, producing supervised module scores. These scores are converted into attention weights and propagated to genes for candidate ranking and screening. In stage two, we develop a Dual-Population Heterogeneous Multi-Objective optimizer (DPHMO), where a decomposition-based population emphasizes Pareto-front coverage and global exploration, while an elite-guided particle swarm focuses on local exploitation and refinement. The two populations share an external elite archive (EP) for cross-population information exchange and non-dominated solution maintenance, jointly minimizing classification error rate and feature selection rate. Experiments on multiple public benchmarks with several classifiers demonstrate that CJHMO achieves superior performance-compression trade-offs over representative multi-objective baselines, with improved Pareto quality reflected by HV and IGD.