HTPI: A New Head–Tail Population Initialization for Feature Selection Stability in IoT IDSs with Post Hoc Explainable AI Analysis

Stochastic metaheuristic feature selection (FS) may generate unstable feature subsets across repeated runs, undermining reproducibility in Internet of Things (IoT) intrusion detection systems (IDSs). This paper presents Head–Tail Population Initialization (HTPI), a feature-importance-guided initialization approach for enhancing the stability of Adaptive Hybrid Genetic Algorithm–Simulated Annealing (AHGA-SA)-based FS. HTPI uses feature-importance scores to split candidate features into Head and Tail groups and initializes candidate subsets by prioritizing Head features and sampling Tail features with importance-based weights. HTPI is integrated into AHGA-SA as an incremental extension, termed HTPI-AHGA-SA, and modifies only the initialization and reinitialization steps. Experiments on eight IoT-oriented IDS datasets using 50 runs per configuration, with seeds paired across methods, showed significantly higher Nogueira stability under HTPI-AHGA-SA on all datasets after Holm correction, with non-overlapping 95% leave-one-run-out jackknife confidence intervals in every case. These results characterize algorithmic cross-run stability under a fixed data partition. All absolute differences in dataset-level mean F1 Macro remained below 0.003; formal equivalence at this margin was supported for six datasets, while dataset-specific security-metric trade-offs remained. On three representative datasets, post hoc explainable artificial intelligence (XAI) analyses indicated generally higher permutation importance (PI)-based cross-run consistency and measurable predictive utility in the selected Head and Tail portions under retraining.