EMG-Hybrid: A Dual-Engine Elite Memory--Guided Adaptive Optimization Framework for High-Dimensional Feature Selection

arXiv:2611.01140v1 Announce Type: new Abstract: Feature selection in ultra-high-dimensional, noisy datasets remains a critical challenge due to exponential search space growth, where conventional binary metaheuristics often suffer from premature convergence. To address this, EMG-Hybrid is introduced as a novel three-level hierarchical optimization architecture designed around decoupled operational planes, feedback-driven scheduling, and isolated state refinement. At Level 1, a Dual-Engine Controller monitors population Shannon entropy and empirical fitness velocity, functioning as an architectural master-switch that dynamically routes execution between global exploration and local intensification. This control loop conditions the Level 2 active optimization plane, where an Adaptive Black Hole Gravity (ABHG) execution layer performs aggressive feature manifold pruning via a distance-decoupled penalty kernel driven by fitness similarities and Hamming metrics. To structurally isolate macro-exploration from micro-exploitation, Level 3 encapsulates an autonomous Elite Memory Archive that preserves high-fidelity sub-schemas and hosts a discrete bitwise Differential Evolution (DE) micro-refinement sandbox for resolving complex parameter epistasis. Theoretically, uniform combinatorial bounds derived via Vapnik--Chervonenkis (VC) dimension criteria demonstrate that this multi-level restriction mitigates high-dimensional overfitting. Empirical results across manifolds scaling up to $d > 100{,}000$ features, complemented by high-performance computing (HPC) profiling under lock-free atomic Compare-and-Swap (CAS) primitives, indicate strong performance gains over state-of-the-art baselines. The framework exhibits near-linear parallel speedups, sub-quadratic runtime scaling, and substantial dimensionality reduction, offering a scalable and structurally innovative paradigm for large-scale data mining.