Learning-Driven Adaptive Genetic Algorithm for Real-World Dynamic Energy Systems

Abstract

Learning-enabled optimization in dynamic energy systems demands more than high-quality solutions; it requires sustained adaptation under non-stationary and shock-driven environments. This paper proposes Adaptive Genetic Algorithm with Dynamic Operator Scoring and Operator Elimination (AGA-DO), an evolutionary learning framework that explicitly models operator-level learning to achieve long-term robustness. In contrast to conventional genetic algorithms with static or heuristically adaptive operators, AGA-DO continuously evaluates operator contributions, applies temporal smoothing to stabilize learning dynamics, and eliminates persistently ineffective operators to prevent negative knowledge accumulation. Unlike conventional adaptive genetic algorithms that primarily focus on parameter adaptation and peak optimization performance, AGA-DO emphasizes operator-level learning, learning stability, and recovery capability under dynamic environments. A ve-phase validation framework is employed to progressively evaluate effectiveness, learning behavior, robustness, and real-world recovery capability. Beyond instantaneous performance metrics, Cumulative Regret Loss (CRL) is adopted as an observational indicator to quantify the accumulated cost of delayed adaptation and repeated suboptimal decisions over time. Results show that benchmark algorithms accumulate substantially higher regret under repeated environmental shocks, whereas AGA-DO achieves the lowest cumulative regret (CRL = 0.81) and the best overall Friedman rank (1.2). These findings suggest that AGA-DO can serve as an effective learning-driven evolutionary optimizer for real-world dynamic energy systems. The proposed framework emphasizes learning stability and retention over aggressive convergence, aligning with practical requirements of real-world adaptive optimization.