A critical component of NEOCS is the fitness function, which must balance operational goals with safety constraints. We define fitness $F$ as: $$ F = \alpha \cdot \textGoalAchievement + \beta \cdot \textEnergyEfficiency - \gamma \cdot \textSafetyViolations $$ Where $\alpha, \beta, \gamma$ are weighting coefficients determined by the mission profile.
The system operates on a dual-loop mechanism: A critical component of NEOCS is the fitness
The rapid deployment of autonomous agents in dynamic and unstructured environments has exposed the limitations of traditional reinforcement learning and hardcoded control logic. This paper introduces the , a novel framework that integrates neuro-evolutionary strategies with real-time operational control. Unlike static deep learning models, NEOCS utilizes a genetic algorithm to evolve the topology and weights of neural networks, allowing the system to adapt to unforeseen environmental variables without explicit retraining. We demonstrate that NEOCS provides superior robustness and faster recovery times in simulation environments characterized by partial observability and hardware degradation. This paper introduces the , a novel framework