The primary limitation of NEOCS is the memory requirement to store the population genomes and the computational cost of fitness evaluation. Future work will focus on "island models" to distribute the evolutionary load across edge computing nodes.
For now, here’s a short, creative text using as a fictional term: The primary limitation of NEOCS is the memory
As autonomous systems transition from controlled industrial settings to dynamic real-world environments (e.g., urban air mobility, deep-sea exploration), the demand for control systems that can handle uncertainty has become paramount. Traditional Deep Reinforcement Learning (DRL) methods often suffer from "catastrophic forgetting" or require extensive retraining when the environment parameters shift. Unlike static deep learning models, NEOCS utilizes a
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. Unlike static deep learning models