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Neuro-evolution, Autonomous Systems, Genetic Algorithms, Adaptive Control, NEAT.

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. urban air mobility