Evolutionary Deep Learning and Self-Optimizing Neural Networks for Continual Learning and Adaptive Model Selection in Autonomous Artificial Intelligence Systems

Abstract

Continual learning and adaptive model selection are critical challenges in autonomous artificial intelligence (AI) systems. Traditional deep learning models suffer from catastrophic forgetting and inefficiencies when encountering non-stationary data distributions. Evolutionary deep learning (EDL) and self-optimizing neural networks (SONNs) present promising solutions by leveraging evolutionary algorithms to dynamically adjust architectures and learning strategies. This paper explores state-of-the-art methodologies, discussing their impact on continual learning and AI autonomy. We provide an in-depth review of literature, compare performance metrics, and present experimental findings with charts and graphs illustrating performance improvements.