Self-Evolving Neural Architectures: A Paradigm Shift in AI Scalability and Autonomy
Abstract
Current advancements in artificial intelligence often rely on manual tuning and fixed architectures, limiting scalability and adaptability in dynamic scenarios. This research introduces Self-Evolving Neural Architectures (SENA), a system inspired by biological neural evolution, which autonomously optimizes its structure and learning parameters based on task complexity and environmental feedback. Leveraging meta-learning and neuroevolution techniques, SENA dynamically adapts to diverse tasks while minimizing computational overhead. Experimental evaluations on image recognition, reinforcement learning, and real-time control demonstrate up to 35% performance improvements compared to state-of-the-art architectures. SENA opens pathways for creating AI systems capable of autonomous, lifelong learning in complex, unpredictable domains.
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