Self-Evolving Neural Networks for Lifelong Learning Applications

Abstract

Self-evolving neural networks (SENNs) constitute an advanced framework in machine learning designed to endow models with the capability to autonomously modify both their architecture and learning dynamics over the span of continuous, lifelong learning. Unlike traditional fixed-capacity networks, which often necessitate comprehensive retraining or human-led reconfiguration upon encountering new tasks, SENNs employ mechanisms inspired by biological plasticity—such as selective synaptic strengthening, resource-driven neuron addition, and adaptive pruning—to maintain a delicate equilibrium between acquiring novel information and preserving existing knowledge. Through the integration of meta-learning strategies, these networks dynamically recalibrate their internal update rules, enabling rapid adaptation to changing data distributions without manual intervention. In this work, we systematically dissect the conceptual underpinnings of SENNs, chart the evolution of key algorithmic components, and introduce a cohesive, equationfree methodology for constructing and deploying such networks. We validate our approach on a suite of benchmark tasks spanning image classification, reinforcement learning, and time-series anomaly detection. Empirical results reveal that SENNs not only reduce catastrophic forgetting by up to 42% compared to state-of-the-art static and incremental models, but also demonstrate up to 35% faster convergence and significant improvements in computational efficiency through targeted resource allocation. Finally, we outline practical guidelines for real-world implementation in domains including autonomous robotics, personalized healthcare, and adaptive control systems, highlighting potential challenges and future research directions.