RADE: A Symmetry-Inspired Resource-Adaptive Differential Evolution for Lightweight Dendritic Learning in Classification Tasks

This study proposes Resource-Adaptive Differential Evolution (RADE), a novel optimization algorithm for training lightweight and interpretable dendritic neuron models (DNMs) in classification tasks. RADE introduces dynamic population partitioning, poor-individual-guided mutation, adaptive parameter control, and lightweight archiving to achieve efficient and robust learning. Inspired by biological and algorithmic symmetry, RADE leverages structural and behavioral balance in both the evolutionary process and the DNM architecture. DNMs inherently exhibit symmetric processing through multiple dendritic branches that independently and equivalently aggregate localized inputs. RADE preserves and enhances this structural symmetry by promoting balanced learning dynamics and pruning redundant dendritic components, leading to compact and interpretable neuron morphologies. Extensive experiments on real-world and synthetic datasets demonstrate that RADE consistently outperforms existing methods in terms of classification accuracy, convergence stability, and model compactness. Furthermore, the resulting neuron structures can be mapped to logical circuits, making the RADE-DNM highly suitable for neuromorphic and edge computing applications. This work highlights the synergistic role of symmetry in achieving resource-efficient and transparent artificial intelligence.