Superparameter Optimization of Complex Function Structures through Adaptive Neural Networks

Abstract

This paper presents a novel approach to parameter optimization utilizing adaptive neural networks, specifically designed to model complex function structures. The core innovation lies in integrating function structure modeling directly into the optimization process, enabling automated learning and optimization of the combined parameter configurations. We leverage reinforcement learning to dynamically adjust the function parameters, achieving improved performance across a range of benchmark datasets. The method addresses limitations of traditional approaches by offering a more intuitive and adaptable framework for parameter tuning. The study demonstrates significant improvements in accuracy and efficiency compared to existing methods.