A hierarchical cooperative genetic programming for complex piecewise symbolic regression

In regression analysis, methodologies range from black-box approaches like artificial neural networks to white-box techniques like symbolic regression. Renowned for its trans-parency and interpretability, symbolic regression has become increasingly prominent in elucidating complex data relationships. Nevertheless, its effectiveness in managing complex piecewise symbolic regression tasks poses significant challenges. This paper introduces a novel Hierarchical Cooperative Genetic Program-ming (HCGP) framework to address this issue. The HCGP model utilizes a unique hierarchical structure, incorporating dual cooperative genetic programming (GP) populations. This innovative design significantly enhances the capability to solve complex piecewise symbolic regression problems. Implementing a scenario-based GP is central to the HCGP framework, which strategically selects the appropriate underlying calculation GP. This feature enables the system to autonomously learn and adapt to complex scenarios, selecting the most suitable calculation GPs for each case. Our HCGP approach distinguishes itself from traditional and state-of-the-art methods. It demonstrates particular proficiency in modeling piecewise expressions within complex scenarios. The empirical evaluation of our model, conducted using benchmark datasets, has exhibited its superior accuracy and computational efficiency. This progress emphasizes the potential of HCGP in sophisticated data modeling and marks a substantial advancement in a hierarchical structure in complex piecewise symbolic regression.