A Hierarchical Cooperative Genetic Programming for Complex Piecewise Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{chen:2024:CEC,

• author = "Xinan Chen and Wenjie Yi and Ruibin Bai and Rong Qu and Yaochu Jin",
• title = "A Hierarchical Cooperative Genetic Programming for Complex Piecewise Symbolic Regression",
• booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2024",
• editor = "Bing Xue",
• address = "Yokohama, Japan",
• month = "30 " # jun # " - 5 " # jul,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Adaptation models, Computational modeling, Sociology, Evolutionary computation, Data models, Regression analysis, symbolic regression, hierarchical structure, evolutionary algorithm",
• isbn13 = "979-8-3503-0837-2",
• DOI = "doi:10.1109/CEC60901.2024.10611754",
• abstract = "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 uses 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 modelling 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 modelling and marks a substantial advancement in a hierarchical structure in complex piecewise symbolic regression.",
• notes = "also known as \cite{10611754}

  WCCI 2024",

}

Genetic Programming entries for Xinan Chen Wenjie Yi Ruibin Bai Rong Qu Yaochu Jin

Citations