Multiform Genetic Programming Framework for Symbolic Regression Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.8344

@Article{Zhong:2025:TEVC,

• author = "Jinghui Zhong and Junlan Dong and Wei-Li Liu and Liang Feng and Jun Zhang",
• title = "Multiform Genetic Programming Framework for Symbolic Regression Problems",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2025",
• volume = "29",
• number = "2",
• pages = "429--443",
• month = apr,
• keywords = "genetic algorithms, genetic programming, gene expression programming, Optimisation, Search problems, Multitasking, Adaptation models, Semantics, Mathematical models, Transfer learning, Resource management, Knowledge transfer, multiform optimisation, multitasking optimisation, transfer learning (TL), symbolic regression (SR)",
• ISSN = "1941-0026",
• DOI = "doi:10.1109/TEVC.2025.3527875",
• abstract = "genetic programming (GP) is a widely recognised and powerful approach for symbolic regression (SR) problems. However, existing GP methods rely on a single form to solve the problem, which limits their search diversity and increases the likelihood of getting stuck in local optima, especially in complex scenarios. In this article, we propose a general multiform GP (MFGP) framework to improve the performance of GP on complicated SR problems. As far as we know, this articel is the first attempt to integrate the multiform optimisation paradigm with GP to accelerate the search performance. The key idea of the proposed framework is to construct multiple forms to solve the same problem cooperatively at the same time. During the evolution process, knowledge gained from different forms is shared among the solvers to improve the search diversity and efficiency. A knowledge transfer mechanism is specifically designed to facilitate knowledge transfer among GP solvers with different modelling forms. In addition, an adaptive resource control mechanism is designed to reallocate computing resources according to the problem solving efficiency of different solvers to further improve search efficiency. To demonstrate the effectiveness of the proposed framework, a multiform gene expression programming algorithm is designed and tested on 20 problems, including physical datasets, synthetic datasets, and real-world datasets. The experimental results have demonstrated the effectiveness of the proposed framework.",
• notes = "Also known as \cite{10835810}",

}

Genetic Programming entries for Jinghui Zhong Junlan Dong Wei-Li Liu Liang Feng Jun Zhang

Citations