GESR: A Geometric Evolution Model for Symbolic Regression

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@Article{Ma:2026:EC,

• author = "Zhitong Ma and Jinghui Zhong",
• title = "{GESR}: A Geometric Evolution Model for Symbolic Regression",
• journal = "Evolutionary Computation",
• note = "Early Access",
• keywords = "genetic algorithms, genetic programming, GP, Geometric Semantic Operator, Symbolic Regression, Semantic Gradient",
• ISSN = "1063-6560",
• DOI = "10.1162/EVCO.a.367",
• code_url = "https://github.com/MZT-srcount/GESR",
• abstract = "... we propose a novel Geometric Evolution Symbolic Regression algorithm. Leveraging geometric semantics, the process of symbolic regression in GESR is transformed into an approximation to an unimodal target in n-dimensional semantic space. Then, three key modules are presented to enhance the approximation: (1) a new semantic gradient concept, proposed from the observation of inaccurate approximation results within semantic backpropagation, to assist the exploration in the semantic space and improve the accuracy of semantic approximation; (2) a new geometric semantic search operator, tailored for efficiently approximating the target formula directly in the sparse semantic space, to obtain more accurate and interpretable solutions under strict program size constraints; (3) the Levenberg-Marquardt algorithm with L1 regularization, used for the adjustment of expression structures and the optimization of global subtree weights to assist the proposed geometric semantic search operator. Assisted with these modules, GESR achieves state-of-the-art accuracy performance on SRSD benchmark datasets. The implementation is available at https://github.com/MZT-srcount/GESR.",
• notes = "School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China",

}

Genetic Programming entries for Zhitong Ma Jinghui Zhong

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