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提出 Dirichlet-Sinkhorn Constant Averaging（DSCA）校准策略，通过在不同协变量分布的数据子集上独立校准候选表达式并取参数均值，为符号回归进化搜索提供额外选择压力，提升函数恢复精度与复杂度权衡。
The paper investigates using Large Language Models as post-hoc auditors to evaluate symbolic regression models for interpretability and medical plausibility, with clinician assessments showing comparative model rankings are more favorably perceived than term-level interpretations.
InsightSR is a framework that leverages Large Language Models to refine the search space for symbolic regression, improving accuracy and physical consistency through iterative semantic and structural guidance.
This paper proposes an adaptive protection mechanism for evolutionary feature construction in symbolic regression to preserve important constructed features during evolution, demonstrating improved performance on regression benchmarks and credit classification tasks.
Introduces ELMER, an evolutionary language model that searches over natural-language policy descriptions and compiles them into executable programs, using fine-tuned Qwen3-8B with Direct Preference Optimization to control mutation strength and improve search efficiency.
GP_ELITE is a pure-Python library for genetic-programming based symbolic regression, enabling discovery of interpretable mathematical formulas from small experimental datasets. Version 0.2.0 introduces Levenberg–Marquardt constant fitting, multi-restart reliability, Pareto front output, and extrapolation mode.
This paper introduces Minimalist Genetic Programming (MGP), a novel algorithm that replaces evolution with a syntactic derivation process inspired by the Minimalist Program from linguistics, using a MERGE operator to construct symbolic expressions. MGP consistently finds exact ground truth models on symbolic regression tasks where standard GP struggles due to bloat.
This paper demonstrates that large language models trained on code can significantly enhance genetic programming mutation operators, enabling the generation of hundreds of thousands of functional Python programs for robot design in the Sodarace domain without prior training data. The approach, called Evolution through Large Models (ELM), combines LLMs with MAP-Elites to bootstrap new conditional models for context-specific artifact generation.