改进符号回归基准测试，提升模型发现效率与准确性。
Fast Symbolic Regression Benchmarking
- 采用可接受表达式列表与早期终止回调机制
- SymbolicRegression.jl rediscovery率从26.7%提升至44.7%
- 节省41.2%计算开销，适合科研建模与算法评估
符号回归（SR）从数据中发掘数学模型。现有基准测试过度强调恢复唯一表达式形式，或仅依赖计算机代数系统（如SymPy）判定成功，且在发现后仍持续搜索。本文通过引入经筛选的可接受表达式列表和早期终止回调机制，改进基准测试方法。以Yoshitomo等提出的科学发现符号回归（SRSD）问题为起点，对SymbolicRegression.jl和TiSR两个包进行评估。新方法使SymbolicRegression.jl的重发现率由26.7%提升至44.7%，计算开销减少41.2%；TiSR的重发现率为69.4%，基准测试耗时节省63%。
原文摘要 · Abstract (English)
Symbolic regression (SR) uncovers mathematical models from data. Several benchmarks have been proposed to compare the performance of SR algorithms. However, existing ground-truth rediscovery benchmarks overemphasize the recovery of "the one" expression form or rely solely on computer algebra systems (such as SymPy) to assess success. Furthermore, existing benchmarks continue the expression search even after its discovery. We improve upon these issues by introducing curated lists of acceptable expressions, and a callback mechanism for early termination. As a starting point, we use the symbolic regression for scientific discovery (SRSD) benchmark problems proposed by Yoshitomo et al., and benchmark the two SR packages SymbolicRegression.jl and TiSR. The new benchmarking method increases the rediscovery rate of SymbolicRegression.jl from 26.7%, as reported by Yoshitomo et at., to 44.7%. Performing the benchmark takes 41.2% less computational expense. TiSR's rediscovery rate is 69.4%, while performing the benchmark saves 63% time.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。