- author = "F. O. {de Franca} and M. Virgolin and M. Kommenda and M. S. Majumder and M. Cranmer and G. Espada and L. Ingelse and A. Fonseca and M. Landajuela and B. Petersen and R. Glatt and N. Mundhenk and C. S. Lee and J. D. Hochhalter and D. L. Randall and P. Kamienny and H. Zhang and G. Dick and A. Simon and B. Burlacu and Jaan Kasak and Meera Machado and Casper Wilstrup and W. G. {La Cava}",
- title = "{SRBench++}: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation",
- journal = "IEEE Transactions on Evolutionary Computation",
- note = "Early Access",
- keywords = "genetic algorithms, genetic programming, XAI, Benchmark testing, Task analysis, Accuracy, Evolutionary computation, Prediction algorithms, Machine learning algorithms, Current measurement, Symbolic Regression, Competition, Interpretable Machine Learning",
- ISSN = "1089-778X",
-
DOI = "
doi:10.1109/TEVC.2024.3423681",
-
code_url = "
https://github.com/cavalab/srbench",
- size = "12 pages",
-
abstract = "... We evaluate 12 modern symbolic regression
algorithms on thesebenchmarks and present an in-depth
analysis of the results,discuss current challenges of
symbolic regression algorithms and highlight possible
improvements for the benchmark itself
operon, PS-Tree, gpzgd, QLattice, uDSR, Bingo, eql, pysr, geneticengine, E2ET, LinearRegression, TaylorGP, nsga-dcgp",
-
notes = "Also known as \cite{10586218}
See also \cite{defranca2023interpretablesymbolicregressiondata}
COVID-19 data in New York state, USA.",
