Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition
2023
Article
al
| Author(s): | FO de Franca and M Virgolin and M Kommenda and MS 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 CS Lee and JD Hochhalter and DL 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 WG La Cava |
| Journal: | arXiv |
| Year: | 2023 |
| Department(s): | Autonomous Learning |
| Bibtex Type: | Article (article) |
| URL: | https://arxiv.org/abs/2304.01117 |
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BibTex @article{gecco_symreg22,
title = {Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition},
author = {de Franca, FO and Virgolin, M and Kommenda, M and Majumder, MS and Cranmer, M and Espada, G and Ingelse, L and Fonseca, A and Landajuela, M and Petersen, B and Glatt, R and Mundhenk, N and Lee, CS and Hochhalter, JD and Randall, DL and Kamienny, P and Zhang, H and Dick, G and Simon, A and Burlacu, B and Kasak, Jaan and Machado, Meera and Wilstrup, Casper and Cava, WG La},
journal = {arXiv},
year = {2023},
doi = {},
url = {https://arxiv.org/abs/2304.01117}
}
|
|
