Racing Control Variable Genetic Programming for Symbolic Regression
Authors: Nan Jiang, Yexiang Xue
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Racing CVGP on several synthetic and real-world datasets corresponding to true physics laws. We demonstrate that Racing CVGP outperforms CVGP and a series of symbolic regressors which discover equations from fixed datasets. |
| Researcher Affiliation | Academia | Nan Jiang, Yexiang Xue Department of Computer Science, Purdue University, USA {jiang631, yexiang}@purdue.edu |
| Pseudocode | Yes | Algorithm 1: Racing Control Variable Genetic Programming |
| Open Source Code | Yes | 1The code is at https://bitbucket.org/xlnxyx/racing cvgp. Please refer to https://arxiv.org/abs/2309.07934 for the Appendix. |
| Open Datasets | Yes | We consider several publicly available and multivariable datasets, including 1) Trigonometric datasets (Jiang and Xue 2023), 2) Livermore2 datasets (Petersen et al. 2021), and 3) Feynamn datasets (Matsubara et al. 2022). |
| Dataset Splits | No | The paper mentions using datasets for experiments but does not provide specific details on training, validation, or test splits (e.g., percentages, sample counts, or explicit splitting methodology). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., specific GPU/CPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | No | The paper does not explicitly provide details about the experimental setup such as specific hyperparameter values (e.g., learning rate, batch size, epochs) or optimizer settings in the main text. |