https://github.com/jespb/c-stdgp
My C implementation of the Standard Genetic Programming Algorithm.
https://github.com/jespb/c-stdgp
Last synced: 10 months ago
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My C implementation of the Standard Genetic Programming Algorithm.
- Host: GitHub
- URL: https://github.com/jespb/c-stdgp
- Owner: jespb
- Created: 2022-01-11T10:37:38.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2025-07-22T02:30:09.000Z (11 months ago)
- Last Synced: 2025-07-22T04:26:32.500Z (11 months ago)
- Language: C
- Size: 51.8 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# C-StdGP
This is a, easy-to-use, scikit-learn inspired version of the Standard Genetic Programming (StdGP) algorithm.
By using this file, you are agreeing to this product's EULA
This product can be obtained in https://github.com/jespb/C-StdGP
Copyright ©2021-2022 J. E. Batista
This file contains information about the command and flags used in the stand-alone version of this implementation and an explanation on how to import, use and edit this implementation.
This implementation of StdGP can be used in a stand-alone fashion using the following command and flags:
$ python Main_StdGP_standalone.py
[-d datasets]
- This flag expects a set of csv dataset names separated by ";" (e.g., a.csv;b.csv)
- By default, the heart.csv dataset is used
[-dsdir dir]
- States the dataset directory.
- By default "datasets/" is used
- Use "-dsdir ./" for the root directory
[-es elite_size]
- This flag expects an integer with the elite size;
- By default, the elite has size 1.
[-md max_depth]
- This flag expects an integer with the maximum initial depth for the trees;
- By default, this value is set to 6.
[-mg max_generation]
- This flag expects an integer with the maximum number of generations;
- By default, this value is set to 100.
[-odir dir]
- States the output directory.
- By default "results/" is used
- Use "-odir ./" for the root directory
[-op operators]
- This flag excepts a set of operators separated by ";"
- Allowed operators: +;-;*;/
- By default, the used operators are the sum, subtraction, multiplication and protected division.
[-ps population_size]
- This flag expects an integer with the size of the population;
- By default, this value is set to 500.
[-runs number_of_runs]
- This flag expects an integer with the number of runs to be made;
- By default, this values is set to 30
[-tf train_fraction]
- This flag expects a float [0;1] with the fraction of the dataset to be used in training;
- By default, this value is set to 0.70
[-ts tournament_size]
- This flag expects an integer with the tournament size;
- By default, this value is set to 10.
[-t number_of_threads]
- This flag expects an integer with the number of threads to use while evaluating the population;
- If the value is set to 1, the multiprocessing library will not be used
- By default, this value is set to 1.
How to use this implementation:
$ make
$ ./exec_StdGP [parameters]
Useful methods:
$ struct StdGP_t* model = stdgp_create( ... ) -> creates the population;
$ fit(model) -> trains the model;
$ predict_classification(model, X, No_of_samples) -> Returns a list with the prediction of the given dataset.
$ predict_regression(model, X, No_of_samples) -> Returns a list with the prediction of the given dataset.
Reference:
Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming. Lulu Enterprises, UK Ltd (2008)
If you use this implementation, please cite one of the following works where I use the Std-GP algorithm:
@article{BATISTA2025101761,
title = {Complexity, interpretability and robustness of GP-based feature engineering in remote sensing},
journal = {Swarm and Evolutionary Computation},
volume = {92},
pages = {101761},
year = {2025},
issn = {2210-6502},
doi = {https://doi.org/10.1016/j.swevo.2024.101761},
url = {https://www.sciencedirect.com/science/article/pii/S2210650224002992},
author = {João E. Batista and Adam K. Pindur and Ana I.R. Cabral and Hitoshi Iba and Sara Silva},
}
@article{Rodrigues2023,
title = {Exploring SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming},
volume = {5},
ISSN = {2661-8907},
url = {http://dx.doi.org/10.1007/s42979-023-02106-3},
DOI = {10.1007/s42979-023-02106-3},
number = {1},
journal = {SN Computer Science},
publisher = {Springer Science and Business Media LLC},
author = {Rodrigues, Nuno M. and Batista, João E. and Cava, William La and Vanneschi, Leonardo and Silva, Sara},
year = {2023},
month = dec
}
@inproceedings{10.1145/3520304.3533946,
author = {Batista, Jo\~{a}o E. and Silva, Sara},
title = {Evolving a cloud-robust water index with genetic programming},
year = {2022},
isbn = {9781450392686},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3520304.3533946},
doi = {10.1145/3520304.3533946},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
pages = {55–56},
numpages = {2},
location = {Boston, Massachusetts},
series = {GECCO '22}
}