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} }