This is a, easy-to-use, scikit-learn inspired version of the Genetic Algorithm (GA).
By using this file, you are agreeing to this product's EULA This product can be obtained in https://github.com/jespb/C-GA Copyright ©2023 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 GA can be used in a stand-alone fashion using the following command and flags:
$ exec_GA
[-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.
[-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
[-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
[-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_GA [parameters]
Useful methods: $ GA model = ga_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.
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