Simple implementation of Grammatical Evolution. Uses python3.
Install requirements
pip3 install -r requirements.txt
Paths are relative the repository root.
See tutorials. The tests can also help understand the program.
Find a symbolic regression, i.e. a symbolic expression that matches the input and output.
python main.py -f tests/configurations/symbolic_regression.yml -o results
python main.py -f tests/configurations/exemplars.yml -o results
The adversaries are coupled, i.e. dependent on each other.
python main.py -f tests/configurations/coevolution_symbolic_regression.yml -o results --coev
donkey_ge prints some information to stdout regarding settings and
search progress for each iteration, see donkey_ge.py:print_stats.
The output files have each generation as a list element, and each individual separated by a ,. They are written to:
donkey_ge_*_fitness_values.json
donkey_ge_*_length_values.json
donkey_ge_*_size_values.json
donkey_ge_*_solution_values.json
python main.py -h
usage: main.py [-h] -f CONFIGURATION_FILE [-o OUTPUT_DIR] [--coev]
Run donkey_ge
optional arguments:
-h, --help show this help message and exit
-f CONFIGURATION_FILE, --configuration_file CONFIGURATION_FILE
YAML configuration file. E.g.
configurations/demo_ge.yml
-o OUTPUT_DIR, --output_dir OUTPUT_DIR
Path to directory for output files. E.g.
donkey_ge_output
--coev Coevolution
Use multiprocessing for fitness evaluation
Configurations are in .yml format, see examples in folder configurations.
Grammar is in Backus-Naur Form (BNF), see examples in folder grammars
Tests are in tests folder. E.g. run with pytest
pytest tests
Some tests are written with Hypothesis, http://hypothesis.readthedocs.io/en/master/index.html
Use pre-commit.sh as a pre-commit hook. E.g. ln -s ../../pre-commit.sh .git/hooks/pre-commit
See docs/README.md for more details and basic Evolutionary Computation background.