Distributed Genetic Algorithms (DGA) is a Python based library that runs paralellized, user-customizable genetic algorithms. Given you have the appropriate hardware, the paralellization should net a boost in performance.
DGA is designed to be highly generalizable, and can run theoretically train any model with a quantifiable 'fitness'.
VScode is the intended IDE for running distributed GA. Should still be compatible with other IDE's, but currently only the .vscode directory is setup.
To run on other IDEs you will need to set the PYTHONPATH environment variable to the DGA base directory on your local machine ("~/path/to/Distributed_GA"). Setting this up will depend on your IDE, but most modern IDE's should have a way to do this.
- Clone repository
- Select a Python interpreter (3.10+ recommended)
- Build and install the package by running the following command in the terminal:
pip install -e .To see examples, navigate to the scripts\Vector Estimate Benchmark folder. Here you will find 2 files, vector_estimator.py and vector_estimator_(parallel).py. The former runs a single-threaded genetic algorithm, while the latter runs a multi-threaded genetic algorithm. Note that the multi-threaded version will be less efficient, and is only included for testing purposes.
To run either, simply run the files from the terminal like this:
python3 vector_estimator.pycode_docs.pdf
Contains in depth info about Server, Model, and Algorithm objects and how they are used. See here for any info about how to use the libraries various objects two pre-packaged algorithms
backend_docs.md
A more in depth look at how to program your own genetic algorithms and understanding how the Server executes.