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Bingo is an open source package for performing symbolic regression, Though it can be used as a general purpose evolutionary optimization package.

• Integrated local optimization strategies
• Parallel island evolution strategy implemented with mpi4py
• Coevolution of fitness predictors

At this point, the API is still in a state of flux. The current release has a much more stable API but still lacks some of the features of older releases.

The Bingo repository uses git submodules so make sure to clone all the submodules when cloning. Git has an easy way to do this with:

git clone --recurse-submodules ...

Bingo is intended for use with Python 3.x. Bingo requires installation of a few dependencies which are relatively common for data science work in python:

• numpy
• scipy
• matplotlib
• mpi4py (if parallel implementations are to be run)
• pytest, pytest-mock (if the testing suite is to be run)

A requirements.txt file is included for easy installation of dependencies with pip or conda.

Installation with pip:

pip install -r requirements.txt

Installation with conda:

conda install --yes --file requirements.txt

A section of bingo is written in c++ for increased performance. In order to take advantage of this capability, the code must be compiled. See the documentation in the bingocpp submodule for more information.

Note that bingo can be run without the bingocpp portion, it will just have lower performance.

If bingocpp has been properly installed, the following command should run without error.

python -c "from bingocpp"

A common error in the installation of bingocpp is that it must be built with the same version of python that will run your bingo scripts. The easiest way to ensure consistent python versioning is to build and run in a Python 3 virtual environment.

Sphynx is used for automatically generating API documentation for bingo. The most recent build of the documentation can be found in the repository at: doc/_build/html/index.html

An extensive unit test suite is included with bingo to help ensure proper installation. The tests can be run using pytest on the tests directory, e.g., by running:

python -m pytest tests 

from the root directory of the repository.

The best place to get started in bingo is by going through the jupyter notebook tutorials in the examples directory. They step through several of the most important aspects of running bingo with detailed explanations at each step. The examples directory also contains several python scripts which may act as a good base when starting to write your own custom bingo scripts.

1. Fork it (https://github.com/nasa/bingo/fork)
2. Create your feature branch (git checkout -b feature/fooBar)
3. Commit your changes (git commit -am 'Add some fooBar')
4. Push to the branch (git push origin feature/fooBar)
5. Create a new Pull Request

We use SemVer for versioning. For the versions available, see the tags on this repository.

• Geoffrey Bomarito
• Tyler Townsend
• Jacob Hochhalter
• Ethan Adams
• Kathryn Esham
• Diana Vera

Copyright 2018 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. No copyright is claimed in the United States under Title 17, U.S. Code. All Other Rights Reserved.

The Bingo Mini-app framework is licensed under the Apache License, Version 2.0 (the "License"); you may not use this application except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 .

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.