https://github.com/heal-research/operon
C++ Large Scale Genetic Programming
https://github.com/heal-research/operon
cmake cpp cpp17 genetic-algorithm genetic-programming machine-learning mathematical-expressions optimization-algorithms python python-bindings symbolic-regression vcpkg
Last synced: 16 days ago
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C++ Large Scale Genetic Programming
- Host: GitHub
- URL: https://github.com/heal-research/operon
- Owner: heal-research
- License: mit
- Created: 2019-09-10T20:19:07.000Z (about 6 years ago)
- Default Branch: main
- Last Pushed: 2025-09-08T11:04:15.000Z (2 months ago)
- Last Synced: 2025-09-08T13:09:56.794Z (2 months ago)
- Topics: cmake, cpp, cpp17, genetic-algorithm, genetic-programming, machine-learning, mathematical-expressions, optimization-algorithms, python, python-bindings, symbolic-regression, vcpkg
- Language: C++
- Homepage: https://operongp.readthedocs.io
- Size: 22.6 MB
- Stars: 174
- Watchers: 9
- Forks: 33
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# Modern C++ framework for Symbolic Regression
[](https://github.com/heal-research/operon/blob/master/LICENSE)
[](https://github.com/heal-research/operon/actions/workflows/build-linux.yml)
[](https://github.com/heal-research/operon/actions/workflows/build-macos.yml)
[](https://github.com/heal-research/operon/actions/workflows/build-windows.yml)
[](https://operongp.readthedocs.io/en/latest/?badge=latest)
[](https://gitter.im/operongp/community)
*Operon* is a modern C++ framework for [symbolic regression](https://en.wikipedia.org/wiki/Symbolic_regression) that uses [genetic programming](https://en.wikipedia.org/wiki/Genetic_programming) to explore a hypothesis space of possible mathematical expressions in order to find the best-fitting model for a given [regression target](https://en.wikipedia.org/wiki/Regression_analysis).
Its main purpose is to help develop accurate and interpretable white-box models in the area of [system identification](https://en.wikipedia.org/wiki/System_identification). More in-depth documentation available at https://operongp.readthedocs.io/.
## How does it work?
Broadly speaking, genetic programming (GP) is said to evolve a population of "computer programs" ― [AST](https://en.wikipedia.org/wiki/Abstract_syntax_tree)-like structures encoding behavior for a given problem domain ― following the principles of [natural selection](https://en.wikipedia.org/wiki/Natural_selection). It repeatedly combines random program parts keeping only the best results ― the "fittest". Here, the biological concept of [fitness](https://en.wikipedia.org/wiki/Survival_of_the_fittest) is defined as a measure of a program's ability to solve a certain task.
In symbolic regression, the programs represent mathematical expressions typically encoded as [expression trees](https://en.wikipedia.org/wiki/Binary_expression_tree). Fitness is usually defined as [goodness of fit](https://en.wikipedia.org/wiki/Goodness_of_fit) between the dependent variable and the prediction of a tree-encoded model. Iterative selection of best-scoring models followed by random recombination leads naturally to a self-improving process that is able to uncover patterns in the data:
## Build instructions
The project requires CMake and a compiler supporting C++20. The recommended way to build Operon is via either [nix](https://github.com/NixOS/nix) or [vcpkg](https://github.com/microsoft/vcpkg).
Check out [https://github.com/heal-research/operon/blob/master/BUILDING.md](BUILDING.md) for detailed build instructions and how to enable/disable certain features.
### Nix
First, you have to [install nix](https://nixos.org/download.html) and [enable flakes](https://nixos.wiki/wiki/Flakes).
For a portable install, see [nix-portable](https://github.com/DavHau/nix-portable).
To create a development shell:
```
nix develop github:heal-research/operon --no-write-lock-file
```
To build Operon (a symlink to the nix store called `result` will be created).
```
nix build github:heal-research/operon --no-write-lock-file
```
### Vcpkg
Select the build generator appropriate for your system and point CMake to the `vcpkg.cmake` toolchain file
```
cmake -S . -B build -G "Visual Studio 16 2019" -A x64 \
-DCMAKE_TOOLCHAIN_FILE=..\vcpkg\scripts\buildsystems\vcpkg.cmake \
-DVCPKG_OVERLAY_PORTS=.\ports
```
The file `CMakePresets.json` contains some presets that you may find useful. For using `clang-cl` instead of `cl`, pass `-TClangCL` to the above ([official documentation](https://docs.microsoft.com/en-us/cpp/build/clang-support-cmake?view=msvc-170)).
## Python wrapper
Python bindings for the Operon library are available as a separate project: [PyOperon](https://github.com/heal-research/pyoperon), which also includes a [scikit-learn](https://scikit-learn.org/stable/index.html) compatible regressor.
## Bibtex info
If you find _Operon_ useful you can cite our work as:
```
@inproceedings{10.1145/3377929.3398099,
author = {Burlacu, Bogdan and Kronberger, Gabriel and Kommenda, Michael},
title = {Operon C++: An Efficient Genetic Programming Framework for Symbolic Regression},
year = {2020},
isbn = {9781450371278},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3377929.3398099},
doi = {10.1145/3377929.3398099},
booktitle = {Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion},
pages = {1562–1570},
numpages = {9},
keywords = {symbolic regression, genetic programming, C++},
location = {Canc\'{u}n, Mexico},
series = {GECCO '20}
}
```
_Operon_ was also featured in a recent survey of symbolic regression methods, where it showed good results:
```
@article{DBLP:journals/corr/abs-2107-14351,
author = {William G. La Cava and
Patryk Orzechowski and
Bogdan Burlacu and
Fabr{\'{\i}}cio Olivetti de Fran{\c{c}}a and
Marco Virgolin and
Ying Jin and
Michael Kommenda and
Jason H. Moore},
title = {Contemporary Symbolic Regression Methods and their Relative Performance},
journal = {CoRR},
volume = {abs/2107.14351},
year = {2021},
url = {https://arxiv.org/abs/2107.14351},
eprinttype = {arXiv},
eprint = {2107.14351},
timestamp = {Tue, 03 Aug 2021 14:53:34 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2107-14351.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```