https://github.com/ntnu-ai-lab/evolp.jl
A playground for evolutionary computation in Julia
https://github.com/ntnu-ai-lab/evolp.jl
artificial-intelligence evolutionary-computation genetic-algorithms julia optimisation optimization pso-algorithm
Last synced: 2 months ago
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A playground for evolutionary computation in Julia
- Host: GitHub
- URL: https://github.com/ntnu-ai-lab/evolp.jl
- Owner: ntnu-ai-lab
- License: mit
- Created: 2022-07-21T14:49:55.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-06-17T01:04:07.000Z (11 months ago)
- Last Synced: 2025-03-17T11:16:57.761Z (2 months ago)
- Topics: artificial-intelligence, evolutionary-computation, genetic-algorithms, julia, optimisation, optimization, pso-algorithm
- Language: Julia
- Homepage: https://ntnu-ai-lab.github.io/EvoLP.jl/
- Size: 971 KB
- Stars: 26
- Watchers: 1
- Forks: 1
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
[](https://ntnu-ai-lab.github.io/EvoLP.jl/)
[](https://julialang.org)
[](https://github.com/ntnu-ai-lab/EvoLP/blob/main/LICENSE)
[](#contributors-)
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---
**EvoLP.jl** is a _playground_ for [evolutionary computation](https://en.wikipedia.org/wiki/Evolutionary_computation) in [Julia](https://julialang.org). It provides a set of predefined _building blocks_ that can be coupled together to _play around_: quickly generate evolutionary computation solvers and compute statistics for a variety of optimisation tasks, including discrete, continuous and combinatorial optimisation.
## Features
- Random population generators (vectors and particles)
- Parent selection operators
- Several crossover and mutation methods
- Test functions for benchmarking
- Convenient result reporting and a statistics logbook
Combine these blocks to make your own algorithms or use some of the included minimisers: GA, 1+1EA and PSO.
Additionally, you can extend EvoLP to create new operators.
## Installation
You can install EvoLP.jl from the REPL using the built-in package manager:
```julia
julia> import Pkg
julia> Pkg.add("EvoLP")
```
Alternatively, you can enter `Pkg` mode by pressing the `]` key and then add EvoLP like so:
```text
julia> ] # upon typing ], the prompt changes (in place) to: pkg>
pkg> add EvoLP
```
## Getting started
- Read the **[documentation](https://ntnu-ai-lab.github.io/EvoLP.jl/)**.
- Browse some of the **[examples](https://github.com/ntnu-ai-lab/EvoLP/tree/main/examples/)** to see how to use the built-in algorithms.
- For a more comprehensive tutorial, read [the 8-queens problem](/examples/ga_k_queens.ipynb) where we construct an algorithm from scratch.
## Bug Reports
Please report any issues via the GitHub [issues tracker](https://github.com/ntnu-ai-lab/EvoLP/issues).
## Citing EvoLP.jl
If you find EvoLP.jl useful in your work or research, we kindly request that you cite the following [paper](https://ceur-ws.org/Vol-3431/paper7.pdf):
```bibtex
@inproceedings{Sanchez-DiazEvoLP2023a,
address = {Bergen, NO},
author = {Sánchez-Díaz, Xavier F. C. and Mengshoel, Ole Jakob},
booktitle = {Proceedings of the 5th Symposium of the Norwegian AI Society},
editor = {Galimullin, Rustam and Touileb, Samia},
month = jun,
publisher = {CEUR Workshop Proceedings},
series = {NAIS 2023: Symposium of the Norwegian AI Society 2023},
title = {{EvoLP.jl: A Playground for Evolutionary Computation in Julia}},
url = {https://ceur-ws.org/Vol-3431/},
year = {2023}
}
```
## Contributors
Xavier F. C. Sánchez-Díaz
💬
📖
👀
📢
Jørgen Aleksander Fagervik
💬
📖
👀
📢
## Acknowledgements
EvoLP.jl started as a toolbox for internal use by PhD students of [NTNU's Open AI Lab](https://www.ntnu.edu/ailab/ai-lab), and whose funding is provided by [Project no. 311284](https://prosjektbanken.forskningsradet.no/en/project/FORISS/311284) by [The Research Council of Norway](https://www.forskningsradet.no/). EvoLP is licensed under the [MIT License](https://github.com/ntnu-ai-lab/EvoLP/blob/main/LICENSE) which makes it **free and open source**.