EvoLP.jl is a playground for evolutionary computation in Julia. 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.

• 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.

You can install EvoLP.jl from the REPL using the built-in package manager:

julia> import Pkg
julia> Pkg.add("EvoLP")

Alternatively, you can enter Pkg mode by pressing the ] key and then add EvoLP like so:

julia> ] # upon typing ], the prompt changes (in place) to: pkg>
pkg> add EvoLP

• Read the documentation.
• Browse some of the examples to see how to use the built-in algorithms.
• For a more comprehensive tutorial, read the 8-queens problem where we construct an algorithm from scratch.

Please report any issues via the GitHub issues tracker.

If you find EvoLP.jl useful in your work or research, we kindly request that you cite the following paper:

@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}
}

Xavier F. C. Sánchez-Díaz 💬 📖 👀 📢

Jørgen Aleksander Fagervik 💬 📖 👀 📢

EvoLP.jl started as a toolbox for internal use by PhD students of NTNU's Open AI Lab, and whose funding is provided by Project no. 311284 by The Research Council of Norway. EvoLP is licensed under the MIT License which makes it free and open source.