An open API service indexing awesome lists of open source software.

https://github.com/simonusher/p4

This project is a successful attempt at making the P3 optimization method work with scheduling problems. I created it as a part of my bachelor's thesis. Afterwards it was used in research that led to a paper published at PPSN 2020 conference. The paper is available here: https://link.springer.com/chapter/10.1007/978-3-030-58112-1_29
https://github.com/simonusher/p4

artificial-intelligence evolutionary-algorithms evolutionary-computation linkage-learning

Last synced: 12 months ago
JSON representation

This project is a successful attempt at making the P3 optimization method work with scheduling problems. I created it as a part of my bachelor's thesis. Afterwards it was used in research that led to a paper published at PPSN 2020 conference. The paper is available here: https://link.springer.com/chapter/10.1007/978-3-030-58112-1_29

Awesome Lists containing this project

README

          

[![Contributors][contributors-shield]][contributors-url]
[![Forks][forks-shield]][forks-url]
[![Stargazers][stars-shield]][stars-url]
[![Issues][issues-shield]][issues-url]
[![MIT License][license-shield]][license-url]
[![LinkedIn][linkedin-shield]][linkedin-url]




P4 - Parameter-less Population Pyramid for Permutations


This project is a successful attempt at making the P3 optimization method work with scheduling problems. I created it as a part of my bachelor's thesis. Afterwards it was used in research that led to a paper published at PPSN 2020 conference. The paper is available here.


Explore the docs »




Report Bug
·
Request Feature

Table of Contents




  1. About The Project


  2. License

  3. Contact

  4. Acknowledgements

## About The Project

This project contains a modified version of [Parameter-less Population Pyramid (P3)](https://dl.acm.org/doi/10.1145/2576768.2598350) by Brian W. Goldman et al. The adaptations I used were originally proposed for another linkage-learning method, namely GOMEA, by Bosman et al. in [Expanding from Discrete Cartesian to Permutation Gene-pool Optimal Mixing Evolutionary Algorithms](https://dl.acm.org/doi/10.1145/2908812.2908917).

I created it as a part of my bachelor's thesis. Afterwards it was used in research that led to a paper published at PPSN 2020 conference. The paper is available here: https://link.springer.com/chapter/10.1007/978-3-030-58112-1_29

The code includes a simple UI in Qt, but the core optimizer parts are located in src/optimizer, src/local_optimizers, and src/problem.

## Research
**This is not the exact code used in P4 paper.** If you wish to use P4 code in your research, please reach out to me or one of the other authors of the paper.

### Built With

* C++,
* [Qt](https://www.qt.io/),
* [Visual Studio 2019](https://visualstudio.microsoft.com/pl/).

## License

Distributed under the GNU GPLv3 License. See `COPYING` for more information.

## Contact

Szymon Woźniak - swozniak6@gmail.com

Project Link: [https://github.com/simonusher/p4](https://github.com/simonusher/p4)

[contributors-shield]: https://img.shields.io/github/contributors/simonusher/p4.svg?style=for-the-badge
[contributors-url]: https://github.com/simonusher/p4/graphs/contributors
[forks-shield]: https://img.shields.io/github/forks/simonusher/p4.svg?style=for-the-badge
[forks-url]: https://github.com/simonusher/p4/network/members
[stars-shield]: https://img.shields.io/github/stars/simonusher/p4.svg?style=for-the-badge
[stars-url]: https://github.com/simonusher/p4/stargazers
[issues-shield]: https://img.shields.io/github/issues/simonusher/p4.svg?style=for-the-badge
[issues-url]: https://github.com/simonusher/p4/issues
[license-shield]: https://img.shields.io/github/license/simonusher/p4.svg?style=for-the-badge
[license-url]: https://github.com/simonusher/p4/blob/master/COPYING
[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge&logo=linkedin&colorB=555
[linkedin-url]: https://www.linkedin.com/in/szymon-wo%C5%BAniak-00505318a/
[p4-paper]: https://link.springer.com/chapter/10.1007/978-3-030-58112-1_29