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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

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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.
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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) 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.

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.

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License

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

Contact

Szymon Woźniak - [email protected]

Project Link: https://github.com/simonusher/p4

About

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

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