David Pätzel
| Phone: | +49 821 598 4630 |
| Email: | |
| Room: | 502 (Standort "Alte Universität") |
| Address: | Eichleitnerstraße 30, 86159 Augsburg |
Curriculum vitae
| since 2017 | Research Assistant with the chair for Organic Computing |
| 2015–2017 | Master course in Computer Science at the University of Augsburg |
| 2011–2015 | Bachelor course in Computer Science and Multimedia at the University of Augsburg |
Research foci
My main research is directed towards Learning Classifier Systems (especially XCS and its derivatives), a family of versatile evolutionary rule-based machine learning algorithms. One of the main arguments for their application is that they generate models that are more easily interpreted than the models generated by other learning systems such as artificial neural networks. However, we still don't yet fully understand in a formal way how LCS work, that is, which kinds of problems LCS are able to learn exactly and why they can learn them—which weakens the argument for them by quite a bit. Through my work, I want to develop a more formal understanding of LCS and finally prove several assumptions that were, up to now, only validated experimentally.
Other than that, I'm very interested in
Academic services
Courses/teaching
| name | semester | type |
|---|---|---|
| Übung zu Grundlagen des Organic Computing | winter semester 2020/21 | Übung |
| Seminar über Ad-hoc- und Sensornetze | winter semester 2020/21 | Seminar |
| Seminar über Organic Computing | winter semester 2020/21 | Seminar |
Publications
- XCSF with experience replay for automatic test case prioritization Lukas Rosenbauer, Anthony Stein, David Pätzel and Jörg Hähner. 2020. To be published in Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence (SSCI '20).
- XCSF for automatic test case prioritization Lukas Rosenbauer, Anthony Stein, David Pätzel and Jörg Hähner. 2020. To be published in Proceedings of the 11th International Joint Conference on Computational Intelligence (ECTA '20).
- Towards a Pittsburgh-style LCS for learning manufacturing machinery parametrizations Michael Heider, David Pätzel and Jörg Hähner. 2020. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (GECCO '20). ACM, New York, NY, USA, 127–128. DOI: https://doi.org/10.1145/3377929.3389963 (PDF)
- XCS as a reinforcement learning approach to automatic test case prioritization Lukas Rosenbauer, Anthony Stein, Roland Maier, David Pätzel and Jörg Hähner. 2020. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (GECCO '20). ACM, New York, NY, USA, 1798–1806. DOI: https://doi.org/10.1145/3377929.3398128 (PDF)
- An overview of LCS research from IWLCS 2019 to 2020 David Pätzel, Anthony Stein and Masaya Nakata. 2020. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (GECCO '20). ACM, New York, NY, USA, 1782–1788. DOI: https://doi.org/10.1145/3377929.3398105 (PDF)
- A survey of formal theoretical advances regarding XCS. David Pätzel, Anthony Stein and Jörg Hähner. 2019. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '19), Manuel López-Ibáñez (Ed.). ACM, New York, NY, USA, 1295–1302. DOI: https://doi.org/10.1145/3319619.3326848 (PDF)
- An algebraic description of XCS. David Pätzel and Jörg Hähner. 2018. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '18), Hernan Aguirre (Ed.). ACM, New York, NY, USA, 1434–1441. DOI: https://doi.org/10.1145/3205651.3208248 (PDF)