Lehrstuhl für Organic Computing

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  8. David Pätzel

David Pätzel

Research Assistant
Lehrstuhl für Organic Computing
Phone: +49 821 598 69254
Email: david.paetzel@informatik.uni-augsburg.de
Room: 2043 (W)
Address: Am Technologiezentrum 8, 86159 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

  • reinforcement learning
  • evolutionary machine learning algorithms
  • functional programming, especially using Haskell
  • ways to incoporate a more formal (functional) view into everyday software such as operating systems (NixOS) or window managers (XMonad)
  • free software

Other academic activities

I’ve reviewed journal articles for I was one of the three elected members of the organization committee of the 23rd and 24th International Workshop on Learning Classifier Systems (IWLCS) which took place at the 2020 and 2021 Genetic and Evolutionary Computation Conference (GECCO). Since then, I’m also a program committee member and review papers submitted to the IWLCS.

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

COURSES / TEACHING

(applied filters: semester: current | lecturers: David Pätzel | course types: Übung, Seminar)
name semester type
Seminar Organic Computing (Bachelor) winter semester 2022/23 Seminar
Kommunikationssysteme - Übungsgruppe "Marconi" winter semester 2022/23 Übung
Seminar Organic Computing (Master) winter semester 2022/23 Seminar
Kommunikationssysteme - Übungsgruppe "Bell" winter semester 2022/23 Übung
Kommunikationssysteme - Übungsgruppe "Floyd" winter semester 2022/23 Übung
Übung zu Grundlagen des Organic Computing winter semester 2022/23 Übung
Kommunikationssysteme - Übungsgruppe "Metcalfe" winter semester 2022/23 Übung
Kommunikationssysteme - Übungsgruppe "Shannon" winter semester 2022/23 Übung

Publications

Pätzel, David
2022 | 2021 | 2020 | 2019 | 2018

2022

Lukas Rosenbauer, David Pätzel, Anthony Stein and Jörg Hähner. 2022. A learning classifier system for automated test case prioritization and selection. SN Computer Science 3, 5, 373. DOI: 10.1007/s42979-022-01255-1
BibTeX | RIS | DOI

Michael Heider, David Pätzel and Alexander R. M. Wagner. 2022. An overview of LCS research from 2021 to 2022. Proceedings of the Genetic and Evolutionary Computation Conference Companion 2086-2094. DOI: 10.1145/3520304.3533985
PDF | BibTeX | RIS | DOI

2021

Lukas Rosenbauer, David Pätzel, Anthony Stein and Jörg Hähner. 2021. An organic computing system for automated testing. In Christian Hochberger, Lars Bauer and Thilo Pionteck (Ed.). Architecture of computing systems. Springer International Publishing, Cham (LNTCS ; 12800), 135-149. DOI: 10.1007/978-3-030-81682-7_9
BibTeX | RIS | DOI

David Pätzel, Michael Heider and Alexander R. M. Wagner. 2021. An overview of LCS research from 2020 to 2021. In Francisco Chicano and Krzysztof Krawiec (Ed.). Proceedings of the Genetic and Evolutionary Computation Conference Companion, Lille, France, July 10 - 14, 2021. ACM, New York, NY, 1648-1656. DOI: 10.1145/3449726.3463173
PDF | BibTeX | RIS | DOI

Lukas Rosenbauer, David Pätzel, Anthony Stein and Jörg Hähner. 2021. Transfer learning for automated test case prioritization using XCSF. In Applications of Evolutionary Computation: EvoApplications 2021. Springer, Cham, 681-696 DOI: 10.1007/978-3-030-72699-7_43
BibTeX | RIS | URL | DOI

2020

David Pätzel, Anthony Stein and Masaya Nakata. 2020. An overview of LCS research from IWLCS 2019 to 2020. In Carlos Artemio Coello Coello (Ed.). Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (GECCO '20), July 2020, Cancún, Mexico. ACM, New York, NY, 1782-1788. DOI: 10.1145/3377929.3398105
BibTeX | RIS | DOI

Michael Heider, David Pätzel and Jörg Hähner. 2020. SupRB: a supervised rule-based learning system for continuous problems. preprint.
PDF | BibTeX | RIS | URL

Michael Heider, David Pätzel and Jörg Hähner. 2020. Towards a Pittsburgh-style LCS for learning manufacturing machinery parametrizations. In Carlos Artemio Coello Coello (Ed.). Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (GECCO '20), Cancún, Mexico, July 2020. ACM, New York, NY, 127-128. DOI: 10.1145/3377929.3389963
PDF | BibTeX | RIS | DOI

Lukas Rosenbauer, Anthony Stein, Roland Maier, David Pätzel and Jörg Hähner. 2020. XCS as a reinforcement learning approach to automatic test case prioritization. In Carlos Artemio Coello Coello (Ed.). Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, Cancún, Mexico, July, 2020. ACM, New York, NY, 1798-1806. DOI: 10.1145/3377929.3398128
BibTeX | RIS | DOI

Lukas Rosenbauer, Anthony Stein, David Pätzel and Jörg Hähner. 2020. XCSF for automatic test case prioritization. In Juan Julian Merelo, Jonathan Garibaldi, Christian Wagner, Thomas Bäck, Kurosh Madani and Kevin Warwick (Ed.). Proceedings of the 12th International Joint Conference on Computational Intelligence (ECTA), November 2-4, 2020. SciTePress, Setúbal, 49-58. DOI: 10.5220/0010105700490058
PDF | BibTeX | RIS | DOI

Lukas Rosenbauer, Anthony Stein, David Pätzel and Jörg Hähner. 2020. XCSF with experience replay for automatic test case prioritization. In Hussein Abbass, Carlos A. Coello Coello and Hemant Kumar Singh (Ed.). 2020 IEEE Symposium Series on Computational Intelligence (SSCI), virtual event, Canberra, Australia, 1-4 December 2020. IEEE, Piscataway, NJ, 1307-1314. DOI: 10.1109/ssci47803.2020.9308379
BibTeX | RIS | DOI

2019

David Pätzel, Anthony Stein and Jörg Hähner. 2019. A survey of formal theoretical advances regarding XCS. In Manuel López-Ibáñez, Anne Auger and Thomas Stützle (Ed.). Proceedings of the Genetic and Evolutionary Computation Conference Companion - GECCO '19, Prague, Czech Republic, July 2019. ACM Press, New York, NY, 1295-1302. DOI: 10.1145/3319619.3326848
BibTeX | RIS | DOI

2018

David Pätzel and Jörg Hähner. 2018. An algebraic description of XCS. In Hernan Aguirre and Keiki Takadama (Ed.). Proceedings of the Genetic and Evolutionary Computation Conference Companion - GECCO '18, Kyoto, Japan — July 15 - 19, 2018. ACM Press, New York, NY, 1434-1441. DOI: 10.1145/3205651.3208248
BibTeX | RIS | DOI

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