Lehrstuhl für Organic Computing

  1. Universität
  2. Fakultäten
  3. Fakultät für Angewandte Informatik
  4. Institut für Informatik
  5. Lehrstühle und Professuren der Informatik
  6. Organic Computing
  7. Team
  8. David Pätzel

David Pätzel

Wissenschaftlicher Mitarbeiter
Lehrstuhl für Organic Computing
Telefon: +49 821 598 69254
E-Mail:
Raum: 2043 (W)
Sprechzeiten: nach Vereinbarung
Adresse: Am Technologiezentrum 8, 86159 Augsburg

Forschungsschwerpunkte

My main research is directed towards metaheuristic Rule Set Learning algorithms (MRSLs) for regression problems. MRSLs are machine learning algorithms that build sets of if-then rules using metaheuristics such as evolutionary algorithms. One of the main arguments for applying MRSLs is that they generate models that are more easily interpreted than the models generated by other learning systems such as neural networks.

While there are several such algorithms (for example, XCSF from the MRSL subgroup of Learning Classifier Systems), it is not well-understood how they work or even when they work well. Even more so, it is not even clear whether the metaheuristics (and their operators) that they build upon are anywhere near optimal for the task of searching rule set space.

With my current my work I'm looking into exactly that: For one, I'm developing novel ways to benchmark these systems such that fresh insights into rule-set-searching metaheuristics can be gained. Further, I'm looking into applying metaheuristic operators to these tasks that have not yet been applied to them.

Other than that, I'm very interested in

  • evolutionary machine learning algorithms
  • reinforcement learning
  • 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

Sonstige wissenschaftliche Tätigkeiten

Ich habe Reviews für Artikel folgender Journals verfasst: Daneben war ich Teil des gewählten dreiköpfigen Organisationskomitees des 23., 24., 25. und 26. International Workshop Evolutionary Rule-based Machine Learning (ERBML) (ehemals International Workshop on Learning Classifier Systems, IWLCS), der im Rahmen der Genetic and Evolutionary Computation Conference (GECCO) in den Jahren 2020 bis 2023 stattfand. Seitdem bin ich auch Reviewer für auf dem ERBML eingereichte Papiere.

Lebenslauf

seit 2017 Wissenschaftlicher Mitarbeiter am Lehrstuhl
2015–2017 Master-Studium im Fach Informatik an der Universität Augsburg
2011–2015 Bachelor-Studium im Fach Informatik und Multimedia an der Universität Augsburg

Lehrveranstaltungen

Keine Vorlesungen vorhanden.


Publikationen

2023 | 2022 | 2021 | 2020 | 2019 | 2018

2023

Michael Heider, David Pätzel, Helena Stegherr and Jörg Hähner. 2023. A metaheuristic perspective on learning classifier systems. In Mansour Eddaly, Bassem Jarboui and Patrick Siarry (Ed.). Metaheuristics for machine learning: new advances and tools. Springer, Singapore (Computational Intelligence Methods and Applications (CIMA)), 73-98. DOI: 10.1007/978-981-19-3888-7_3
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Michael Heider, Helena Stegherr, David Pätzel, Roman Sraj, Jonathan Wurth, Benedikt Volger and Jörg Hähner. 2023. Discovering rules for rule-based machine learning with the help of novelty search. SN Computer Science 4, 6, 778. DOI: 10.1007/s42979-023-02198-x
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Michael Heider, Helena Stegherr, Roman Sraj, David Pätzel, Jonathan Wurth and Jörg Hähner. 2023. SupRB in the context of rule-based machine learning methods: a comparative study. Applied Soft Computing 147, 110706. DOI: 10.1016/j.asoc.2023.110706
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David Pätzel, Michael Heider and Jörg Hähner. 2023. Towards principled synthetic benchmarks for explainable rule set learning algorithms. In Sara Silva, Luís Paquete (Eds.). GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, Lisbon, Portugal, July 15-19, 2023. ACM, New York, NY, 1657-1662 DOI: 10.1145/3583133.3596416
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Henning Cui, David Pätzel, Andreas Margraf and Jörg Hähner. 2023. Weighted mutation of connections to mitigate search space limitations in Cartesian Genetic Programming. In FOGA '23: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 30 August - 1 September 2023, Potsdam, Germany. Association for Computing Machinery, New York, NY, 50-60 DOI: 10.1145/3594805.3607130
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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
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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
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Michael Heider, Helena Stegherr, David Pätzel, Roman Sraj, Jonathan Wurth, Benedikt Volger and Jörg Hähner. 2022. Approaches for rule discovery in a learning classifier system. In Thomas Bäck, Bas van Stein, Christian Wagner, Jonathan Garibaldi, H. K. Lam, Marie Cottrell, Faiyaz Doctor, Joaquim Filipe, Kevin Warwick, Janusz Kacprzyk (Eds.). Proceedings of the 14th International Joint Conference on Computational Intelligence, October 24-26, 2022, in Valletta, Malta. SciTePress, Setúbal, 39-49 DOI: 10.5220/0011542000003332
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David Pätzel and Jörg Hähner. 2022. The Bayesian learning classifier system: implementation, replicability, comparison with XCSF. In Jonathan E. Fieldsend, Markus Wagner (Eds.). GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference, July 9-13, 2022, Boston, MA, USA. ACM, New York, NY, 413-421 DOI: 10.1145/3512290.3528736
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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
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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
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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
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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
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Michael Heider, David Pätzel and Jörg Hähner. 2020. SupRB: a supervised rule-based learning system for continuous problems. preprint.
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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
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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
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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
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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
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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
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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
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