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:
Room: 2043 (W)
Address: Am Technologiezentrum 8, 86159 Augsburg

RESEARCH FOCI

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 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

Other academic activities

I’ve reviewed journal articles for I was one of the three elected members of the organization committee of the 23rd, 24th, 25th and 26th International Workshop on Evolutionary Rule-based Machine Learning (ERBML) (formerly International Workshop on Learning Classifier Systems, IWLCS) which took place at the Genetic and Evolutionary Computation Conference (GECCO) between 2020 and 2023. Since then, I’m also a program committee member and review papers submitted to the ERBML.

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 (Master) summer semester 2024 Seminar
Übung zu Organic Computing II summer semester 2024 Übung
Seminar Organic Computing (Bachelor) summer semester 2024 Seminar

Publications

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
BibTeX | RIS | DOI

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
BibTeX | RIS | DOI

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
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

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
PDF | BibTeX | RIS | DOI

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
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
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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
BibTeX | RIS | DOI | URL

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.
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
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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
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
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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
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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