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. Jonathan Wurth

Jonathan Wurth

Wissenschaftlicher Mitarbeiter
Lehrstuhl für Organic Computing
Telefon: +49 821 598 69256
E-Mail:
Raum: 2044 (W)
Adresse: Am Technologiezentrum 8, 86159 Augsburg

Forschungsschwerpunkte

My primary research focus revolves around the automated design of metaheuristic algorithms (e.g., using hyper-heuristics). Metaheuristics are nature-inspired stochastic optimization methods that have proven to be highly effective in solving real-world problems where many exact methods are not applicable or fail to produce comparable results. Practitioners often encounter scenarios where they need to solve similar instances of optimization problems repeatedly, and metaheuristics specifically adapted to such instances have great potential over general search methods.

Automated metaheuristic design is an alternative to the complex and labor-intensive task of tailoring these algorithms by hand, and not only makes custom algorithms accessible to a much wider range of users, but also potentially outperforms human-designed algorithms by thoroughly exploring different design alternatives.
As part of my research, I am also interested in the following topics:

  • parameter control and tuning methodologies for metaheuristics
  • coevolutionary approaches
  • parallel and distributed algorithms
  • combining metaheuristics with machine learning
  • modular metaheuristic frameworks (e.g., using Rust)

 

Publikationen

2024 | 2023 | 2022

2024

Jonathan Wurth, Helena Stegherr, Michael Heider and Jörg Hähner. 2024. GRAHF: a hyper-heuristic framework for evolving heterogeneous island model topologies. In Xiaodong Li, Julia Handl (Eds.). GECCO '24: proceedings of the Genetic and Evolutionary Computation Conference, Melbourne, VIC, Australia, July 14-18, 2024. ACM, New York, NY, 1054-1063 DOI: 10.1145/3638529.3654136
BibTeX | RIS | DOI

2023

Helena Stegherr, Leopold Luley, Jonathan Wurth, Michael Heider and Jörg Hähner. 2023. A framework for modular construction and evaluation of metaheuristics. Reports / Technische Berichte der Fakultät für Angewandte Informatik der Universität Augsburg 2023-01. Institut für Informatik, Universität Augsburg, Augsburg.
PDF | BibTeX | RIS

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
PDF | BibTeX | RIS | DOI

Jonathan Wurth, Helena Stegherr, Michael Heider, Leopold Luley and Jörg Hähner. 2023. Fast, flexible, and fearless: a rust framework for the modular construction of metaheuristics. 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, 1900-1909 DOI: 10.1145/3583133.3596335
BibTeX | RIS | DOI

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

2022

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

Jonathan Wurth, Michael Heider, Helena Stegherr, Roman Sraj and Jörg Hähner. 2022. Comparing different metaheuristics for model selection in a supervised learning classifier system. Proceedings of the Genetic and Evolutionary Computation Conference Companion 316-319. DOI: 10.1145/3520304.3529015
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Michael Heider, Helena Stegherr, Jonathan Wurth, Roman Sraj and Jörg Hähner. 2022. Investigating the impact of independent rule fitnesses in a learning classifier system. Lecture Notes in Computer Science 13627, 142-156. DOI: 10.1007/978-3-031-21094-5_11
PDF | BibTeX | RIS | DOI

Michael Heider, Helena Stegherr, Jonathan Wurth, Roman Sraj and Jörg Hähner. 2022. Separating rule discovery and global solution composition in a learning classifier system. Proceedings of the Genetic and Evolutionary Computation Conference Companion 248-251. DOI: 10.1145/3520304.3529014
PDF | BibTeX | RIS | DOI

Lebenslauf

seit 2023

Wissenschaftlicher Mitarbeiter am Lehrstuhl Organic Computing der Universität Augsburg
2021–2023 Wissenschaftliche Hilfskraft am Lehrstuhl Organic Computing der Universität Augsburg
2021–2023 Master-Studium im Fach Informatik an der Universität Augsburg
2018–2021 Bachelor-Studium im Fach Informatik an der Universität Augsburg

Lehrveranstaltungen

(Angewandte Filter: Semester: aktuelles | Institutionen: Organic Computing | Dozenten: Jonathan Wurth | Vorlesungsarten: alle)
Name Semester Typ
Grundlagen des Organic Computing Wintersemester 2024/25 Vorlesung
Seminar Organic Computing (Bachelor) Wintersemester 2024/25 Seminar
Studentische Arbeiten am Lehrstuhl Organic Computing Wintersemester 2024/25 sonstige
Übung zu Grundlagen des Organic Computing Wintersemester 2024/25 Übung
Seminar Organic Computing (Master) Wintersemester 2024/25 Seminar

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