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 (F)

Forschungsschwerpunkte

My research focus is on the automated design of metaheuristic algorithms (AAD) for complex continuous optimization problems. Metaheuristics are nature-inspired stochastic optimization methods highly effective for problems where exact methods fail or are inapplicable. When practitioners need to repeatedly solve similar optimization instances, automatically designed custom algorithms can significantly outperform generic approaches.
I'm especially interested metaheuristics with good anytime performance (that can be interrupted at any time and still produce good solutions), and the unique challenges of real-world optimization problems that often combine multiple difficult characteristics: high dimensionality, expensive evaluations, multiple objectives, or noise. 
Key research areas and interests include:

  • Anytime performance
  • Hyperparameter-tuning and algorithm configuration
  • (Parallel) algorithm portfolios and island models
  • Integration of machine learning techniques with metaheuristics
  • Bayesian optimization
  • Modular metaheuristic frameworks

 

Publikationen

2025 | 2024 | 2023 | 2022

2025

Helena Stegherr, Michael Heider, Jonathan Wurth and Jörg Hähner. 2025. A comparison of dimensionality reduction techniques for visualising search behaviour. In Gabriela Ochoa, Bogdan Filipič (Eds.). GECCO '25 Companion: proceedings of the Genetic and Evolutionary Computation Conference Companion, 14-18 July 2025, Malaga, Spain. Association for Computing Machinery (ACM), New York, NY, 171-174 DOI: 10.1145/3712255.3726648
BibTeX | RIS | DOI

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
PDF | 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
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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
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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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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
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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

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