Lehrstuhl für Organic Computing

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  2. Faculties
  3. Faculty of Applied Computer Science
  4. Institute of Computer Science
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  6. Organic Computing Group
  7. Team
  8. Helena Stegherr

Helena Stegherr

Research Assistant
Lehrstuhl für Organic Computing
Phone: +49 821 598 69260
Email:
Room: 1031 (W)
Address: Am Technologiezentrum 8, 86159 Augsburg

Research foci

My research is focussed on nature-inspired optimisation algorithms, also known as metaheuristics or evolutionary algorithms. A main part of my work is the analysis of algorithmic behaviour, especially in relation to the components causing or influencing it. This is why I examine this topic from a conceptual perspective, highlighting similarities and differences of the optimisation algorithms, as well as from an empirical perspective, performing experiments to quantify the influence of different components on the overall algorthmic behaviour.

 

In addition, I am interested in possibilities of combining metaheuristics and machine learning techniques, parallel and distributed algorithms, and methods and tools for experimental and statistical analyses of optimisation algorithms. I am also interested in applying metaheuristics to optimisation problems, especially from the field of bioinformatics.

 

Publications

2024 | 2023 | 2022 | 2021 | 2020 | 2019

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, 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
PDF | BibTeX | RIS | DOI

Michael Heider, Helena Stegherr, Richard Nordsieck and Jörg Hähner. 2023. Assessing model requirements for explainable AI: a template and exemplary case study. Artificial Life 29, 4, 468-486. DOI: 10.1162/artl_a_00414
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Helena Stegherr, Michael Heider and Jörg Hähner. 2023. Assisting convergence behaviour characterisation with unsupervised clustering. In Niki van Stein, Francesco Marcelloni, H. K. Lam, Marie Cottrell, Joaquim Filipe (Eds.). Proceedings of the 15th International Joint Conference on Computational Intelligence, November 13-15, 2023, in Rome, Italy. SciTePress, Setúbal, 108-118 DOI: 10.5220/0012202100003595
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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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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

Helena Stegherr, Michael Heider and Jörg Hähner. 2022. Classifying metaheuristics: towards a unified multi-level classification system. Natural Computing 21, 155-171. DOI: 10.1007/s11047-020-09824-0
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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

2021

Helena Stegherr and Jörg Hähner. 2021. Analysing metaheuristic components. In Anthony Stein, Sven Tomforde, Jean Botev and Peter Lewis (Ed.). LIFELIKE 2020 & LIFELIKE 2021, Lifelike Computing Systems Workshop 2020 and 2021: Joint Proceedings of the LIFELIKE 2020 - 8th Edition in the Evolution of the Workshop Series of Autonomously Learning and Optimizing Systems (SAOS) co-located with 2020 Conference on Artificial Life (ALIFE 2020), Online, July 16th, 2020, and the LIFELIKE 2021 - 9th Edition in the Evolution of the Workshop Series of Autonomously Learning and Optimizing Systems (SAOS) co-located with 2021 Conference on Artificial Life (ALIFE 2021), Online, July 19th, 2021. CEUR-WS (CEUR Workshop Proceedings ; Vol-3007)
PDF | BibTeX | RIS | URL

Helena Stegherr, Michael Heider, Leopold Luley and Jörg Hähner. 2021. Design of large-scale metaheuristic component studies. 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, 1217-1226. DOI: 10.1145/3449726.3463168
PDF | BibTeX | RIS | DOI

2020

Lukas Rosenbauer, Anthony Stein, Helena Stegherr and Jörg Hähner. 2020. Metaheuristics for the minimum set cover problem: a comparison. 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, 123-130. DOI: 10.5220/0010019901230130
PDF | BibTeX | RIS | DOI

2019

Helena Stegherr, Anthony Stein and Jörg Hähner. 2019. Parallel chemical reaction optimization for utilization in intelligent RNA prediction systems. In Carsten Trinitis, Thilo Pionteck (Eds.). Intelligent Systems Workshop, part of ARCS 2019: 32nd International Conference on Architecture of Computing Systems; workshop proceedings; May 20- 21, 2019, Technical University of Denmark, Copenhagen, Denmark. VDE Verlag, Berlin, 135-142
PDF | BibTeX | RIS | URL

Curriculum/Vitae

since 2019 Research Assistant with the chair for Organic Computing
2015–2019 Bachelor programme Computer Science at the University of Augsburg
2012–2014 Master programme Biochemistry at the University of Ulm
2009–2012 Bachelor programme Biochemistry at the University of Ulm

Courses / teaching

(applied filters: semester: current | institute: Organic Computing | lecturers: Helena Stegherr | course types: all)
name semester type
Grundlagen des Organic Computing winter semester 2024/25 Vorlesung
Seminar Organic Computing (Bachelor) winter semester 2024/25 Seminar
Studentische Arbeiten am Lehrstuhl Organic Computing winter semester 2024/25 sonstige
Übung zu Grundlagen des Organic Computing winter semester 2024/25 Übung
Seminar Organic Computing (Master) winter semester 2024/25 Seminar

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