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

2023 | 2022 | 2021 | 2020 | 2019

2023

Helena Stegherr, Leopold Luley, Jonathan Wurth, Michael Heider and Jörg Hähner. 2023. A framework for modular construction and evaluation of metaheuristics.
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Michael Heider, David Pätzel, Helena Stegherr and Jörg Hähner. 2023. A metaheuristic perspective on learning classifier systems. DOI: 10.1007/978-981-19-3888-7_3
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. 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. 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. 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. 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. 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. DOI: 10.5220/0011542000003332
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Helena Stegherr, Michael Heider and Jörg Hähner. 2022. Classifying metaheuristics: towards a unified multi-level classification system. 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. 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. DOI: 10.1007/978-3-031-21094-5_11
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Michael Heider, Helena Stegherr, Jonathan Wurth, Roman Sraj and Jörg Hähner. 2022. Separating rule discovery and global solution composition in a learning classifier system. DOI: 10.1145/3520304.3529014
PDF | BibTeX | RIS | DOI

2021

Helena Stegherr and Jörg Hähner. 2021. Analysing metaheuristic components.
PDF | BibTeX | RIS | URL

Helena Stegherr, Michael Heider, Leopold Luley and Jörg Hähner. 2021. Design of large-scale metaheuristic component studies. 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. 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.
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
Studentische Arbeiten am Lehrstuhl Organic Computing winter semester 2023/24 sonstige
Seminar Organic Computing (Bachelor) winter semester 2023/24 Seminar
Seminar Organic Computing (Master) winter semester 2023/24 Seminar

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