Lehrstuhl für Organic Computing

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  8. Michael Heider

Michael Heider

Research Assistant
Lehrstuhl für Organic Computing
Phone: +49 821 598 69261
Email:
Room: 1031 (W)
Address: Am Technologiezentrum 8, 86159 Augsburg

Research foci

My main research is to automate parameter optimisation of industrial machinery by including existing expert knowledge into advanced machine learning algorithms, such as Learning Classifier Systems, a family of evolutionary learning methods. In our case study we try to implement our concepts in the context of plastic extrusion manufacturing. An important role in finding optimal parameter settings plays predicting the quality, that results from applying a given setting.

 

  • evolutionary rule-based learning (supervised and reinforcement learning)
  • unsupervised deep learning for feature extraction
  • socio-technical assistance systems
  • extrusion-based manufacturing
  • 3D printing / additive manufacturing

Publications

Michael Heider
2021 | 2020 | 2019 | 2016

2021

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
PDF | BibTeX | RIS | DOI

Andreas Wiedholz, Michael Heider, Richard Nordsieck, Andreas Angerer, Simon Dietrich and Jörg Hähner. 2021. CAD-based grasp and motion planning for process automation in fused deposition modelling. In Oleg Gusikhin, Henk Nijmeijer and Kurosh Madani (Ed.). Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics, July 6-8, 2021. SciTePress, Setúbal, 450-458. DOI: 10.5220/0010571204500458
PDF | BibTeX | RIS | DOI

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

Richard Nordsieck, Michael Heider, Anton Winschel and Jörg Hähner. 2021. Knowledge extraction via decentralized knowledge graph aggregation. In Dick Bulterman, Atsushi Kitazawa, David Ostrowski, Phillip Sheu and Jeffrey Tsai (Ed.). 2021 IEEE 15th International Conference on Semantic Computing (ICSC), 27-29 January 2021, Laguna Hills, CA, USA. IEEE, Piscataway, NJ, 92-99. DOI: 10.1109/icsc50631.2021.00024
PDF | BibTeX | RIS | DOI

Michael Heider, Richard Nordsieck and Jörg Hähner. 2021. Learning classifier systems for self-explaining socio-technical-systems. 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

2020

Helena Stegherr, Michael Heider and Jörg Hähner. in press. Classifying metaheuristics: towards a unified multi-level classification system. Natural Computing . DOI: 10.1007/s11047-020-09824-0
PDF | BibTeX | RIS | DOI

Richard Nordsieck, Michael Heider, Andreas Angerer and Jörg Hähner. 2020. Evaluating the effect of user-given guiding attention on the learning process. In Esam El-Araby, Sven Tomforde, Timothy Wood, Pradeep Kumar, Claudia Raibulet, Ioan Petri, Gabriele Valentini, Phyllis Nelson and Barry Porter (Ed.). 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS), 17-21 August 2020, Washington, DC, USA. IEEE, Piscataway, NJ, 215-221. DOI: 10.1109/acsos49614.2020.00044
PDF | BibTeX | RIS | DOI

Michael Heider, David Pätzel and Jörg Hähner. 2020. SupRB: a supervised rule-based learning system for continuous problems. preprint.
PDF | 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
PDF | BibTeX | RIS | DOI

2019

Michael Heider. 2019. Increasing reliability in FDM manufacturing. In C. Draude, M. Lange and B. Sick (Ed.). Informatik 2019: 50 Jahre Gesellschaft für Informatik - Informatik für Gesellschaft (Workshop-Beiträge). Gesellschaft für Informatik e.V., Bonn, 483-491. DOI: 10.18420/inf2019_ws52
PDF | BibTeX | RIS | DOI

Richard Nordsieck, Michael Heider, Andreas Angerer and Jörg Hähner. 2019. Towards automated parameter optimisation of machinery by persisting expert knowledge. In Oleg Gusikhin, Kurosh Madani and Janan Zaytoon (Ed.). Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO), July 29-31, 2019, Prague, Czech Republic. SciTePress, Setúbal, 406-413. DOI: 10.5220/0007953204060413
PDF | BibTeX | RIS | DOI

2016

Sebastian von Mammen, Heiko Hamann and Michael Heider. 2016. Robot gardens: an augmented reality prototype for plant-robot biohybrid systems. In Dieter Kranzlmüller and Gudrun Klinker (Ed.). Proceedings of the 22nd ACM Conference on Virtual Reality Software and Technology, VRST '16, Munich, Germany, November 2016. ACM, New York, NY, 139-142. DOI: 10.1145/2993369.2993400
BibTeX | RIS | DOI

Curriculum/Vitae

since 2019 Research Assistant with the chair for Organic Computing
2015–2018 Master programme Computer Science and Information-oriented Business Management at the University of Augsburg
2012–2016 Bachelor programme Computer Science at the University of Augsburg

Courses / teaching

(applied filters: semester: current | institute: Organic Computing | lecturers: Michael Heider | course types: all)
name semester type
Seminar über Ad-hoc- und Sensornetze winter semester 2021/22 Seminar
Studentische Arbeiten am Lehrstuhl Organic Computing winter semester 2021/22 sonstige
Seminar über Organic Computing winter semester 2021/22 Seminar

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