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


Michael Heider
2021 | 2020 | 2019 | 2016


David Pätzel, Michael Heider and Alexander R. M. Wagner. 2021. An overview of LCS research from 2020 to 2021. DOI: 10.1145/3449726.3463173
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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. DOI: 10.5220/0010571204500458
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Helena Stegherr, Michael Heider, Leopold Luley and Jörg Hähner. 2021. Design of large-scale metaheuristic component studies. DOI: 10.1145/3449726.3463168
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Richard Nordsieck, Michael Heider, Anton Winschel and Jörg Hähner. 2021. Knowledge extraction via decentralized knowledge graph aggregation. DOI: 10.1109/icsc50631.2021.00024
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Michael Heider, Richard Nordsieck and Jörg Hähner. 2021. Learning classifier systems for self-explaining socio-technical-systems.
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Helena Stegherr, Michael Heider and Jörg Hähner. in press. Classifying metaheuristics: towards a unified multi-level classification system. DOI: 10.1007/s11047-020-09824-0
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Richard Nordsieck, Michael Heider, Andreas Angerer and Jörg Hähner. 2020. Evaluating the effect of user-given guiding attention on the learning process. DOI: 10.1109/acsos49614.2020.00044
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Michael Heider, David Pätzel and Jörg Hähner. 2020. SupRB: a supervised rule-based learning system for continuous problems.
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Michael Heider, David Pätzel and Jörg Hähner. 2020. Towards a Pittsburgh-style LCS for learning manufacturing machinery parametrizations. DOI: 10.1145/3377929.3389963
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Michael Heider. 2019. Increasing reliability in FDM manufacturing. DOI: 10.18420/inf2019_ws52
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Richard Nordsieck, Michael Heider, Andreas Angerer and Jörg Hähner. 2019. Towards automated parameter optimisation of machinery by persisting expert knowledge. DOI: 10.5220/0007953204060413
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Sebastian von Mammen, Heiko Hamann and Michael Heider. 2016. Robot gardens: an augmented reality prototype for plant-robot biohybrid systems. DOI: 10.1145/2993369.2993400
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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)