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 project aims to automate parameter optimization of industrial machinery by including existing expert knowledge into advanced machine learning algorithms, such as Learning Classifier Systems, a family of evolutionary learning methods. Therefore, I am developing a new way of creating LCS models (that can be thought of as generalisations of decision trees) with the Supervised Rule-based Learning System (SupRB). 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. However, operators, supervisors, process engineers and management do all request that these predictive models offer explainability, interpretability and transparency (ergo, be somewhat understandable to a human user).

 

  • evolutionary rule-based learning (supervised and reinforcement learning)
  • unsupervised deep learning for feature extraction (e.g. Autoencoders)
  • explainable AI (XAI)
  • socio-technical assistance systems
  • extrusion-based manufacturing
  • 3D printing / additive manufacturing

Publications

2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2016

2024

Henning Cui, Michael Heider and Jörg Hähner. 2024. Positional bias does not influence Cartesian Genetic Programming with crossover. In Michael Affenzeller, Stephan M. Winkler, Anna V. Kononova, Heike Trautmann, Tea Tušar, Penousal Machado, Thomas Bäck (Eds.). Parallel Problem Solving from Nature – PPSN XVIII: 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14–18, 2024, proceedings, part I. Springer, Cham, 151-167 DOI: 10.1007/978-3-031-70055-2_10
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2023

Markus Görlich-Bucher, Michael Heider, Tobias Ciemala and Jörg Hähner. 2023. A decision-theoretic approach for prioritizing maintenance activities in organic computing systems. Lecture Notes in Computer Science 13949, 37-47. DOI: 10.1007/978-3-031-42785-5_3
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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.
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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
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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
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Neele Kemper, Michael Heider, Dirk Pietruschka and Jörg Hähner. in press. Forecasting of residential unit's heat demands: a comparison of machine learning techniques in a real-world case study. Enery Systems . DOI: 10.1007/s12667-023-00579-y
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Lukas Meitz, Michael Heider, Thorsten Schöler and Jörg Hähner. 2023. On data-preprocessing for effective predictive maintenance on multi-purpose machines. In Oleg Gusikhin, Slimane Hammoudi, Alfredo Cuzzocrea (Eds.). Proceedings of the 12th International Conference on Data Science, Technology and Applications, July 11-13, 2023, in Rome, Italy. SciTePress, Setúbal, 606-612 DOI: 10.5220/0012146700003541
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Markus Görlich-Bucher, Michael Heider and Jörg Hähner. 2023. Predicting physical disturbances in organic computing systems using automated machine learning. Lecture Notes in Computer Science 13949, 48-62. DOI: 10.1007/978-3-031-42785-5_4
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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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David Pätzel, Michael Heider and Jörg Hähner. 2023. Towards principled synthetic benchmarks for explainable rule set learning algorithms. 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, 1657-1662 DOI: 10.1145/3583133.3596416
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Tobias Wittmeir, Michael Heider, André Schweiger, Michaela Krä, Jörg Hähner, Johannes Schilp and Joachim Berlak. 2023. Towards robustness of production planning and control against supply chain disruptions. In David Herberger, Marco Hübner, Volker Stich (Eds.). Proceedings of the Conference on Production Systems and Logistics: CPSL 2023. publish-Ing., Hannover, 65-75 DOI: 10.15488/13425
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Henning Cui, Andreas Margraf, Michael Heider and Jörg Hähner. 2023. Towards understanding crossover for Cartesian Genetic Programming. In Niki van Stein, Francesco Marcelloni, H. K. Lam, Marie Cottrell, Joaquim Filipe (Eds.). Proceedings of the 15th International Joint Conference on Computational Intelligence - ECTA, November 13-15, 2023, in Rome, Italy. SciTePress, Setúbal, 308-314 DOI: 10.5220/0012231400003595
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2022

Richard Nordsieck, Michael Heider, Anton Hummel and Jörg Hähner. 2022. A closer look at sum-based embeddings for knowledge graphs containing procedural knowledge. In Mehwish Alam, Davide Buscaldi, Michael Cochez, Francesco Osborne, Diego Reforgiato Recupero (Eds.). DL4KG 2022 - Deep Learning for Knowledge Graphs 2022: Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG 2022) co-located with the 21th International Semantic Web Conference (ISWC 2022), virtual conference, online, October 24, 2022. CEUR-WS, Aachen, 7
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Michael Heider, David Pätzel and Alexander R. M. Wagner. 2022. An overview of LCS research from 2021 to 2022. Proceedings of the Genetic and Evolutionary Computation Conference Companion 2086-2094. DOI: 10.1145/3520304.3533985
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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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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
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Richard Nordsieck, Michael Heider, Alwin Hoffmann and Jörg Hähner. 2022. Reliability-based aggregation of heterogeneous knowledge to assist operators in manufacturing. 2022 IEEE 16th International Conference on Semantic Computing (ICSC) 131-138. DOI: 10.1109/icsc52841.2022.00027
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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. Proceedings of the Genetic and Evolutionary Computation Conference Companion 248-251. DOI: 10.1145/3520304.3529014
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Richard Nordsieck, Michael Heider, Anton Hummel, Alwin Hoffmann and Jörg Hähner. 2022. Towards models of conceptual and procedural operator knowledge. In Arild Waaler, Evgeny Kharlamov, Baifan Zhou, Dongzhuoran Zhou (Eds.). SemIIM 2022 - International Workshop on Semantic Industrial Information Modelling 2022: Proceedings of the First International Workshop on Semantic Industrial Information Modelling (SemIIM 2022) co-located with the 19th Extended Semantic Web Conference ESWC 2022, Greece, Crete, 30 May 2022. CEUR-WS, Aachen
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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
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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. 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
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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
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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
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Michael Heider, Richard Nordsieck and Jörg Hähner. 2021. Learning classifier systems for self-explaining socio-technical-systems. In Anthony Stein, Sven Tomforde, Jean Botev, Peter Lewis (Eds.). 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
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2020

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
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Michael Heider, David Pätzel and Jörg Hähner. 2020. SupRB: a supervised rule-based learning system for continuous problems. preprint.
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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
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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
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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
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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
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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 Organic Computing (Bachelor) winter semester 2024/25 Seminar
Studentische Arbeiten am Lehrstuhl Organic Computing winter semester 2024/25 sonstige
Seminar Organic Computing (Master) winter semester 2024/25 Seminar

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