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ORCIDMichael Heider
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2020 – today
- 2024
[c29]Michael Heider
, Maximilian Krischan, Roman Sraj, Jörg Hähner:
Exploring Self-Adaptive Genetic Algorithms to Combine Compact Sets of Rules. CEC 2024: 1-8- [c28]Abubakar Siddique, Michael Heider, Muhammad Iqbal, Hiroki Shiraishi:
A Survey on Learning Classifier Systems from 2022 to 2024. GECCO Companion 2024: 1797-1806 - [c27]Jonathan Wurth, Helena Stegherr, Michael Heider, Jörg Hähner:
GRAHF: A Hyper-Heuristic Framework for Evolving Heterogeneous Island Model Topologies. GECCO 2024 - [c26]Henning Cui, Michael Heider, Jörg Hähner:
Positional Bias Does Not Influence Cartesian Genetic Programming with Crossover. PPSN (1) 2024: 151-167 - 2023
- [j4]Michael Heider, Helena Stegherr, Richard Nordsieck, Jörg Hähner:
Assessing Model Requirements for Explainable AI: A Template and Exemplary Case Study. Artif. Life 29(4): 468-486 (2023) - [j3]Michael Heider, Helena Stegherr, Roman Sraj, David Pätzel, Jonathan Wurth, Jörg Hähner:
SupRB in the context of rule-based machine learning methods: A comparative study. Appl. Soft Comput. 147: 110706 (2023) - [j2]Michael Heider, Helena Stegherr, David Pätzel, Roman Sraj, Jonathan Wurth, Benedikt Volger, Jörg Hähner:
Discovering Rules for Rule-Based Machine Learning with the Help of Novelty Search. SN Comput. Sci. 4(6): 778 (2023) - [c25]Markus Görlich-Bucher, Michael Heider, Tobias Ciemala, Jörg Hähner:
A Decision-Theoretic Approach for Prioritizing Maintenance Activities in Organic Computing Systems. ARCS 2023: 37-47 - [c24]Markus Görlich-Bucher, Michael Heider, Jörg Hähner:
Predicting Physical Disturbances in Organic Computing Systems Using Automated Machine Learning. ARCS 2023: 48-62 - [c23]Lukas Meitz, Michael Heider, Thorsten Schöler, Jörg Hähner:
On Data-Preprocessing for Effective Predictive Maintenance on Multi-Purpose Machines. DATA 2023: 606-612 - [c22]David Pätzel, Michael Heider, Jörg Hähner:
Towards Principled Synthetic Benchmarks for Explainable Rule Set Learning Algorithms. GECCO Companion 2023: 1657-1662 - [c21]Jonathan Wurth, Helena Stegherr, Michael Heider, Leopold Luley, Jörg Hähner:
Fast, Flexible, and Fearless: A Rust Framework for the Modular Construction of Metaheuristics. GECCO Companion 2023: 1900-1909 - [c20]Helena Stegherr, Michael Heider, Jörg Hähner:
Assisting Convergence Behaviour Characterisation with Unsupervised Clustering. IJCCI 2023: 108-118 - [c19]Henning Cui, Andreas Margraf, Michael Heider, Jörg Hähner:
Towards Understanding Crossover for Cartesian Genetic Programming. IJCCI 2023: 308-314 - [i5]Richard Nordsieck, André Schweizer, Michael Heider, Jörg Hähner:
PDPK: A Framework to Synthesise Process Data and Corresponding Procedural Knowledge for Manufacturing. CoRR abs/2308.08371 (2023) - 2022
- [j1]Helena Stegherr, Michael Heider, Jörg Hähner:
Classifying Metaheuristics: Towards a unified multi-level classification system. Nat. Comput. 21(2): 155-171 (2022) - [c18]Michael Heider, Helena Stegherr, Jonathan Wurth, Roman Sraj, Jörg Hähner:
Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System. BIOMA 2022: 142-156 - [c17]Michael Heider, Helena Stegherr, Jonathan Wurth, Roman Sraj, Jörg Hähner:
Separating rule discovery and global solution composition in a learning classifier system. GECCO Companion 2022: 248-251 - [c16]Jonathan Wurth, Michael Heider, Helena Stegherr, Roman Sraj, Jörg Hähner:
Comparing different metaheuristics for model selection in a supervised learning classifier system. GECCO Companion 2022: 316-319 - [c15]Michael Heider, David Pätzel, Alexander R. M. Wagner:
An overview of LCS research from 2021 to 2022. GECCO Companion 2022: 2086-2094 - [c14]Michael Heider, Helena Stegherr, David Pätzel, Roman Sraj, Jonathan Wurth, Benedikt Volger, Jörg Hähner:
Approaches for Rule Discovery in a Learning Classifier System. IJCCI 2022: 39-49 - [c13]Richard Nordsieck, Michael Heider, Alwin Hoffmann, Jörg Hähner:
Reliability-Based Aggregation of Heterogeneous Knowledge to Assist Operators in Manufacturing. ICSC 2022: 131-138 - [c12]Richard Nordsieck, Michael Heider, Anton Hummel, Alwin Hoffmann, Jörg Hähner:
Towards Models of Conceptual and Procedural Operator Knowledge. SemIIM 2022 - [c11]Richard Nordsieck, Michael Heider, Anton Hummel, Jörg Hähner:
A Closer Look at Sum-based Embeddings for Knowledge Graphs Containing Procedural Knowledge. DL4KG@ISWC 2022 - [i4]Michael Heider, Helena Stegherr, Jonathan Wurth, Roman Sraj, Jörg Hähner:
Separating Rule Discovery and Global Solution Composition in a Learning Classifier System. CoRR abs/2202.01677 (2022) - [i3]Michael Heider, Helena Stegherr, Richard Nordsieck, Jörg Hähner:
Learning Classifier Systems for Self-Explaining Socio-Technical-Systems. CoRR abs/2207.02300 (2022) - [i2]Michael Heider, Helena Stegherr, Jonathan Wurth, Roman Sraj, Jörg Hähner:
Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System. CoRR abs/2207.05582 (2022) - 2021
- [c10]Helena Stegherr, Michael Heider, Leopold Luley, Jörg Hähner:
Design of large-scale metaheuristic component studies. GECCO Companion 2021: 1217-1226 - [c9]David Pätzel, Michael Heider, Alexander R. M. Wagner:
An overview of LCS research from 2020 to 2021. GECCO Companion 2021: 1648-1656 - [c8]Andreas Wiedholz, Michael Heider, Richard Nordsieck, Andreas Angerer, Simon Dietrich, Jörg Hähner:
CAD-based Grasp and Motion Planning for Process Automation in Fused Deposition Modelling. ICINCO 2021: 450-458 - [c7]Michael Heider, Richard Nordsieck, Jörg Hähner:
Learning Classifier Systems for Self-Explaining Socio-Technical-Systems. LIFELIKE 2021 - [c6]Richard Nordsieck, Michael Heider, Anton Winschel, Jörg Hähner:
Knowledge Extraction via Decentralized Knowledge Graph Aggregation. ICSC 2021: 92-99 - 2020
- [c5]Richard Nordsieck, Michael Heider, Andreas Angerer, Jörg Hähner:
Evaluating the Effect of User-Given Guiding Attention on the Learning Process. ACSOS 2020: 215-221 - [c4]Michael Heider, David Pätzel, Jörg Hähner:
Towards a Pittsburgh-style LCS for learning manufacturing machinery parametrizations. GECCO Companion 2020: 127-128 - [i1]Michael Heider, David Pätzel, Jörg Hähner:
SupRB: A Supervised Rule-based Learning System for Continuous Problems. CoRR abs/2002.10295 (2020)
2010 – 2019
- 2019
- [c3]Michael Heider:
Increasing Reliability in FDM Manufacturing. GI-Jahrestagung (Workshops) 2019: 483-491 - [c2]Richard Nordsieck, Michael Heider, Andreas Angerer, Jörg Hähner:
Towards Automated Parameter Optimisation of Machinery by Persisting Expert Knowledge. ICINCO (1) 2019: 406-413 - 2016
- [c1]Sebastian von Mammen, Heiko Hamann, Michael Heider:
Robot gardens: an augmented reality prototype for plant-robot biohybrid systems. VRST 2016: 139-142
Coauthor Index
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last updated on 2024-10-13 18:01 CEST by the dblp team
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