| A survey of formal theoretical advances regarding XCS D Pätzel, A Stein, J Hähner Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2019 | 23 | 2019 |
| XCS as a reinforcement learning approach to automatic test case prioritization L Rosenbauer, A Stein, R Maier, D Pätzel, J Hähner Proceedings of the 2020 Genetic and Evolutionary Computation Conference …, 2020 | 20 | 2020 |
| XCSF with Experience Replay for Automatic Test Case Prioritization L Rosenbauer, A Stein, D Pätzel, J Hähner 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 1307-1314, 2020 | 19 | 2020 |
| An overview of LCS research from IWLCS 2019 to 2020 D Pätzel, A Stein, M Nakata Proceedings of the 2020 Genetic and Evolutionary Computation Conference …, 2020 | 16 | 2020 |
| A Metaheuristic Perspective on Learning Classifier Systems M Heider, D Pätzel, H Stegherr, J Hähner Metaheuristics for Machine Learning: New Advances and Tools, 73-98, 2022 | 12 | 2022 |
| Transfer learning for automated test case prioritization using XCSF L Rosenbauer, D Pätzel, A Stein, J Hähner Applications of Evolutionary Computation: 24th International Conference …, 2021 | 12 | 2021 |
| An overview of LCS research from 2020 to 2021 D Pätzel, M Heider, ARM Wagner Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021 | 9 | 2021 |
| Towards a Pittsburgh-style LCS for learning manufacturing machinery parametrizations M Heider, D Pätzel, J Hähner Proceedings of the 2020 Genetic and Evolutionary Computation Conference …, 2020 | 7 | 2020 |
| An algebraic description of XCS D Pätzel, J Hähner Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2018 | 7 | 2018 |
| An overview of LCS research from 2021 to 2022 M Heider, D Pätzel, ARM Wagner Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2022 | 6 | 2022 |
| XCSF for automatic test case prioritization L Rosenbauer, A Stein, D Pätzel, J Hähner | 6 | 2020 |
| Deep Q-Network Updates for the Full Action-Space Utilizing Synthetic Experiences WP von Pilchau, D Pätzel, A Stein, J Hähner 2023 International Joint Conference on Neural Networks (IJCNN), 1-9, 2023 | 4 | 2023 |
| Approaches for Rule Discovery in a Learning Classifier System M Heider, H Stegherr, D Pätzel, R Sraj, J Wurth, B Volger, J Hähner 14th International Joint Conference on Computational Intelligence - ECTA, 39–49, 2022 | 4 | 2022 |
| Discovering Rules for Rule-Based Machine Learning with the Help of Novelty Search M Heider, H Stegherr, D Pätzel, R Sraj, J Wurth, B Volger, J Hähner SN Computer Science 4 (6), 778, 2023 | 3 | 2023 |
| Weighted Mutation of Connections To Mitigate Search Space Limitations in Cartesian Genetic Programming H Cui, D Pätzel, A Margraf, J Hähner Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic …, 2023 | 3 | 2023 |
| Towards Principled Synthetic Benchmarks for Explainable Rule Set Learning Algorithms D Pätzel, M Heider, J Hähner Proceedings of the Companion Conference on Genetic and Evolutionary …, 2023 | 3 | 2023 |
| A learning classifier system for automated test case prioritization and selection L Rosenbauer, D Pätzel, A Stein, J Hähner SN Computer Science 3 (5), 373, 2022 | 3 | 2022 |
| An organic computing system for automated testing L Rosenbauer, D Pätzel, A Stein, J Hähner Architecture of Computing Systems: 34th International Conference, ARCS 2021 …, 2021 | 3 | 2021 |
| SupRB in the context of rule-based machine learning methods: A comparative study M Heider, H Stegherr, R Sraj, D Pätzel, J Wurth, J Hähner Applied Soft Computing 147, 110706, 2023 | 2 | 2023 |
| The Bayesian learning classifier system: implementation, replicability, comparison with XCSF D Pätzel, J Hähner Proceedings of the Genetic and Evolutionary Computation Conference, 413-421, 2022 | 2 | 2022 |
Jörg HähnerUniversität AugsburgVerified email at informatik.uni-augsburg.de