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Jonathan Wurth
Jonathan Wurth
Universität Augsburg
uni-a.de의 이메일 확인됨
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Separating rule discovery and global solution composition in a learning classifier system
M Heider, H Stegherr, J Wurth, R Sraj, J Hähner
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2022
122022
Investigating the impact of independent rule fitnesses in a learning classifier system
M Heider, H Stegherr, J Wurth, R Sraj, J Hähner
International Conference on Bioinspired Optimization Methods and Their …, 2022
72022
Comparing different metaheuristics for model selection in a supervised learning classifier system
J Wurth, M Heider, H Stegherr, R Sraj, J Hähner
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2022
72022
Approaches for rule discovery in a learning classifier system
M Heider, H Stegherr, D Pätzel, R Sraj, J Wurth, B Volger, J Hähner
42022
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
32023
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
22023
Fast, Flexible, and Fearless: A Rust Framework for the Modular Construction of Metaheuristics
J Wurth, H Stegherr, M Heider, L Luley, J Hähner
Proceedings of the Companion Conference on Genetic and Evolutionary …, 2023
22023
A framework for modular construction and evaluation of metaheuristics
H Stegherr, L Luley, J Wurth, M Heider, J Hähner
22023
GRAHF: A Hyper-Heuristic Framework for Evolving Heterogeneous Island Model Topologies
J Wurth, H Stegherr, M Heider, J Hähner
Proceedings of the Genetic and Evolutionary Computation Conference, 1054-1063, 2024
2024
Suprb in the Context of Rule-Based Machine Learning
M Heider, H Stegherr, R Sraj, D Pätzel, J Wurth, J Hähner
Available at SSRN 4481895, 0
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학술자료 1–10