Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System. - Michael Heider; Helena Stegherr; Jonathan Wurth; Roman Sraj; Jörg Hähner (BIOMA, 2022)

Separating rule discovery and global solution composition in a learning classifier system. - Michael Heider; Helena Stegherr; Jonathan Wurth; Roman Sraj; Jörg Hähner (GECCO Companion, 2022)

Comparing different metaheuristics for model selection in a supervised learning classifier system. - Jonathan Wurth; Michael Heider; Helena Stegherr; Roman Sraj; Jörg Hähner (GECCO Companion, 2022)

Approaches for Rule Discovery in a Learning Classifier System. - Michael Heider; Helena Stegherr; David Pätzel; Roman Sraj; Jonathan Wurth; Benedikt Volger; Jörg Hähner (IJCCI, 2022)

Fast, Flexible, and Fearless: A Rust Framework for the Modular Construction of Metaheuristics. - Jonathan Wurth; Helena Stegherr; Michael Heider; Leopold Luley; Jörg Hähner (GECCO Companion, 2023)

SupRB in the context of rule-based machine learning methods: A comparative study. - Michael Heider; Helena Stegherr; Roman Sraj; David Pätzel; Jonathan Wurth; Jörg Hähner (Appl. Soft Comput., 2023)

Discovering Rules for Rule-Based Machine Learning with the Help of Novelty Search. - Michael Heider; Helena Stegherr; David Pätzel; Roman Sraj; Jonathan Wurth; Benedikt Volger; Jörg Hähner (SN Comput. Sci., 2023)

GRAHF: A Hyper-Heuristic Framework for Evolving Heterogeneous Island Model Topologies. - Jonathan Wurth; Helena Stegherr; Michael Heider; Jörg Hähner (GECCO, 2024)