An algebraic description of XCS. - David Pätzel; Jörg Hähner (GECCO (Companion), 2018)

A survey of formal theoretical advances regarding XCS. - David Pätzel; Anthony Stein; Jörg Hähner (GECCO (Companion), 2019)

Towards a Pittsburgh-style LCS for learning manufacturing machinery parametrizations. - Michael Heider; David Pätzel; Jörg Hähner (GECCO Companion, 2020)

An overview of LCS research from IWLCS 2019 to 2020. - David Pätzel; Anthony Stein; Masaya Nakata (GECCO Companion, 2020)

XCS as a reinforcement learning approach to automatic test case prioritization. - Lukas Rosenbauer; Anthony Stein; Roland Maier; David Pätzel; Jörg Hähner (GECCO Companion, 2020)

XCSF for Automatic Test Case Prioritization. - Lukas Rosenbauer; Anthony Stein; David Pätzel; Jörg Hähner (IJCCI, 2020)

XCSF with Experience Replay for Automatic Test Case Prioritization. - Lukas Rosenbauer; Anthony Stein; David Pätzel; Jörg Hähner (SSCI, 2020)

SupRB: A Supervised Rule-based Learning System for Continuous Problems. - Michael Heider; David Pätzel; Jörg Hähner (CoRR, 2020)

An Organic Computing System for Automated Testing. - Lukas Rosenbauer; David Pätzel; Anthony Stein; Jörg Hähner (ARCS, 2021)

Transfer Learning for Automated Test Case Prioritization Using XCSF. - Lukas Rosenbauer; David Pätzel; Anthony Stein; Jörg Hähner (EvoApplications, 2021)

An overview of LCS research from 2020 to 2021. - David Pätzel; Michael Heider; Alexander R. M. Wagner (GECCO Companion, 2021)

An overview of LCS research from 2021 to 2022. - Michael Heider; David Pätzel; Alexander R. M. Wagner (GECCO Companion, 2022)

The Bayesian learning classifier system: implementation, replicability, comparison with XCSF. - David Pätzel; Jörg Hähner (GECCO, 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)

A Learning Classifier System for Automated Test Case Prioritization and Selection. - Lukas Rosenbauer; David Pätzel; Anthony Stein; Jörg Hähner (SN Comput. Sci., 2022)

Correction: A Learning Classifier System for Automated Test Case Prioritization and Selection. - Lukas Rosenbauer; David Pätzel; Anthony Stein; Jörg Hähner (SN Comput. Sci., 2022)

Weighted Mutation of Connections To Mitigate Search Space Limitations in Cartesian Genetic Programming. - Henning Cui; David Pätzel; Andreas Margraf; Jörg Hähner (FOGA, 2023)

Towards Principled Synthetic Benchmarks for Explainable Rule Set Learning Algorithms. - David Pätzel; Michael Heider; Jörg Hähner (GECCO Companion, 2023)

Deep Q-Network Updates for the Full Action-Space Utilizing Synthetic Experiences. - Wenzel Pilar von Pilchau; David Pätzel; Anthony Stein; Jörg Hähner (IJCNN, 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)

Measuring Similarities in Model Structure of Metaheuristic Rule Set Learners. - David Pätzel; Richard Nordsieck; Jörg Hähner (EvoApplications@EvoStar, 2024)

Length-niching Selection and Spatial Crossover in Variable-length Evolutionary Rule Set Learning. - David Pätzel; Richard Nordsieck; Jörg Hähner (GECCO Companion, 2024)

A Closer Look at Length-niching Selection and Spatial Crossover in Variable-length Evolutionary Rule Set Learning. - David Pätzel; Richard Nordsieck; Jörg Hähner (GECCO Companion, 2024)