SupRB is a rule-set learning algorithm (or Learning Classifier System (LCS)) that utilises two separate optimizers that discover new rules and create global solutions to the learning problem, respectively. Those rules are stored in a Pool that expands during runtime and provides rules for solution creation.
Rules use interval bounds for matching and a simple local model for outputs.
Solutions of the problem select classifiers from the unchanging pool.
Michael Heider, Helena Stegherr, Jonathan Wurth, Roman Sraj, and Jörg Hähner. 2022. Separating Rule Discovery and Global Solution Composition in a Learning Classifier System. In Genetic and Evolutionary Computation Conference Companion (GECCO ’22 Companion). https://doi.org/10.1145/3520304.3529014
Michael Heider, Helena Stegherr, Jonathan Wurth, Roman Sraj, and Jörg Hähner. 2022. Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System. In: Mernik, M., Eftimov, T., Črepinšek, M. (eds) Bioinspired Optimization Methods and Their Applications. BIOMA 2022. Lecture Notes in Computer Science, vol 13627. Springer, Cham. https://doi.org/10.1007/978-3-031-21094-5_11 http://arxiv.org/abs/2207.05582 A comparison with XCSF.
Michael Heider, Helena Stegherr, Roman Sraj, David Pätzel, Jonathan Wurth and Jörg Hähner. 2023. SupRB in the context of rule-based machine learning methods: a comparative study. Applied Soft Computing 147, 110706. DOI: 10.1016/j.asoc.2023.110706 A comparison with XCSF, Decision Trees, and Random Forests.
Jonathan Wurth, Michael Heider, Helena Stegherr, Roman Sraj, and Jörg Hähner. 2022. Comparing different Metaheuristics for Model Selection in a Supervised Learning Classifier System. In Genetic and Evolutionary Computation Conference Companion (GECCO ’22 Companion). https://doi.org/10.1145/3520304.3529015
Michael Heider, Helena Stegherr, David Pätzel, Roman Sraj, Jonathan Wurth and Jörg Hähner. 2022. Approaches for Rule Discovery in a Learning Classifier System. In Proceedings of the 14th International Joint Conference on Computational Intelligence - ECTA. https://doi.org/10.5220/0011542000003332
Michael Heider, Helena Stegherr, David Pätzel, Roman Sraj, Jonathan Wurth, Benedikt Volger and Jörg Hähner. 2023. Discovering rules for rule-based machine learning with the help of novelty search. SN Computer Science 4, 6, 778. DOI: 10.1007/s42979-023-02198-x
Michael Heider, Helena Stegherr, Richard Nordsieck and Jörg Hähner. 2023. Assessing model requirements for explainable AI: a template and exemplary case study. Artificial Life 29, 4, 468-486. MIT press. DOI: https://doi.org/10.1162/artl_a_00414
Michael Heider, David Pätzel, Helena Stegherr, Jörg Hähner. 2023. A Metaheuristic Perspective on Learning Classifier Systems. In: Eddaly, M., Jarboui, B., Siarry, P. (eds) Metaheuristics for Machine Learning. Computational Intelligence Methods and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-19-3888-7_3
We provide the experiment scripts used in associated publications under: https://github.com/heidmic/suprb-experimentation
pip3 install -r requirements.txt
Tested with Python 3.9.4.
Newly created branch should follow a pattern that allows easy comprehension of what is being done and why, e.g.:
<Issue_number>_<short_description_of_PR>, e.g. 3_introduce_volume_for_rule_discovery.
If there is no open issue the branch name should just reflect a short description of the feature/fix, e.g. introduce_volume_for_rule_discovery.
The commit messages of all the commits in your PR should be properly formatted:
- The first line is the commit message's summary line.
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- The summary line is just one line (aim for 50 characters or less).
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- The body may contain multiple paragraphs, separated by blank lines.
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Avoid merge commits by using rebase rather than merge when combining branches
If you use this code for research, please cite:
Michael Heider, Helena Stegherr, Jonathan Wurth, Roman Sraj, and Jörg Hähner. 2022. Separating Rule Discovery and Global Solution Composition in a Learning Classifier System. In Genetic and Evolutionary Computation Conference Companion (GECCO ’22 Companion). https://doi.org/10.1145/3520304.3529014