David Pätzel
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View article: A Closer Look at Length-niching Selection and Spatial Crossover in Variable-length Evolutionary Rule Set Learning
A Closer Look at Length-niching Selection and Spatial Crossover in Variable-length Evolutionary Rule Set Learning Open
We explore variable-length metaheuristics for optimizing sets of rules for regression tasks by extending an earlier short paper that performed a preliminary analysis of several variants of a single-objective Genetic Algorithm. We describe …
View article: Discovering Rules for Rule-Based Machine Learning with the Help of Novelty Search
Discovering Rules for Rule-Based Machine Learning with the Help of Novelty Search Open
Automated prediction systems based on machine learning (ML) are employed in practical applications with increasing frequency and stakeholders demand explanations of their decisions. ML algorithms that learn accurate sets of rules, such as …
View article: Weighted Mutation of Connections To Mitigate Search Space Limitations in Cartesian Genetic Programming
Weighted Mutation of Connections To Mitigate Search Space Limitations in Cartesian Genetic Programming Open
This work presents and evaluates a novel modification to existing mutation operators for Cartesian Genetic Programming (CGP). We discuss and highlight a so far unresearched limitation of how CGP explores its search space which is caused by…
View article: Synthetic Datasets from the 2023 ECXAI Workshop Paper on Principled Benchmarking for Rule Set Learning Algorithms
Synthetic Datasets from the 2023 ECXAI Workshop Paper on Principled Benchmarking for Rule Set Learning Algorithms Open
Synthetic datasets generated as part of the demonstration given in the paper Towards Principled Synthetic Benchmarks for Explainable Rule Set Learning Algorithms presented at the Evolutionary Computing and Explainable Artificial Intelligen…
View article: Synthetic Datasets from the 2023 ECXAI Workshop Paper on Principled Benchmarking for Rule Set Learning Algorithms
Synthetic Datasets from the 2023 ECXAI Workshop Paper on Principled Benchmarking for Rule Set Learning Algorithms Open
Synthetic datasets generated as part of the demonstration given in the paper Towards Principled Synthetic Benchmarks for Explainable Rule Set Learning Algorithms presented at the Evolutionary Computing and Explainable Artificial Intelligen…
View article: Experiment data for the 2022 GECCO paper on the Bayesian Learning Classifier System
Experiment data for the 2022 GECCO paper on the Bayesian Learning Classifier System Open
Data collected during the empirical study for the paper *Pätzel and Hähner. 2022. The Bayesian Learning Classifier System: Implementation, Replicability, Comparison with XCSF* (DOI: https://doi.org/10.1145/3512290.3528736). To evaluate the…
View article: Experiment data for the 2022 GECCO paper on the Bayesian Learning Classifier System
Experiment data for the 2022 GECCO paper on the Bayesian Learning Classifier System Open
Data collected during the empirical study for the paper *Pätzel and Hähner. 2022. The Bayesian Learning Classifier System: Implementation, Replicability, Comparison with XCSF* (DOI: https://doi.org/10.1145/3512290.3528736). To evaluate the…
View article: Approaches for Rule Discovery in a Learning Classifier System
Approaches for Rule Discovery in a Learning Classifier System Open
To fill the increasing demand for explanations of decisions made by automated prediction systems, machine learning (ML) techniques that produce inherently transparent models are directly suited. Learning Classifier Systems (LCSs), a family…
View article: An overview of LCS research from 2020 to 2021
An overview of LCS research from 2020 to 2021 Open
The International Workshop on Learning Classifier Systems (IWLCS) is an annual workshop at the GECCO conference where new concepts and results regarding learning classifier systems (LCSs) are presented and discussed. One recurring part of …
View article: Towards a Pittsburgh-style LCS for learning manufacturing machinery parametrizations
Towards a Pittsburgh-style LCS for learning manufacturing machinery parametrizations Open
We present a first evaluation of a new accuracy-based Pittsburgh-style learning classifier system (LCS) for supervised learning of multi-dimensional continuous decision problems: The SupRB-1 (Supervised Rule-Based) learning system. Designe…
View article: SupRB: A Supervised Rule-based Learning System for Continuous Problems
SupRB: A Supervised Rule-based Learning System for Continuous Problems Open
We propose the SupRB learning system, a new Pittsburgh-style learning classifier system (LCS) for supervised learning on multi-dimensional continuous decision problems. SupRB learns an approximation of a quality function from examples (con…
View article: XCSF for Automatic Test Case Prioritization
XCSF for Automatic Test Case Prioritization Open
Testing is a crucial part in the development of a new product.Due to the change from manual testing to automated testing, companies can rely on a higher number of tests.There are certain cases such as smoke tests where the execution of all…