Evolutionary Co-Optimization of Rule Shape and Fuzziness in Rule-Based Machine Learning

Rule-based machine learning systems face a fundamental representation challenge: traditional approaches require a priori selection between crisp intervals and/or fuzzy membership functions. This can result in either overly complex fuzzy rule sets or insufficiently expressive crisp rule sets. To address this limitation, we introduce a novel evolutionary approach for learning classifier system (LCS) machine learning algorithms that co-optimizes both rule shape and fuzziness using a four-parameter beta distribution. Our method integrates specialized genetic operators with generalization pressure mechanisms, such as subsumption and crispification operators, to favor crisp, interpretable rules when possible. Experiments on real-world classification tasks demonstrate competitive accuracy compared to state-of-the-art black-box models while maintaining superior interpretability. Our method can automatically determine appropriate rule representations for different feature space regions, evolving toward simpler crisp rules where possible while retaining fuzzy rules only where necessary for handling complex decision boundaries.