Guided evolution in XCSF

Patrick O. Stalph, Martin V. Butz

Abstract: High-dimensional problems are challenging for iterative, online (Michigan-style) Learning Classifier Systems, especially because of the large size of the evolutionary search space. The present work proposes to guide mutation toward most suitable classifier condition structures. Focusing on XCSF, we introduce a guided mutation operator: Each classifier stores a finite set of matched samples. Mutation uses those samples to optimize classifier condition shapes by means of an accuracy-weighted covariance matrix. While this approach does not necessarily produce an optimal shape, it is sufficient to discriminate relevant from irrelevant dimensions. Regular evolutionary operators handle the fine tuning. We show that guided XCSF does not only drastically speed up learning, but it also solves higher-dimensional problems than regular XCSF. Experiments illustrate that guided XCSF quickly detects the intrinsic structure of non-linear, oblique, fully sampled, ten-dimensional approximation tasks. The results show that guided XCSF strongly outperforms regular XCSF as well as a statistics-based machine learning approach, the so called Locally Weighted Projection Regression algorithm.