Adaptive filter theory
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Prediction update algorithms for XCSF: RLS, Kalman filter, and gain adaptation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
A principled foundation for LCS
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
A Principled Foundation for LCS
Learning Classifier Systems
Filtering sensory information with XCSF: improving learning robustness and control performance
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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In this study we deal with the mixing problem, which concerns combining the prediction of independently trained local models to form a global prediction. We deal with it from the perspective of Learning Classifier Systems where a set of classifiers provide the local models. Firstly, we formalise the mixing problem and provide both analytical and heuristic approaches to solving it. The analytical approaches are shown to not scale well with the number of local models, but are nevertheless compared to heuristic models in a set of function approximation tasks. These experiments show that we can design heuristics that exceed the performance of the current state-of-the-art Learning Classifier System XCS, and are competitive when compared to analytical solutions. Additionally, we provide an upper bound on the prediction errors for the heuristic mixing approaches.