Neurocomputing: foundations of research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Classifiers that approximate functions
Natural Computing: an international journal
Learning probability distributions in continuous evolutionary algorithms– a comparative review
Natural Computing: an international journal
Extending XCSF beyond linear approximation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Classifier prediction based on tile coding
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Prediction update algorithms for XCSF: RLS, Kalman filter, and gain adaptation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
XCSF with computed continuous action
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
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The evolution strategy is one of the strongest evolutionary algorithms for optimizing real-value vectors. In this paper, we study how to use it for the evolution of prediction weights in XCSF in order to make the computed prediction more accurate. Our version of XCSF shows to be able to evolve more accurate linear approximations of functions. It is more efficient than the original XCSF and slightly better than XCSF with recursive least squares, in spite of its simple structure and its low complexity.