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# Gradient descent methods in learning classifier systems: improving XCS performance in multistep problems

@article{Butz2005GradientDM, title={Gradient descent methods in learning classifier systems: improving XCS performance in multistep problems}, author={Martin V. Butz and David E. Goldberg and Pier Luca Lanzi}, journal={IEEE Transactions on Evolutionary Computation}, year={2005}, volume={9}, pages={452-473} }

- Published 2005 in IEEE Transactions on Evolutionary Computation
DOI:10.1109/TEVC.2005.850265

The accuracy-based XCS classifier system has been shown to solve typical data mining problems in a machine-learning competitive way. However, successful applications in multistep problems, modeled by a Markov decision process, were restricted to very small problems. Until now, the temporal difference learning technique in XCS was based on deterministic updates. However, since a prediction is actually generated by a set of rules in XCS and Learning Classifier Systems in general, gradient-basedâ€¦Â CONTINUE READING

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