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}
}
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-based… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 28 references

Reinforcement learning through gradient descent

  • Ph.D. dissertation, School of Comput. Sci…
  • 1999
Highly Influential
7 Excerpts

Classifier fitness based on accuracy

  • Evol. Comput., vol. 3, no. 2, pp. 149–175, 1995…
  • 1995
Highly Influential
10 Excerpts

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