Prediction update algorithms for XCSF: RLS, Kalman filter, and gain adaptation

  • Authors:

  • Pier Luca Lanzi;Daniele Loiacono;Stewart W. Wilson;David E. Goldberg

  • Affiliations:

  • Politecnico di Milano, Milano, Italy and University of Illinois at Urbana Champaign, Urbana, IL;Politecnico di Milano, Milano, Italy;University of Illinois at Urbana Champaign, Urbana, IL and Prediction Dynamics, Concord, MA;University of Illinois at Urbana Champaign, Urbana, IL

  • Venue:
  • Proceedings of the 8th annual conference on Genetic and evolutionary computation
  • Year:
  • 2006

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Abstract

We study how different prediction update algorithms influence the performance of XCSF. We consider three classical parameter estimation algorithms (NLMS, RLS, and Kalman filter) and four gain adaptation algorithms (K1, K2, IDBD, and IDD). The latter have been shown to perform comparably to the best algorithms (RLS and Kalman), but they have a lower complexity. We apply these algorithms to update classifier prediction in XCSF and compare the performances of the seven versions of XCSF on a set of real functions. Our results show that the best known algorithms still perform best: XCSF with RLS and XCSF with Kalman perform significantly better than the others. In contrast, when added to XCSF, gain adaptation algorithms perform comparably to NLMS, the simplest estimation algorithm, the same used in the original XCSF. Nevertheless, algorithms that perform similarly generalize differently. For instance: XCSF with Kalman filter evolves more compact solutions than XCSF with RLS and gain adaptation algorithms allow better generalization than NLMS.