By Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson
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Additional resources for Advances in learning classifier systems: third international workshop, IWLCS 2000, Paris, France, September 15-16, 2000 : revised papers
E. R. (1986) Induction: Processes of Inference, Learning and Discovery. MIT Press. , Goldberg, D. & Deb, K. (1994) Implicit Niching in a Learning Classifier System: Nature’s Way. Evolutionary Computation, 2(1):37-65. Kovacs, T. (1999) Strength or Accuracy ? A Comparison of Two Approaches to Fitness Calculation in Learning Classifier Systems. S. ) Proceedings of the 1999 Genetic and Evolutionary Computation Conference Workshop Program, Gecco, pp290297. Lanzi, P-L. & Colombetti, M. (1999) An Extension to the XCS Classifier System for Stochastic Environments.
For clarity, the absorbing states are not shown here. 25 26 L. Bull (a) (b) Fig. 2. Comparison of probabilities under the generalization hypothesis. (a) shows the panmictic GA and (b) approximates a niche GA. 6 Mutation It has recently been suggested that self-adaptive mutation techniques, such as those used in Meta-Evolutionary Programming [Fogel 1992], can be used in LCSs [Bull et al. 2000]. Results showed that it is possible to allow the mutation rate to evolve over time in LCSs which make use of either prediction-based fitness or accuracy-based fitness.
In combination with the enhanced classifier structure, the learning mechanism results in a complete internal model of the environment rather than a condition-action-payoff model. e. cognitive processes). L. Lanzi, W. W. ): IWLCS 2000, LNAI 1996, pp. 37–51, 2001. V. E. Goldberg, and W. Stolzmann Stolzmann, Butz, Hoffmann, and Goldberg (2000) investigated the present cognitive capabilities, introduced two new ones and outlined possible future applications. Until now, the ACS considered all changing attributes as results from its actions.