Abstract
To evaluate a real-valued XCS classifier system, we present a validation of Wilson’s XCSR from two points of view. These are: (1) sensitivity of real-valued XCS specific parameters on performance and (2) the design of classifier representation with classifier operators such as mutation and covering. We also propose model with another classifier representation (LU-Model) to compare it with a model with the original XCSR classifier representation (CS-Model.) We did comprehensive experiments by applying a 6-dimensional real-valued multiplexor problem to both models. This revealed the following: (1) there are critical threshold on covering operation parameter (r 0), which must be considered in setting parameters to avoid serious decreases in performance; and (2) the LU-Model has an advantage in smaller classifier population size within the same performance level over the CS-Model, which reveals the superiority of alternative classifier representation for real-valued XCS.
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References
Butz, M.V., Wilson, S.W.: An algorithmic description of XCS. Soft Computing 6, 144–153 (2002)
Holland, J.H.: Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In: Machine learning, an artificial intelligence approach, vol. II (1986)
Kovacs, T.: XCS classifier system reliably evolves accurate, complete, and minimal representations for boolean functions. In: Soft Computing in Engineering Design and Manufacturing, pp. 59–68 (1997)
Stone, C., Bull, L.: For Real! XCS with Continuous-Valued Inputs. Evolutionary Computation 11(3), 298–336 (2003)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: an Introduction. MIT Press, Cambridge (1998)
Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3(2), 149–175 (1995)
Wilson, S.W.: Generalization in the XCS classifier system. In: Genetic Programming 1998: Proc. of the Third Annual Conference, pp. 665–674 (1998)
Wilson, S.W.: Get real! XCS with continuous-valued inputs. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 1999. LNCS (LNAI), vol. 1813, Springer, Heidelberg (2000)
Wilson, S.W.: Mining oblique data with XCS. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996, Springer, Heidelberg (2001)
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Wada, A., Takadama, K., Shimohara, K., Katai, O. (2007). Analyzing Parameter Sensitivity and Classifier Representations for Real-Valued XCS. In: Kovacs, T., Llorà, X., Takadama, K., Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Learning Classifier Systems. IWLCS IWLCS IWLCS 2003 2004 2005. Lecture Notes in Computer Science(), vol 4399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71231-2_1
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DOI: https://doi.org/10.1007/978-3-540-71231-2_1
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