Real-valued GCS classifier system

• Autorzy: Łukasz Cielecki , Olgierd Unold
• Języki publikacji: EN

Abstrakty

EN

Learning Classifier Systems (LCSs) have gained increasing interest in the genetic and evolutionary computation literature. Many real-world problems are not conveniently expressed using the ternary representation typically used by LCSs and for such problems an interval-based representation is preferable. A new model of LCSs is introduced to classify real-valued data. The approach applies the continous-valued context-free grammar-based system GCS. In order to handle data effectively, the terminal rules were replaced by the so-called environment probing rules. The rGCS model was tested on the checkerboard problem.

Słowa kluczowe

EN

learning classifier systems; GAs; grammatical inference; context-free grammar; GCS

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2007
• Tom: 17
• Numer: 4
• Strony: 539-547

Daty

wydano

2007

(nieznana)

2006-12-15

otrzymano

2007-04-11

poprawiono

2007-06-05

Twórcy

autor

Łukasz Cielecki

• Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

autor

Olgierd Unold

• Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

Bibliografia

• Gold E. (1967): Language identification in the limit. Information Control, Vol.10, No.5, pp.447-474.
• Cielecki L. and Unold O. (2007): GCS with real-valued input. Lecture Notes in Computer Science, Vol.4527. Berlin: Springer Verlag, pp.488-497.
• Holland J.H. (1975): Adaptation in Natural and Artificial Systems. Ann Arbor, University of Michigan Press.
• Holland J.H. (1976): Adaptation. In: Progress in Theoretical Biology, (R.F. Rosen, Ed.) New York: Plenum Press, pp.263-293.
• Holland J.H. (1986): Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In: Machine Learning, an Artificial Intelligence Approach, Vol.II, (R.S. Michalski, J.G. Carbonell, T.M. Mitchell, Eds.), San Mateo, Morgan Kaufmann, pp.593-623.
• Holmes J.H. and Lanzi P.L., and Stolzmann W., and Wilson S.W (2002): Learning classifier systems: New models, successful applications. Information Processing Letters, Vol.82, No.1, pp.23-30.
• Judd K.L. and Tesfatsion L. (2005): Agent-based computational economics. In: Handbook of Computational Economics, Vol.2, Agent-Based Computational Economics Elsevier Science B.V.
• Katagami D. and Yamada S. (2000): Interactive classifier system for real robot learning. Proceedings of the IEEE International Workshop on Robot-Human Interaction ROMAN-2000, Osaka, Japan, pp.258-263.
• Lanzi P.L. and Riolo R.L. (2000): A roadmap to the last decade of learning classifier system research, Lecture Notes in Artificial Intelligence, Vol.1813, Berlin: Springer-Verlag, pp.33-62.
• Stolzmann W. (2000): An introduction to anticipatory classifier systems. Lecture Notes in Artificial Intelligence, Vol.1813, Berlin: Springer-Verlag, pp.175-194.
• Stone C. and Bull L. (2003): For real! XCS with continuous-valued inputs. Evolutionary Computation,Vol.11, No.3, pp.299-336.
• Unold O. (2005a): Context-free grammar induction with grammar-based classifier system. Archives of Control Science, Vol.15(LI), No.4, pp.681-690.
• Unold O. (2005b): Playing a toy-grammar with GCS. Lecture Notes in Computer Science, Vol.3562, Springer-Verlag, pp.300-309.
• Unold O. and Cielecki L. (2005a): Grammar-based classifier system. In: Issues in Intelligent Systems: Paradigms (O.Hryniewicz, J. Kacprzyk, J.Koronacki, S.T. Wierzchon, Eds.), EXIT, Warsaw, pp.273-286.
• Unold O. and Cielecki L. (2005b): How to use crowding selection in grammar-based classifier system. In: Proceedings of the 5th International Conference on Intelligent Systems Design and Applications (H. Kwasnicka and M. Paprzycki M., Eds.), Los Alamos, IEEE Computer Society Press, pp.126-129.
• Unold O. and Dabrowski G. (2003): Use of learning classifier system for inferring natural language grammar. In: Design and Application of Hybrid Intelligent Systems (A.Abraham, M.Koppen, K.Franke, Eds.), Amsterdam, IOS Press, pp.272-278.
• Wilson S.W. (1995): Classifier fitness based on accuracy. Evolutionary Computation, Vol.3, No.2, pp.147-175.
• Wilson, S.W (2000): Get real! XCS with continuous-valued inputs. In: Learning Classifier Systems. From Foundations to Applications (P.L. Lanzi and W. Stolzmann, and S.W. Wilson, Eds.), Lecture Notes in Artificial Intelligence, Vol.813, Berlin: Springer-Verlag, pp.209-222.
• Younger D. (1967): Recognition and parsing of context-free languages in time n^3. Technical report, University of Hawaii, Department of Computer Science