Geodesic
Real–valued GCS classifier system
International Journal of Applied Mathematics and Computer Science, Tome 17 (2007) no. 4, pp. 539-547.

Voir la notice de l'article provenant de la source Library of Science

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 realvalued 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.
Keywords: learning classifier systems, GCS, GAs, grammatical inference, context-free grammar
Mots-clés : uczenie maszynowe, wnioskowanie gramatyczne, gramatyka bezkontekstowa 
@article{IJAMCS_2007_17_4_a9,
     author = {Cielecki, {\L}. and Unold, O.},
     title = {Real{\textendash}valued {GCS} classifier system},
     journal = {International Journal of Applied Mathematics and Computer Science},
     pages = {539--547},
     publisher = {mathdoc},
     volume = {17},
     number = {4},
     year = {2007},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/IJAMCS_2007_17_4_a9/}
}
TY  - JOUR
AU  - Cielecki, Ł.
AU  - Unold, O.
TI  - Real–valued GCS classifier system
JO  - International Journal of Applied Mathematics and Computer Science
PY  - 2007
SP  - 539
EP  - 547
VL  - 17
IS  - 4
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/IJAMCS_2007_17_4_a9/
LA  - en
ID  - IJAMCS_2007_17_4_a9
ER  - 
%0 Journal Article
%A Cielecki, Ł.
%A Unold, O.
%T Real–valued GCS classifier system
%J International Journal of Applied Mathematics and Computer Science
%D 2007
%P 539-547
%V 17
%N 4
%I mathdoc
%U http://geodesic.mathdoc.fr/item/IJAMCS_2007_17_4_a9/
%G en
%F IJAMCS_2007_17_4_a9
Cielecki, Ł.; Unold, O. Real–valued GCS classifier system. International Journal of Applied Mathematics and Computer Science, Tome 17 (2007) no. 4, pp. 539-547. http://geodesic.mathdoc.fr/item/IJAMCS_2007_17_4_a9/

[1] Gold E. (1967): Language identification in the limit. Information Control, Vol. 10, No. 5, pp. 447-474.

[2] Cielecki L. and Unold O. (2007): GCS with real-valued input. Lecture Notes in Computer Science, Vol. 4527. Berlin: Springer Verlag, pp. 488-497.

[3] Holland J.H. (1975): Adaptation in Natural and Artificial Systems. Ann Arbor, University of Michigan Press.

[4] Holland J.H. (1976): Adaptation. In: Progress in Theoretical Biology, (R.F. Rosen, Ed.) New York: Plenum Press, pp. 263-293.

[5] 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.

[6] 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.

[7] 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.

[8] 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.

[9] 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.

[10] Stolzmann W. (2000): An introduction to anticipatory classifier systems. Lecture Notes in Artificial Intelligence, Vol. 1813, Berlin: Springer-Verlag, pp. 175-194.

[11] Stone C. and Bull L. (2003): For real! XCS with continuous-valued inputs. Evolutionary Computation,Vol. 11, No. 3, pp. 299-336.

[12] Unold O. (2005a): Context-free grammar induction with grammar-based classifier system. Archives of Control Science, Vol. 15 (LI), No. 4, pp. 681-690.

[13] Unold O. (2005b): Playing a toy-grammar with GCS. Lecture Notes in Computer Science, Vol. 3562, Springer-Verlag, pp. 300-309.

[14] Unold O. and Cielecki L. (2005a): Grammar-based classifier system. In: Issues in Intelligent Systems: Paradigms (O.Hryniewicz, J. Kacprzyk, J.Koronacki, S.T.Wierzchoń, Eds.), EXIT, Warsaw, pp. 273-286.

[15] 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 Alamitos, IEEE Computer Society Press, pp. 126-129.

[16] 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.Köppen, K.Franke, Eds.), Amsterdam, IOS Press, pp. 272-278.

[17] Wilson S.W. (1995): Classifier fitness based on accuracy. Evolutionary Computation, Vol. 3, No. 2, pp. 147-175.

[18] 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.

[19] Younger D. (1967): Recognition and parsing of context-free languages in time n3. Technical report, University of Hawaii, Department of Computer Science.