Hyper-ellipsoidal conditions in XCS: Rotation, linear approximation, and solution structure

GECCO 2006 - Genetic and Evolutionary Computation Conference

Volumn 2, Issue , 2006, Pages 1457-1464

  Butz, Martin V ^a   Lanzi, Pier Luca ^b   Wilson, Stewart W ^c  

^a UNIVERSITY OF WÜRZBURG   (Germany)

^b POLITECNICO DI MILANO   (Italy)

^c Prediction Dynamics   (United States)

Author keywords

Function Approximation; Genetic Algorithms; LCS; Locally Weighted Learning; Recursive Least Squares; XCS

Indexed keywords

APPROXIMATION THEORY; CLASSIFICATION (OF INFORMATION); FUNCTION EVALUATION; GENETIC ALGORITHMS; ITERATIVE METHODS; LEAST SQUARES APPROXIMATIONS; LINEAR EQUATIONS; LINEAR SYSTEMS;

FUNCTION APPROXIMATION; LCS; LOCALLY WEIGHTED LEARNING; RECURSIVE LEAST SQUARES; XCS;

LEARNING SYSTEMS;

EID: 33750259988     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. C. G. Atkeson, A. W. Moore, and S. Schaal. Locally weighted learning. Artificial Intelligence Review, 11:11-73, 1997.
2. E. Bernadó, X. Llorà, and J. M. Garrell. XCS and GALE: A comparative study of two learning classifier systems and six other learning algorithms on classification tasks. In P. L. Lanzi, W. Stolzmann, and S. W. Wilson, editors, Advances in Learning Classifier Systems (LNAI 2321), pages 115-132. Springer-Verlag, Berlin Heidelberg, 2002.
3. E. Bernado-Mansilla and J. M. Garrell-Guiu. Accuracy-based learning classifier systems: Models, analysis, and applications to classification tasks. Evolutionary Computation, 11:209-238, 2003.
4. L. B. Booker, D. E. Goldberg, and J. H. Holland. Classifier systems and genetic algorithms. Artificial Intelligence, 40:235-282, 1989.
5. M. V. Butz. Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system. GECCO 2005: Genetic and Evolutionary Computation Conference: Volume 2, pages 1835-1842, 2005.
6. M. V. Butz, D. E. Goldberg, and P. L. Lanzi. Foundations of Learning Classifier Systems, chapter Computational Complexity of the XCS Classifier System, pages 91-126. Studies in Fuzziness and Soft Computing. Springer-Verlag, Berlin Heidelberg, 2005.
7. M. V. Butz, D. E. Goldberg, and K. Tharakunnel. Analysis and improvement of fitness exploitation in XCS: Bounding models, tournament selection, and bilateral accuracy. Evolutionary Computation, 11:239-277, 2003.
8. M. V. Butz and S. W. Wilson. An algorithmic description of XCS. In P. L. Lanzi, W. Stolzmann, and S. W. Wilson, editors, Advances in learning classifier systems: Third international workshop, IWLCS 2000 (LNAI 1996), pages 253-272. Springer-Verlag, Berlin Heidelberg, 2001.
9. M. Ebner, M. Shackleton, and R. Shipman. How neutral networks influence evolvability. Complexity, 7(2):19-33, 2001.
10. N. Hansen and A. Ostermeier. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9:159-195, 2001.
11. S. Haykin. Adaptive filter theory. Prentice Hall, Upper Saddle River, NJ, 4th edition edition, 2002.
12. J. H. Holland and J. S. Reitman. Cognitive systems based on adaptive algorithms. In D. A. Waterman and F. Hayes-Roth, editors, Pattern directed inference systems, pages 313-329. Academic Press, New York, 1978.
13. P. L. Lanzi. Generalization in the XCSF classifier system: Analysis, improvement, and extensions. IlliGAL report 2005012, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, 2005.
14. P. L. Lanzi, D. Loiacono, S. W. Wilson, and D. E. Goldberg. Extending XCSF beyond linear approximation IGECCO 2005: Genetic and Evolutionary Computation Conference: Volume 2, pages 1827-1834, 2005.
15. E. W. Euler angles. From MathWorld-A Wolfram Web Resource. http://mathworld.wolfram.com/EulerAngles.html, 1999.
16. S. W. Wilson. Classifier fitness based on accuracy. Evolutionary Computation, 3(2):149-175, 1995.
17. S. W. Wilson. Get real! XCS with continuous-valued inputs. In P. L. Lanzi, W. Stolzmann, and S. W. Wilson, editors, Learning classifier systems: From foundations to applications (LNAI 1813), pages 209-219. Springer-Verlag, Berlin Heidelberg, 2000.
18. S. W. Wilson. Classifiers that approximate functions. Natural Computing, 1:211-234, 2002.
19. S. W. Wilson. Compact rulesets from XCSI. In P. L. Lanzi, W. Stolzmann, and S. W. Wilson, editors, Advances in learning classifier systems: Fourth international workshop, IWLCS 2001 (LNAI 2321), pages 196-208. Springer-Verlag, Berlin Heidelberg, 2002.
20. S. W. Wilson. Classifier systems for continuous payoff environments. Proceedings of the Sixth Genetic and Evolutionary Computation Conference (GECCO-2004): Part II, pages 824-835, 2004.