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Tytuł artykułu

LCS and GP Approaches to Multiplexer’s Problem

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Języki publikacji
EN
Abstrakty
EN
In this paper we present the use of learning classifier systems and genetic programming to solving multiplexer’s problem. The function of multiplexer is the popular apparatus of researches which is used to investigate the effectiveness of systems based on evolutionary algorithms. It turns out that the eXtended Classifier System (XCS) learns the problem of multiplexer effectively and Genetic Programming (GP) finds the form of function of multiplexer correctly.
Rocznik
Tom
Strony
195--206
Opis fizyczny
Bibliogr. 15 poz., wykr.
Twórcy
  • The State Higher School of Vocational Education in Elblag, The Institute of Applied Informatics Wojska Polskiego 1, 82-300 Elblag, Poland
autor
  • The State Higher School of Vocational Education in Elblag, The Institute of Applied Informatics Wojska Polskiego 1, 82-300 Elblag, Poland
  • University of Podlasie, Institute of Computer Science Sienkiewicza 51, 08-110 Siedlce, Poland
  • Institute of Computer Science, Polish Academy of Science Ordona 21, 01-237 Warsaw, Poland
Bibliografia
  • 1. Cramer, N. L. (1985). A representation for the adaptive generation of simple sequential programs. Proceedings of an International Conference on Genetic Algorithms and the Applications, Carnegie-Mellon University, Pittsburgh, PA, USA, 183-187.
  • 2. Holland J. H., (1986). Escaping Brittleness: The possibilities of General-Purpose Learning Algorithms Applied to Parallel Rule-Based Systems, in: M. et al. (Ed.), Machine learning, an artificial intelligence approach. Volume II, Morgan Kaufmann.
  • 3. Koza John R., (1994). Genetic Programming II: Automatic Discovery of Reusable Programs, Cambridge, Bradford Book / MIT Press 4 s. XXI, 746 Complex Adaptive Systems.
  • 4. Koza J. R., (1994). Genetic Programming: on the Programming of Computers by Means of Natural Selection, Cambridge, Bradford Book / MIT Press 4 s. XV, 819 Complex Adaptive Systems.
  • 5. Koza J. R., (1994). Genetic Programming for Economic Modeling, Stanford University, California USA.
  • 6. Langdon W. B., Qureshi Adil, (2000). Genetic Programming - Computer using “Natural Selection ” to Generate Programs, University Collage London.
  • 7. Lanzi P. L., (1997). A Study of the Generalization Capabilities of XCS, in Back, T. (ed.), Proc. of ICGA97 Conference, Morgan Kaufmann, San Francisco, 418-425.
  • 8. Lanzi P. L., Perruci A., (1999). Extending the representation of classifier conditions, part II: from messy coding to s-expressions, GECCO.
  • 9. Venturini G., (1994). Apprentissage Adaptatif et Apprentissage Supervise par Algorithme Genetique, These de Docteur en Science (Informatique), Universite de Paris-Sud.
  • 10. Watkins C. J. C. H., (1989). Learning from Delayed Rewards, Ph.D thesis, Cambridge University.
  • 11. Widrow B., Hoff M., (1960). Adaptive switching circuits. In Western Electronic Show and Convention, Institute of Radio Engineers (now IEEE) vol. 96-104.
  • 12. Wilson S. W., (1994). ZCS: a zeroth level classifier system. Evolutionary Computation, 1(2): 1-18.
  • 13. Wilson S. W., (1995). Classifier Fitness Based on Accuracy, Evolutionary Computation, 3(2), 149-175.
  • 14. Wilson S. W., (1996). Generalization in the XCS classifier system, unpublished contribution to the ICML'96 Workshop on Evolutionary Computing and Machine Learning, http://prediction-dynamics.com.
  • 15. Zonker D., Punch B., (1996). Lil-gp 1.01 User's Manual, Michigan State University.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-abd3d137-de0a-47e7-a033-17b73abc1e13
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