Building anticipations in an accuracy-based learning classifier system by use of an artificial neural network

2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings

Volumn 3, Issue , 2005, Pages 2046-2052

  O'Hara, Toby ^a   Bull, Larry ^a  

^a UNIVERSITY OF THE WEST OF ENGLAND   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EVOLUTIONARY ALGORITHMS; LEARNING SYSTEMS;

LEARNING CLASSIFIER SYSTEMS;

CLASSIFICATION (OF INFORMATION);

EID: 27144452939     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. Belew, R.K., McInerney, J. & Schraudolph, N.N. (1991) Evolving Networks: Using the Genetic Algorithm with Connectionist Learning. In C.G. Langton, C. Taylor, J.D. Farmer & S. Rasmussen (eds) Artificial Life II, Addison-Wesley, pp511-548.
2. Booker, L. (1985) Improving the Performance of Genetic Algorithms in Classifier Systems. In J.J. Grefenstette (ed.) Proceedings of the First International Conference on Genetic Algorithms and their Applications, Lawrence Erlbaum Assoc., pp80-92.
3. Booker, L.B. (1989) Triggered Rule Discovery in Classifier Systems. In J.D. Schaffer (ed.) Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann. pp265-274.
4. Bull, L. (2002) Lookahead and Latent Learning in ZCS. In W.B. Langdon, E.Cantu-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A.C. Schultz, J. F. Miller, E. Burke & N. Jonoska (eds) GECCO-2002; Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, pp 897-904.
5. Bull, L. (2004) Lookahead and Latent Learning in a Simple Accuracy-based Learning Classifier System. In X. Yao et al. (eds) Parallel Problem Solving from Nature - -PPSN VIII. Springer Verlag, pp1042-1050.
6. Bull, L. & O'Hara, T. (2002) Accuracy-based Neuro and Neuro-Fuzzy Classifier Systems. In W.B.Langdon, E.Cantu-Paz, K.Mathias, R. Roy, D.Davis, R. Poli, K.Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A.C. Schultz, J. F. Miller, E. Burke & N.Jonoska (eds) GECCO-2002: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, pp905-911.
7. Butz, M. & Wilson, S.W. (2001) An Algorithmic Description of XCS. In P-L. Lanzi, W. Stolzmann & S.W. Wilson (eds) Advances in Learning Classifier Systems: IWLCS 2000. Springer, pp253-272.
8. Butz, M.V. & Stolzmann, W. (2002) An Algorithmic Description of ACS2. In P-L. Lanzi, W. Stolzmann & S.W. Wilson (eds) Advances in Learning Classifier Systems: Proceedings of the Fourth International Workshop. Springer, pp211-230.
9. Gerard, G. Stolzmann, W. & Sigaud, O. (2002) YACS a new Learning Classifier System using Anticipation Soft Computing 6(3-4), 216-228.
10. Holland, J.H. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press.
11. Holland, J.H. (1976) Adaptation. In R. Rosen & F.M. Snell (Eds) Progress in Theor. Biology 4. Plenum.
12. Holland, J.H., Holyoak, K.J., Nisbett, R.E. & Thagard, P.R. (1986) Induction: Processes of Inference, Learning and Discovery. MIT Press.
13. Kovacs, T. (1997) XCS Classifier System Reliably Evolves Accurate, Complete, and Minimal Representations for Boolean Functions. In Soft Computing in Engineering Design and Manufacturing. Springer, pp59-68.
14. Lanzi, P-L (1997) A Model of the Environment to Avoid Local Learning. In Technical Report N. 97.46. Dipartimento di Elettronica e Informazione Politecnico di Milano, 1997. Available at fttp.//www.elet.polimi.it/people/ lanzi/listpub.html.
15. O'Hara, T. & Bull, L. (2003) Backpropagation in Accuracy-based Neural Learning Classifier Systems. Technical Report UWELCSG03-007. Available from http://www.cems.uwe.ac.uk/lcsg.
16. O'Hara, T. & Bull, L. (2004) Prediction Calculation in Accuracy-based Neural Learning Classifier Systems. Technical Report UWELCSG04-004. Available from http://www.cems.uwe.ac.uk/lcsg.
17. Riolo, R.L. (1991). Lookahead planning and latent learning in a classifier system. In Meyer, J.-A., & Wilson, S. W. (Eds.), From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior pp. 316-326. Cambridge, MA: MIT Press.
18. Rumelhart, D.E. & McClelland, J.L. (1986) Explorations in Parallel Distributed Processing. MIT Press.
19. Stolzmann, W. (1998). Anticipatory Classifier Systems In Genetic Programming 1998 pp. 658-664. University of Wisconsin, Madison, Wisconsin: Morgan Kaufmann.
20. Tani, J. (1996). Model-based learning for mobile robot navigation from the dynamical system perspective. IEEE Transactions on System, Man and Cybernetics, 26(3) :421-436.
21. Wilson, S.W. (1994). ZCS: A zeroth level classifier system. Evolutionary Computation 2(1): 1-18, 1994.
22. Wilson, S.W. (1995). Classifier Fitness Based on Accuracy. Evolutionary Computing 3: 149-175
23. Wilson, S.W. (2000) Get Real! XCS with Continuous-Valued Inputs. In P-L. Lanzi, W. Stolzmann & S.W. Wilson (eds) Learning Classifier Systems: From Foundations to Applications. Springer, pp209-222.