Simple markov models of the genetic algorithm in classifier systems: Multi-step tasks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1996, Issue , 2001, Pages 29-36

  Bull, Larry ^a  

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

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; GENETIC ALGORITHMS; MARKOV PROCESSES;

CLASSIFIER SYSTEMS; MARKOV MODEL; MICHIGAN; MULTI-STEP; RULE DISCOVERY;

LEARNING SYSTEMS;

EID: 84942903759     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44640-0_3     Document Type: Conference Paper

References (18)

1. 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 Associates, pp80-93
2. Bull, L. & Hurst, J. (2000) Self-Adaptive Mutation in ZCS Controllers. In S. Cagnoni, R.Poli, G. Smith, D. Corne, M. Oates, E. Hart, P-L. Lanzi, E. Willem, Y. Li, B. Paechter & T.C Fogarty (eds) Real-World Applications of Evolutionary Computing: Proceedings of the EvoNet Workshops-EvoRob 2000, Springer, pp339-346
3. Fogel, D.B. (1992) Evolving Artificial Intelligence. PhD dissertation, University of California
4. Frey, P.W. & Slate, D.J. (1991) Letter Recognition using Holland-style Adaptive Classifiers. Machine Learning 6:161-182
5. Goldberg, D. & Segrest, P. (1987) Finite Markov Chain Analysis of Genetic Algorithms. In J.J. Grefenstette (ed.) Proceedings of the Second International Conference on Genetic Algorithms, Lawrence Erlbaum, pp1-7
6. Holland, J.H. (1975) Adaptation in Natural and Artificial Systems, University of Michigan Press
7. Holland, J.H., Holyoak, K.J., Nisbett, R.E. & Thagard, P.R. (1986) Induction: Processes of Inference, Learning and Discovery. MIT Press
8. Horn, J., Goldberg, D. & Deb, K. (1994) Implicit Niching in a Learning Classifier System: Nature’s Way. Evolutionary Computation, 2(1):37-65
9. Kovacs, T. (1999) Strength or Accuracy ? A Comparison of Two Approaches to Fitness Calculation in Learning Classifier Systems. In A.S. Wu (ed.) Proceedings of the 1999 Genetic and Evolutionary Computation Conference Workshop Program, Gecco, pp290-297
10. Lanzi, P-L. & Colombetti, M. (1999) An Extension to the XCS Classifier System for Stochastic Environments. In W.Banzhaf, J.Daida, A.E.Eiben, M.H.Garzon, V.Honavar, M.Jakiela & R.E.Smith (eds) Proceedings of the Genetic and Evolutionary Computation Conference-Gecco ’99, Morgan Kauffman, pp353-360
11. Lanzi, P-L. & Wilson, S.W. (1999) Optimal Classifier System Performance in Non-Markov Environments. Tech. Report N99.36 Dipartmento di Eletronica e Infromazione. Politecnico di Milano
12. Nix, A.E. & Vose, M.D. (1992) Modelling Genetic Algorithms with Markov Chains. Annals of Mathematics and Artificial Intelligence, 5(1):79-88
13. Rechenberg, I. (1973) Evolutionsstrategie; Optimierung technischer Systeme nach Prinzipen der biologischen Evolution. Frommann-Holzboog Verlag
14. Tomlinson, A. & Bull, L. (1998) A Corporate Classifier System. In A.E. Eiben, T. Bäck, M. Schoenauer & H-P. Schwefel (eds.) Parallel Problem Solving from Nature-PPSN V, Springer, pp. 550-559
15. Watkins, C. (1989) Learning from Delayed Rewards. PhD Dissertation, Cambridge
16. Westerdale, T.H. (1999) An Approach to Credit Assignment in Classifier Systems. Complexity 4(2):
17. Wilson, S.W. (1994) ZCS: A Zeroth-level Classifier System. Evolutionary Computation 2(1):1-18
18. Wilson, S.W. (1995) Classifier Fitness Based on Accuracy. Evolutionary Computation 3(2):149-177.