Abstract
This sequel continues the comparison of the twins XCS and SB–XCS. We find they tend towards different representations of the solution and distinguish three types of representations which rule populations can form, namely complete maps, partial maps, and default hierarchies. Following this we evaluate the respective advantages and disadvantages of complete and partial maps at some length. We conclude that complete maps are likely to be superior for sequential tasks. For non-sequential tasks, partial maps have the advantage of parsimony whereas complete maps can take advantage of subsumption deletion. It is unclear which is more significant. We also conclude that partial maps are likely to be suitable for Pittsburgh classifier systems and supervised learning systems.
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Booker, L.B.: Do We Really Need to Estimate Rule Utilities in Classifier Systems? In: Lanzi et al. [18], pp. 125–142
Booker, B., Goldberg, D.E., Holland, J.H.: Classifier systems and genetic algorithms. Artificial Intelligence 40, 235–282 (1989)
Bull, L., Hurst, J.: ZCS Redux. To appear in Evolutionary Computation (2002)
Butz, M.V., Kovacs, T., Lanzi, P.L., Wilson, S.W.: How XCS Evolves Accurate Classifiers. In: Spector, L., Goodman, E.D., Wu, A., Langdon, W.B., Voigt, H.M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E. (eds.) GECCO 2001: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 927–934. Morgan Kaufmann, San Francisco (2001)
Dorigo, M.: New perspectives about default hierarchies formation in learning classifier systems. In: Ardizzone, E., Sorbello, F., Gaglio, S. (eds.) AI*IA 1991. LNCS (LNAI), vol. 549, pp. 218–227. Springer, Heidelberg (1991)
Goldberg, D.E.: Computer-Aided Gas Pipeline Operation using Genetic Algorithms and Rule Learning. PhD thesis, The University of Michigan (1983)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Hartley, A.: Genetics Based Machine Learning as a Model of Perceptual Category Learning in Humans. Master’s thesis, University of Birmingham (1998)
Hartley, A.: Accuracy-based fitness allows similar performance to humans in static and dynamic classification environments. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) GECCO 1999: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 266–273. Morgan Kaufmann, San Francisco (1999)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975), Republished by the MIT press (1992)
Holland, J.H.: Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In: Mitchell, T., Michalski, R., Carbonell, J. (eds.) Machine learning, an artificial intelligence approach, vol. II, ch. 20, pp. 593–623. Morgan Kaufmann, San Francisco (1986)
Holland, J.H., Booker, L.B., Colombetti, M., Dorigo, M., Goldberg, D.E., Forrest, S., Riolo, R.L., Smith, R.E., Lanzi, P.L., Stolzmann, W., Wilson, S.W.: What is a Learning Classifier System. In: Lanzi et al. [18], pp. 3–32
Holland, J.H., Holyoak, K.J., Nisbett, R.E., Thagard, P.R.: Induction. Processes of Inference, Learning and Discovery. The MIT Press, Cambridge (1986)
Holland, J.H., Reitman, J.S.: Cognitive systems based on adaptive algorithms. In: Waterman, D.A., Hayes-Roth, F. (eds.) Pattern-directed inference systems. Academic Press, New York (1978); Reprinted in: Fogel, D.B. (Ed.): Evolutionary Computation. The Fossil Record. IEEE Press, Los Alamitos (1998) ISBN: 0-7803-3481-7
Kovacs, T.: Strength or Accuracy? Fitness Calculation in Learning Classifier Systems. In: Lanzi et al. [18], pp. 143–160
Kovacs, T.: A Comparison of Strength and Accuracy-Based Fitness in Learning Classifier Systems. PhD thesis, School of Computer Science, University of Birmingham (2002)
Kovacs, T., Kerber, M.: What makes a problem hard for XCS? In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996, pp. 80–99. Springer, Heidelberg (2001)
Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.): IWLCS 1999. LNCS (LNAI), vol. 1813, p. 243. Springer, Heidelberg (2000)
Riolo, R.L.: Bucket Brigade Performance: II. Default Hierarchies. In: Grefenstette, J.J. (ed.) Proceedings of the 2nd International Conference on Genetic Algorithms (ICGA 1987), Cambridge, MA, 196–201. Lawrence Erlbaum Associates, Mahwah (1987)
Smith, R.E.: Default Hierarchy Formation and Memory Exploitation in Learning Classifier Systems. PhD thesis, University of Alabama (1991)
Smith, R.E., Goldberg, D.E.: Variable default hierarchy separation in a classifier system. In: Rawlins, G.J.E. (ed.) Proceedings of the First Workshop on Foundations of Genetic Algorithms, San Mateo, July 15–18, pp. 148–170. Morgan Kaufmann, San Francisco (1991)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Wilson, S.W.: Classifier Systems and the Animat Problem. Machine Learning 2, 199–228 (1987)
Wilson, S.W.: Bid competition and specificity reconsidered. Complex Systems 2, 705–723 (1989)
Wilson, S.W.: Classifier Fitness Based on Accuracy. Evolutionary Computation 3(2), 149–175 (1995)
Wyatt, J.L.: Exploration and Inference in Learning from Reinforcement. PhD thesis, Dept. of Artificial Intelligence, University of Edinburgh (1997)
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Kovacs, T. (2003). XCS’s Strength-Based Twin: Part II. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Learning Classifier Systems. IWLCS 2002. Lecture Notes in Computer Science(), vol 2661. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-40029-5_6
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DOI: https://doi.org/10.1007/978-3-540-40029-5_6
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