Abstract
Learning Classifier Systems (LCSs) originated from artificial cognitive systems research, but migrated such that LCS became powerful classification techniques in single domains. Modern LCSs can extract building blocks of knowledge utilizing Code Fragments in order to scale to more difficult problems in the same or a related domain. Code Fragments (CF) are GP-like sub-trees where past learning can be reused in future CF sub-trees. However, the rich alphabet produced by the code fragments requires additional computational resources as the knowledge and functional rulesets grow. Eventually this leads to impractically long chains of CFs. The novel work here introduces methods to produce Distilled Rules to remedy this problem by compacting learned functions. The system has been tested on Boolean problems, up to the 70 bit multiplexer and 3x11 bit hidden multiplexer, which are known to be difficult problems for conventional algorithms to solve due to large and complex search spaces. The new methods have been shown to create a new layer of rules that reduce the tree length, making it easier for the system to scale to more difficult problems in the same or a related domain.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
CodeBlocks - code profiler.
References
Alvarez, I.M., Browne, W.N., Zhang, M.: Reusing learned functionality to address complex boolean functions. In: Dick, G., et al. (eds.) SEAL 2014. LNCS, vol. 8886, pp. 383–394. Springer, Heidelberg (2014)
Alvarez, I.M., Browne, W.N., Zhang, M.: Reusing learned functionality in XCS: code fragments with constructed functionality and constructed features. In: Genetic and Evolutionary Computation Conference GECCO ’14 Companion, pp. 969–976. ACM (2014)
Butz, M.V., Wilson, S.W.: An algorithmic description of XCS. Soft Comput. 6, 144–153 (2002)
Butz, M.V.: Rule-Based Evolutionary Online Learning Systems: A Principal Approach to LCS Analysis and Design. Springer, Berlin (2006)
Dixon, W.: An Investigation of the XCS Classifier System in Data Mining. The University of Reading, Reading (2004)
Goldberg, D.E., Korb, B., Deb, K.: Messy genetic algorithms: motivation, analysis, and first results. Complex Syst. 3, 493–530 (1989)
Holland, J.H.: Adaptation. In: Rosen, R., Snell, F.M. (eds.) Progress in Theoretical Biology, vol. 4, pp. 263–293. Academic Press, New York (1976)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. The University of Michigan Press, Ann Arbor (1975)
Koza, J.R.: A hierarchical approach to learning the boolean multiplexer function. In: Foundations of Genetic Algorithms, pp. 171–192. Morgan Kaufmann (1991)
Lanzi, P.L.: Extending the representation of classifier conditions Part I : from binary to messy coding. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1999), vol. 1, pp. 337–344, July 1999
Lanzi, P.L., Perrucci, A.: Extending the representation of classifier conditions Part II : from messy coding to S-Expressions. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1999), vol. 1, pp. 345–352, July 1999
Iqbal, M., Browne, W.N., Zhang, M.: Comparison of Two Methods for Computing Action Values in XCS with Code-Fragment Actions. In: GECCO’13 Companion, pp. 1235–1242 (2013)
Iqbal, M., Browne, W.N., Zhang, M.: Extending learning classifier system with cyclic graphs for scalability on complex, large-scale boolean problems. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1045–1052 (2013)
Iqbal, M., Browne, W.N., Zhang, M.: Learning overlapping natured and niche imbalance boolean problems using XCS classifier systems. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1818–1825 (2013)
Iqbal, M., Browne, W.N., Zhang, M.: Reusing building blocks of extracted knowledge to solve complex, large-scale boolean problems. IEEE Trans. Evol. Comput. 17(3), 503–518 (2013)
Schaffer, J.D.: Learning multiclass pattern discrimination. In: Proceedings of the 1st International Conference on Genetic Algorithms and their Applications (ICGA85), pp. 74–79. Lawrence Erlbaum Associates (1985)
Liang-Yu, C., Po-Ming, L., Tzu-Chien, H.: A sensor tagging approach of knowledge in learning classifier systems. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 2953–2960 (2015)
Hsuan-Ta, L., Po-Ming, L., Tzu-Chien, H.: The subsumption mechanism for XCS using code fragmented conditions. In: Proceedings Companion of the Genetic and Evolutionary Computation Conference, pp. 1275–1282 (2013)
Wilson, S.W.: Classifier fitness based on accuracy. Evol. Comput. 3(2), 149–175 (1995)
Wilson, S.W.: Compact rulesets from XCSI. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) Advances in Learning Classifier Systems 2002. LNCS, vol. 2321, pp. 196–208. Springer, Berlin (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Alvarez, I.M., Browne, W.N., Zhang, M. (2016). Compaction for Code Fragment Based Learning Classifier Systems. In: Ray, T., Sarker, R., Li, X. (eds) Artificial Life and Computational Intelligence. ACALCI 2016. Lecture Notes in Computer Science(), vol 9592. Springer, Cham. https://doi.org/10.1007/978-3-319-28270-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-28270-1_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-28269-5
Online ISBN: 978-3-319-28270-1
eBook Packages: Computer ScienceComputer Science (R0)