Visualisation and Optimisation of Learning Classifier Systems for Multiple Domain Learning

  • Yi LiuEmail author
  • Bing Xue
  • Will N. Browne
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10593)


Learning classifier system (LCSs) have the ability to solve many difficult benchmark problems, but they have to be applied individually to each separate problem. Moreover, the solutions produced, although accurate, are not compact such that important knowledge is obscured. Recently a multi-agent system has been introduced that enables multiple, different LCSs to address multiple different problems simultaneously, which reduces the need for human system set-up, recognises existing solutions and assigns a suitable LCS to a new problem. However, the LCSs do not collaborate to solve a problem in a compact or human observable manner. Hence the aim is to extract knowledge from problems by combining solutions from multiple LCSs in a compact manner that enables patterns in the data to be visualised. Results show the successful compaction of multiple solutions to a single, optimum solution, which shows important feature knowledge that would otherwise have been hidden.


Learning classifier systems Multiple domain learning 


  1. 1.
    Bernadó, E., Llorà, X., Garrell, J.M.: XCS and GALE: a comparative study of two learning classifier systems on data mining. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2001. LNCS, vol. 2321, pp. 115–132. Springer, Heidelberg (2002). doi: 10.1007/3-540-48104-4_8 CrossRefGoogle Scholar
  2. 2.
    Browne, W., Scott, D.: An abstraction algorithm for genetics-based reinforcement learning. In: Proceedings of the 7th annual conference on Genetic and evolutionary computation, pp. 1875–1882. ACM (2005)Google Scholar
  3. 3.
    Butz, M.V., Lanzi, P.L., Wilson, S.W.: Function approximation with XCS: Hyperellipsoidal conditions, recursive least squares, and compaction. Trans. Evol. Comput. 3(12), 355–376 (2008)CrossRefGoogle Scholar
  4. 4.
    Dixon, P.W., Corne, D.W., Oates, M.J.: A preliminary investigation of modified XCS as a generic data mining tool. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2001. LNCS, vol. 2321, pp. 133–150. Springer, Heidelberg (2002). doi: 10.1007/3-540-48104-4_9 CrossRefGoogle Scholar
  5. 5.
    Ioannides, C., Browne, W.: Investigating scaling of an abstracted LCS utilising ternary and s-expression alphabets. In: Bacardit, J., Bernadó-Mansilla, E., Butz, M.V., Kovacs, T., Llorà, X., Takadama, K. (eds.) IWLCS 2006-2007. LNCS, vol. 4998, pp. 46–56. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-88138-4_3 CrossRefGoogle Scholar
  6. 6.
    Iqbal, M., Browne, W.N., Zhang, M.: Extracting and using building blocks of knowledge in learning classifier systems. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, pp. 863–870. ACM (2012)Google Scholar
  7. 7.
    Iqbal, M., Browne, W.N., Zhang, M.: Evolving optimum populations with XCS classifier systems. Soft. Comput. 17(3), 503–518 (2013)CrossRefGoogle Scholar
  8. 8.
    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 15th Annual Conference on Genetic and Evolutionary Computation, pp. 1045–1052. ACM (2013)Google Scholar
  9. 9.
    Iqbal, M., Browne, W.N., Zhang, M.: Learning overlapping natured and niche imbalance boolean problems using XCS classifier systems. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 1818–1825. IEEE (2013)Google Scholar
  10. 10.
    Iqbal, M., Browne, W.N., Zhang, M.: Reusing building blocks of extracted knowledge to solve complex, large-scale boolean problems. IEEE Trans. Evol. Comput. 18(4), 465–480 (2014)CrossRefGoogle Scholar
  11. 11.
    Iqbal, M., Naqvi, S.S., Browne, W.N., Hollitt, C., Zhang, M.: Salient object detection using learning classifiersystems that compute action mappings. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation GECCO 2014, pp. 525–532 (2014)Google Scholar
  12. 12.
    Lanzi, P.L.: Mining interesting knowledge from data with the XCS classifier system. In: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, pp. 958–965. Morgan Kaufmann Publishers Inc. (2001)Google Scholar
  13. 13.
    Liu, Y., Iqbal, M., Alvarez, I., Browne, W.N.: Integration of code-fragment based learning classifier systems for multiple domain perception and learning. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 2177–2184 (2016)Google Scholar
  14. 14.
    Orriols-Puig, A., Bernadó-Mansilla, E.: A further look at UCS classifier system. In: GECCO06, pp. 8–12 (2006)Google Scholar
  15. 15.
    Urbanowicz, R.J., Granizo-Mackenzie, A., Moore, J.H.: An analysis pipeline with statistical and visualization-guided knowledge discovery for Michigan-style learning classifier systems. Comput. Intell. Mag. 7(4), 35–45 (2012)CrossRefGoogle Scholar
  16. 16.
    Urbanowicz, R.J., Browne, W.N.: Introduction to Learning Classifier Systems. Springer, Heidelberg (2017)CrossRefGoogle Scholar
  17. 17.
    Urbanowicz, R.J., Moore, J.H.: Exstracs 2.0: description and evaluation of a scalable learning classifier system. Evol. Intell. 8(2–3), 89–116 (2015)CrossRefGoogle Scholar
  18. 18.
    Wilson, S.W.: Classifier fitness based on accuracy. Evol. Comput. 3(2), 149–175 (1995)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Victoria University of WellingtonWellingtonNew Zealand

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