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Multi Objective Learning Classifier Systems Based Hyperheuristics for Modularised Fleet Mix Problem

  • Kamran Shafi
  • Axel Bender
  • Hussein A. Abbass
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7673)

Abstract

This paper presents an offline multi-objective hyperheuristic for the Modularised Fleet Mix Problem (MFMP) using Learning Classifier Systems (LCS). The LCS based hyperheuristic is built from multi-objective low-level heuristics that are derived from an existing MFMP solver. While the low-level heuristics use multi-objective evolutionary algorithms to search non-dominated solutions, the LCS based hyperheuristic applies the non-dominance concept at the primitive heuristic level. Two LCS, namely the eXtended Classifier System (XCS) and the sUpervised Classifier System (UCS) are augmented by multi-objective reward and accuracy functions, respectively. The results show that UCS performs better than XCS: the hyperheuristic learned by the UCS is able to select low-level heuristics which create MFMP solutions that, in terms of a distance-based convergence metric, are closer to the derived global Pareto curves on a large set of MFMP test scenarios than the solutions created by heuristics that are selected by the XCS hyperheuristic.

Keywords

fleet optimisation hyperheuristic learning classifier system multi-objective optimisation 

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References

  1. 1.
    Abbass, H., Bender, A.: The Pareto operating curve for risk minimization. Artificial Life and Robotics 14(4), 449–452 (2009)CrossRefGoogle Scholar
  2. 2.
    Baker, S., Bender, A., Abbass, H., Sarker, R.: A scenario-based evolutionary scheduling approach for assessing future supply chain fleet capabilities. In: Dahal, K., Tan, K., Cowling, P. (eds.) Evolutionary Scheduling. SCI, vol. 49, pp. 485–511. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Bernadó-Mansilla, E., Garrell-Guiu, J.: Accuracy-based learning classifier systems: models, analysis and applications to classification tasks. Evolutionary Computation 11(3), 209–238 (2003)CrossRefGoogle Scholar
  4. 4.
    Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyper-heuristics: An emerging direction in modern search technology. In: Handbook of Metaheuristics, pp. 457–474 (2003)Google Scholar
  5. 5.
    Khare, V.R., Yao, X., Deb, K.: Performance Scaling of Multi-objective Evolutionary Algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 376–390. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Ross, P.: Hyper-heuristics. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies, pp. 529–556. Springer, US (2005)CrossRefGoogle Scholar
  7. 7.
    Shafi, K., Bender, A., Abbass, H.: Fleet estimation for defence logistics using a multi-objective learning classifier system. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 1195–1202. ACM (2011)Google Scholar
  8. 8.
    Shafi, K., Kovacs, T., Abbass, H., Zhu, W.: Intrusion detection with evolutionary learning classifier systems. Natural Computing 8(1), 3–27 (2009)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Wilson, S.: Classifier fitness based on accuracy. Evolutionary Computation 3(2), 149–175 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kamran Shafi
    • 1
  • Axel Bender
    • 1
  • Hussein A. Abbass
    • 1
  1. 1.School of Engineering & Information TechnologyUniversity of New South WalesCanberraAustralia

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