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On the use of CBR in optimisation problems such as the TSP

  • Pádraig Cunningham
  • Barry Smyth
  • Neil Hurley
Poster Sessions
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1010)

Abstract

The particular strength of CBR is normally considered to be its use in weak theory domains where solution quality is compiled into cases and is reusable. In this paper we explore an alternative use of CBR in optimisation problems where cases represent highly optimised structures in a huge highly constrained solution space. Our analysis focuses on the Travelling Salesman Problem where difficulty arises from the computational complexity of the problem rather than any difficulty associated with the domain theory. We find that CBR is good for producing medium quality solutions in very quick time. We have difficulty getting CBR to produce high quality solutions because solution quality seems to be lost in the adaptation process. We also argue that experiments with CBR on transparent problems such as the TSP tell us a lot about aspects of CBR such as; the quality of CBR solutions, the coverage that cases in the case-base offer and the utility of extending a case-base.

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References

  1. Cunningham P., Browne J., (1986) A LISP-based heuristic scheduler for automatic insertion in electronics assembly, International Journal of Production Research, Vol.24, No.6, pp1395–1408.Google Scholar
  2. Goldberg D.E., (1989) Genetic Algorithms in Search Optimization & Machine Learning, Addison Wesley, Reading Massachusetts.Google Scholar
  3. Kirkpatrick S., Gelatt C.D., Vecchi M.P., (1983) Optimization by Simulated Annealing, Science, Vol. 220, No. 4597, pp671–680.Google Scholar
  4. Koton, P. (1989) SMARTplan: A Case-Based Resource Allocation and Scheduling System. Proceedings of the Case-Based Reasoning Workshop, pp 285–289, Florida, USA.Google Scholar
  5. Muñoz H., Paulokat J., Wess S., (1994) Controlling Nonlinear Hierarchical Planning by Case Replay, in working papers of the Second European Workshop on Case-based Reasoning, pp195-203, Chantilly, France.Google Scholar
  6. Norback J., Love R., (1977) Geometric Approaches to Solving the Travelling Salesman Problem, Management Science, July 1977, pp1208–1223.Google Scholar
  7. Smyth B., Cunningham P., (1992) Déjà Vu: A Hierarchical Case-Based Reasoning System for Software Design, in Proceedings of European Conference on Artificial Intelligence, ed. Bernd Neumann, John Wiley, pp587–589.Google Scholar
  8. Sycara, K. & Miyashita, K. (1994) Case-Based Acquisition of User Preferences for Solution Improvement in Ill-Structured Domains. Proceedings of the 12th National Conference on Artificial Intelligence, pp. 44–49. Seattle, USA.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Pádraig Cunningham
  • Barry Smyth
    • 1
  • Neil Hurley
    • 2
  1. 1.Department of Computer ScienceTrinity CollegeDublinIreland
  2. 2.Hitachi Dublin LaboratoryTrinity CollegeDublinIreland

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