The Tree-String Problem: An Artificial Domain for Structure and Content Search

  • Steven Gustafson
  • Edmund K. Burke
  • Natalio Krasnogor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3447)

Abstract

This paper introduces the Tree-String problem for genetic programming and related search and optimisation methods. To improve the understanding of optimisation and search methods, we aim to capture the complex dynamic created by the interdependencies of solution structure and content. Thus, we created an artificial domain that is amenable for analysis, yet representative of a wide-range of real-world applications. The Tree-String problem provides several benefits, including: the direct control of both structure and content objectives, the production of a rich and representative search space, the ability to create tunably difficult and random instances and the flexibility for specialisation.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37(1) (August 1988)Google Scholar
  2. 2.
    Fox, D., Burgard, W., Kruppa, H., Thrun, S.: A probabilistic approach to collaborative multi-robot localization. Autonomous Robots 8(3), 325–344 (2000)CrossRefGoogle Scholar
  3. 3.
    Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)Google Scholar
  4. 4.
    Koza, J.R., et al.: Genetic Programming IV: Routine Human-Competitive Machine Intelligence. Kluwer Academic Publishers, Dordrecht (2003)MATHGoogle Scholar
  5. 5.
    Kushchu, I.: An evaluation of evolutionary generalisation in genetic programming. Artificial Intelligence Review 18(1), 3–14 (2002)CrossRefMATHGoogle Scholar
  6. 6.
    Daida, J.M., et al.: What makes a problem GP-hard? analysis of a tunably difficult problem in genetic programming. Genetic Programming and Evolvable Machines 2(2), 165–191 (2001)CrossRefMATHGoogle Scholar
  7. 7.
    Gustafson, S., Ekárt, A., Burke, E.K., Kendall, G.: Problem difficulty and code growth in genetic programming. Genetic Programming and Evolvable Hardware 5(3), 271–290 (2004)CrossRefGoogle Scholar
  8. 8.
    Daida, J.M., Li, H., Tang, R., Hilss, A.M.: What makes a problem GP-hard? validating a hypothesis of structural causes. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2724, pp. 1665–1677. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Gathercole, C., Ross, P.: An adverse interaction between crossover and restricted tree depth in genetic programming. In: Koza, J.R., et al. (eds.) Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, July 28–31, 1996, pp. 291–296. MIT Press, Cambridge (1996)Google Scholar
  10. 10.
    Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer, Berlin (2002)MATHGoogle Scholar
  11. 11.
    Punch, W.F., Zongker, D., Goodman, E.D.: The royal tree problem, a benchmark for single and multi-population genetic programming. In: Angeline, P.J., Kinnear Jr., K.E. (eds.) Advances in Genetic Programming 2, ch. 15, pp. 299–316. The MIT Press, Cambridge (1996)Google Scholar
  12. 12.
    O’Reilly, U.-M.: The impact of external dependency in genetic programming primitives. In: Proceedings of the IEEE World Congress on Computational Intelligence, Anchorage, AL, USA, May 5-9, 1998, pp. 306–311. IEEE Press, Los Alamitos (1998)CrossRefGoogle Scholar
  13. 13.
    O’Reilly, U.-M., Goldberg, D.E.: How fitness structure affects subsolution acquisition in genetic programming. In: Koza, J.R., et al. (eds.) Proceedings of the Third Annual Genetic Programming Conference, Madison, WI, USA, July 22-25, 1998, pp. 269–277. Morgan Kaufmann, San Francisco (1998)Google Scholar
  14. 14.
    Goldberg, D.E., O’Reilly, U.-M.: Where does the good stuff go, and why? how contextual semantics influence program structure in simple genetic programming. In: Banzhaf, W., Poli, R., Schoenauer, M., Fogarty, T.C. (eds.) EuroGP 1998. LNCS, vol. 1391, pp. 16–36. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  15. 15.
    Gustafson, S.: An Analysis of Diversity in Genetic Programming. PhD thesis, School of Computer Science and Information Technology, University of Nottingham, Nottingham, England (February 2004)Google Scholar
  16. 16.
    Juels, A., Wattenberg, M.: Stochastic hillclimbing as a baseline method for evaluating genetic algorithms. Technical Report Technical Report CSD-94-834. Computers Science Department, University of California at Berkeley, USA (1995)Google Scholar
  17. 17.
    de Jong, E.D., Pollack, J.B.: Multi-objective methods for tree size control. Genetic Programming and Evolvable Machines 4(3), 211–233 (2003)CrossRefGoogle Scholar
  18. 18.
    Ekárt, A., Gustafson, S.: A data structure for improved GP analysis via efficient computation and visualisation of population measures. In: Keijzer, M., O’Reilly, U.-M., Lucas, S., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 35–46. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Steven Gustafson
    • 1
  • Edmund K. Burke
    • 1
  • Natalio Krasnogor
    • 1
  1. 1.School of Computer Science & ITUniversity of NottinghamNottinghamUnited Kingdom

Personalised recommendations