Skip to main content
SpringerLink
Log in

What Makes a Problem GP-Hard? Validating a Hypothesis of Structural Causes

  • Conference paper
  • First Online:
Genetic and Evolutionary Computation — GECCO 2003 (GECCO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2724))

Included in the following conference series:

Abstract

This paper provides an empirical test of a hypothesis, which describes the effects of structural mechanisms in genetic programming. In doing so, the paper offers a test problem anticipated by this hypothesis. The problem is tunably difficult, but has this property because tuning is accomplished through changes in structure. Content is not involved in tuning. The results support a prediction of the hypothesis — that GP search space is significantly constrained as an outcome of structural mechanisms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. J.M. Daida, “Limits to Expression in Genetic Programming: Lattice-Aggregate Modeling,” in Proceedings of the 2002 Congress on Evolutionary Computation, vol. 1. Piscataway: IEEE, 2002, pp. 273–278.

    Chapter  Google Scholar 

  2. J.M. Daida and A. Hilss, “Identifying Structural Mechanisms in Standard GP,” in GECCO 2003.

    Google Scholar 

  3. L. Altenberg, “Emergent Phenomena in Genetic Programming,” in Evolutionary Programming — Proceedings, A.V. Sebald and L.J. Fogal, Eds. Singapore: World Scientific Publishing, 1994, pp. 233–241.

    Google Scholar 

  4. W.A. Tackett, “Recombination, Selection and the Genetic Construction of Computer Programs,” Ph.D. Dissertation, Electrical Engineering. Los Angeles: University of Southern California, 1994.

    Google Scholar 

  5. T. Soule, et al., “Code Growth in Genetic Programming,” in GP 1996 Proceedings, J. R. Koza, et al., Eds. Cambridge: The MIT Press, 1996, pp. 215–223.

    Google Scholar 

  6. T. Soule, et al., “Using Genetic Programming to Approximate Maximum Clique,” in GP 1996 Proceedings, J.R. Koza, et al. Eds. Cambridge: The MIT Press, 1996, pp. 400–405.

    Google Scholar 

  7. T. Soule and J.A. Foster, “Code Size and Depth Flows in Genetic Programming,” in GP 1997 Proceedings, J.R. Koza, et al. Eds. San Francisco: Morgan Kaufmann Publishers, 1997, pp. 313–320.

    Google Scholar 

  8. T. Soule and J.A. Foster, “Removal Bias: A New Cause of Code Growth in Tree Based Evolutionary Programming,” in 1998 IEEE ICEC Proceeding, vol. 1. Piscataway: IEEE Press, 1998, pp. 781–786.

    Google Scholar 

  9. W.B. Langdon, “Scaling of Program Fitness Spaces,” Evolutionary Computation, vol. 7, pp. 399–428, 1999.

    Article  Google Scholar 

  10. W.B. Langdon, “Size Fair and Homologous Tree Crossovers for Tree Genetic Programming,” GP and Evolvable Machines, vol. 1, pp. 95–119, 2000.

    Article  MATH  Google Scholar 

  11. W.B. Langdon, et al., “The Evolution of Size and Shape,” in Advances in Genetic Programming 3, L. Spector, et al., Eds. Cambridge: The MIT Press, 1999, pp. 163–190.

    Google Scholar 

  12. W.B. Langdon, “Size Fair and Homologous Tree Genetic Programming Crossover,” in GECCO 1999 Proceeding, vol. 2, W. Banzhaf, et al., Eds. San Francisco: Morgan Kaufmann Publishers, 1999, pp. 1092–1097.

    Google Scholar 

  13. U.-M. O’Reilly and D.E. Goldberg, “How Fitness Structure Affects Subsolution Acquisition in Genetic Programming,” in GP 1998 Proceedings, J. R. Koza, et al., Eds. San Francisco: Morgan Kaufmann Publishers, 1998, pp. 269–277.

    Google Scholar 

  14. D. E. Goldberg and U.-M. O’Reilly, “Where Does the Good Stuff Go, and Why?,” in Proceedings of the First European Conference on GP, W. Banzhaf, et al., Eds. Berlin: Springer-Verlag, 1998.

    Google Scholar 

  15. W. Punch, D. Zongker, and E. Goodman, “The Royal Tree Problem, A Benchmark for Single and Multiple Population Genetic Programming,” in Advances in Genetic Programming, vol. 2, P. J. Angeline and J. K.E. Kinnear, Eds. Cambridge: The MIT Press, 1996, pp. 299–316.

    Google Scholar 

  16. W. B. Langdon and R. Poli, Foundations of Genetic Programming. Berlin: Springer-Verlag, 2002.

    Book  MATH  Google Scholar 

  17. D. Zongker and W. Punch, “lilgp,” v. 1.02 ed. Lansing: Michigan State University Genetic Algorithms Research and Applications Group, 1995.

    Google Scholar 

  18. J. M. Daida, et al., “Analysis of Single-Node (Building) Blocks in Genetic Programming,” in Advances in Genetic Programming 3, L. Spector, et al., Eds. Cambridge: The MIT Press, 1999, pp. 217–241.

    Google Scholar 

  19. M. Matsumoto and T. Nishimura, “Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudorandom Number Generator,” ACM Transactions on Modeling and Computer Simulation, vol. 8, pp. 3–30, 1998.

    Article  MATH  Google Scholar 

  20. J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge: The MIT Press, 1992.

    MATH  Google Scholar 

  21. O. A. Chaudhri, et al., “Characterizing a Tunably Difficult Problem in Genetic Programming,” in GECCO 2000 Proceedings, L. D. Whitley, et al., Eds. San Francisco: Morgan Kaufmann Publishers, 2000, pp. 395–402.

    Google Scholar 

  22. J. M. Daida, et al., “What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming,” Journal of GP and Evolvable Hardware, vol. 2, pp. 165–191, 2001.

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center for the Study of Complex Systems and Space Physics Research Laboratory, The University of Michigan, 2455 Hayward Avenue, Ann Arbor, Michigan, 48109-2143

    Jason M. Daida, Hsiaolei Li, Ricky Tang & Adam M. Hilss

Authors
  1. Jason M. Daida
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hsiaolei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ricky Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adam M. Hilss
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Center for Applied Scientific Computing (CASC), Lawrence Livermore National Laboratory, 7000 East Avenue, L-561, Livermore, CA, 94550, USA

    Erick Cantú-Paz

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Daida, J.M., Li, H., Tang, R., Hilss, A.M. (2003). What Makes a Problem GP-Hard? Validating a Hypothesis of Structural Causes. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_60

Download citation

  • DOI: https://doi.org/10.1007/3-540-45110-2_60

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40603-7

  • Online ISBN: 978-3-540-45110-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation