Skip to main content
SpringerLink
Log in

The identification and exploitation of dormancy in genetic programming

  • Original Paper
  • Published:
Genetic Programming and Evolvable Machines Aims and scope Submit manuscript

Abstract

In genetic programming, introns—fragments of code which do not contribute to the fitness of individuals—are usually viewed negatively, and much research has been undertaken into ways of minimising their occurrence or effects. However, identification and removal of introns is often computationally expensive and sometimes intractable. We have therefore focused our attention on one particular class of intron, which we refer to as dormant nodes. Mechanisms for locating such nodes are cheap to implement, and reveal that the presence of dormancy can be extensive. Once identified, dormancy can be exploited in at least three ways: improving execution efficiency, improving solution-finding performance, and simplifying program code. Experimentation shows that the gains to be had in all three cases can be significant.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. T. Soule, in Proceedings of EuroGP 2002, LNCS 2278, ed. by J.A. Foster et al. Exons and code growth in genetic programming, (Springer, Berlin, Heidelberg, 2002), pp. 142–151

  2. T. Soule, J.A. Foster, J. Dickinson, in Genetic Programming 1996: Proceedings of the First Annual Conference, ed. by J.R. Koza et al. Code growth in genetic programming, (MIT Press, Cambridge MA, 1996), pp. 215–223

  3. T. Soule, Code Growth in Genetic Programming. PhD Thesis, University of Idaho, 1998

  4. S. Luke, in Late Breaking Papers, Proceedings of Genetic and Evolutionary Computation Conference (GECCO), ed. by D. Whitley. Code growth is not caused by introns, (Las Vegas, USA, 2000), pp. 228–235

  5. J. Miller, in Late Breaking Papers, Proceedings Genetic and Evolutionary Computation Conference (GECCO), ed. by E.D. Goodman. What bloat? Cartesian genetic programming on boolean problems, (San Francisco, CA, USA, 2001), pp. 295–302

  6. P. Smith, K. Harries, Code growth, explicitly defined introns and alternative selection schemes. Evolutionary Computation 6(4), 339–360 (1998)

    Article  Google Scholar 

  7. J. Stevens, R. B. Heckendorn, T. Soule, in Proceedings of Genetic and Evolutionary Computation Conference (GECCO), ed. by H.-G. Beyer, U.-M. O’Reilly. Exploiting disruption aversion to control code bloat, (ACM Press, Washington, DC, New York, 2005), pp. 1605–1612

  8. S. Mahler, D. Robilliard, C. Fonlipt, in Proceedings of EuroGP 2005, LNCS 3447. Tarpeian bloat control and generalization accuracy, (Springer, Berlin, Heidelberg, 2005), pp. 203–214

  9. R. Poli, in Proceedings of EuroGP 2003, LNCS 2610. A simple but theoretically-motivated method to control bloat in genetic programming, (Springer, Berlin, Heidelberg, 2003), pp. 204–217

  10. S. Luke, L. Panait, A comparison of bloat control methods for genetic programming. Evolutionary Computation 14(3), 309–344 (2006)

    Article  Google Scholar 

  11. P. Nordin, F. Francone, W. Banzhaf, Explicitly defined introns and destructive crossover in genetic programming, in Advances in Genetic Programming, vol. 2, ed. by P.J. Angeline, K.E. Kinnear (MIT Press, Cambridge, MA, 1996), pp. 111–134

    Google Scholar 

  12. S. Carbajal, F. Martinez, in Genetic Programming and Evolvable Machines. Evolutive introns: a non-costly method of using introns in GP, vol. 2, no. 2, June 2001, pp. 111–122

  13. H. Iba, M. Terao, in Proceedings of Genetic and Evolutionary Computation Conference (GECCO), ed. by D. Whitley. Controlling effective introns for multi-agent learning by genetic programming, (Las Vegas, USA, 2000), pp. 419–426

  14. W.B. Langdon, T. Soule, R. Poli, J.A. Foster, The evolution of size and shape, in Advances in Genetic Programming, vol. 3, ed. by L. Spector, et al. (MIT Press, Cambridge, MA, 1999), pp. 163–190

    Google Scholar 

  15. T. Soule, J.A. Foster, in Proceedings 1998 IEEE International Conference on Evolutionary Computation. Removal bias: a new cause of code growth in tree based evolutionary programming, (IEEE Press, Anchorage, Alaska, USA, 1998), pp. 781–786

  16. T. Blickle, L. Thiele, in Genetic Algorithms within the Framework of Evolutionary Computation (Workshop at KI-94), ed. by J. Hopf. Genetic programming and redundancy, (Saarbrücken, 1994), pp. 33–38

  17. J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection (MIT Press, Cambridge, MA, 1992)

    MATH  Google Scholar 

  18. W.B. Langdon, Data structures and genetic programming, in Advances in Genetic Programming, vol. 2, ed. by P.J. Angeline, K.E. Kinnear (MIT Press, Cambridge, MA, 1996), pp. 395–414

    Google Scholar 

  19. D. Jackson, in Proceedings EuroGP 2005, LNCS 3447, ed. by M. Keijzer et al. Evolving defence strategies by genetic programming, (Springer, Berlin, Heidelberg, 2005), pp. 281–290

  20. D. Jackson, in Proceedings Genetic and Evolutionary Computation Conference (GECCO), ed. by H.-G Beyer, U.-M O’Reilly. Parsing and translation of expressions by genetic programming, (ACM Press, Washington, DC, New York, 2005), pp. 1681–1688

  21. J.R. Koza, Genetic Programming II: Automatic Discovery of Reusable Programs (MIT Press, Cambridge, MA, 1994)

    MATH  Google Scholar 

  22. L. Igel, K. Chellapilla, in Proceedings of Genetic and Evolutionary Computation Conference (GECCO), ed. by W. Banzhaf et al. Investigating the influence of depth and degree of genotypic change on fitness in genetic programming, (Morgan Kaufmann, Orlando, Fl, 1999), pp. 1061–1068

  23. T. Soule, J.A. Foster, Effects of code growth and parsimony pressure on populations in genetic programming. Evolutionary Computation 6(4), 293–309 (1998)

    Article  Google Scholar 

  24. T. Blickle, in Proceedings of PPSN IV, LNCS 1141. Evolving compact solutions in genetic programming: A case study, (Springer, Berlin, Heidelberg, 1996), pp. 564–573

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, UK

    David Jackson

Authors
  1. David Jackson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David Jackson.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jackson, D. The identification and exploitation of dormancy in genetic programming. Genet Program Evolvable Mach 11, 89–121 (2010). https://doi.org/10.1007/s10710-009-9086-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10710-009-9086-1

Keywords

Search

Navigation