Skip to main content

Benefits of Employing an Implicit Context Representation on Hardware Geometry of CGP

  • Conference paper
Book cover Evolvable Systems: From Biology to Hardware (ICES 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3637))

Included in the following conference series:

Abstract

Cartesian Genetic Programming (CGP) has successfully been applied to the evolution of simple image processing filters and implemented in intrinsic evolvable hardware by the authors. However, conventional CGP exhibits the undesirable characteristic of positional dependence in which the specific location of genes within the chromosome has a direct or indirect influence on the phenotype. An implicit context representation of CGP (IRCGP) has been implemented by the authors which is positionally independent and outperforms conventional CGP in this application. This paper describes the additional benefits of IRCGP when considering alternative geometries for the hardware components. Results presented show that smaller hardware arrays under IRCGP are more robust and outperform equivalent arrays implemented in conventional CGP.

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

Access this chapter

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

    MATH  Google Scholar 

  2. Langdon, W.: Quadratic bloat in genetic programming. In: Whitley, D., Goldberg, D., Cantu-Paz, E. (eds.) Proceedings of the 2000 Genetic and Evolutionary Computation Conference, pp. 451–458 (2000)

    Google Scholar 

  3. Lones, M.A., Tyrrell, A.M.: Enzyme genetic programming. In: Kim, J.-H., Zhang, B.-T., Fogel, G., Kuscu, I. (eds.) Proc. 2001 Congress on Evolutionary Computation, vol. 2, pp. 1183–1190. IEEE Press, Los Alamitos (2001)

    Chapter  Google Scholar 

  4. Lones, M.A., Tyrrell, A.M.: Crossover and Bloat in the Functionality Model of Enzyme Genetic Programming. In: Proc. Congress on Evolutionary Computation 2002, pp. 986–992 (2002)

    Google Scholar 

  5. Lones, M.A., Tyrrell, A.M.: Biomimetic Representation with Enzyme Genetic Programming. Journal of Genetic Programming and Evolvable Machines 3(2), 193–217 (2002)

    Article  MATH  Google Scholar 

  6. Lones, M.A.: Enzyme Genetic Programming. PhD Thesis, University of York, UK (2003)

    Google Scholar 

  7. Lones, M.A., Tyrrell, A.M.: Modelling biological evolvability: implicit context and variation filtering in enzyme generic programming. BioSystems (2004)

    Google Scholar 

  8. Miller, J., Thomson, P.: Cartesian genetic programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 121–132. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  9. Miller, J.F., Job, D., Vasilev, V.K.: Principles in the evolutionary design of digital circuits—Part I. Genetic Programming and Evolvable Machines 1, 7–36 (2000)

    Article  MATH  Google Scholar 

  10. Schaffer, J., Morishima, A.: An adaptive crossover distribution mechanism for genetic algorithms. In: Proceedings of the Second International Conference on Genetic Algorithms and their Applications (1987)

    Google Scholar 

  11. Sekanina, L., Drabek, V.: Automatic Design of Image Operators Using Evolvable Hardware. Fifth IEEE Design and Diagnostic of Electronic Circuits and Systems, 132–139 (2002)

    Google Scholar 

  12. Sekanina, L.: Image Filter Design with Evolvable Hardware. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds.) EvoIASP 2002, EvoWorkshops 2002, EvoSTIM 2002, EvoCOP 2002, and EvoPlan 2002. LNCS, vol. 2279, pp. 255–266. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  13. Smith, S.L., Leggett, S., Tyrrell, A.M.: An Implicit Context Representation for Evolving Image Processing Filters. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 407–416. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  14. Yang, Z., Smith, S.L., Tyrrell, A.M.: Intrinsic Evolvable Hardware in Digital Filter Design. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 389–398. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  15. Yang, Z., Smith, S.L., Tyrrell, A.M.: Digital Circuit Design using Intrinsic Evolvable Hardware. In: Proceedings of 2004 NASA/DoD Conference on Evolvable Hardware, Seattle (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Cai, X., Smith, S.L., Tyrrell, A.M. (2005). Benefits of Employing an Implicit Context Representation on Hardware Geometry of CGP. In: Moreno, J.M., Madrenas, J., Cosp, J. (eds) Evolvable Systems: From Biology to Hardware. ICES 2005. Lecture Notes in Computer Science, vol 3637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11549703_14

Download citation

  • DOI: https://doi.org/10.1007/11549703_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28736-0

  • Online ISBN: 978-3-540-28737-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics