Skip to main content

An Adaptive GP Strategy for Evolving Digital Circuits

  • Conference paper
Knowledge-Based Intelligent Information and Engineering Systems (KES 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5179))

  • 3352 Accesses


The aim of this research is to develop an adaptive system for designing digital circuits. The investigated system, called Adaptive Genetic Programming (AdGP) contains most of the features required by an adaptive GP algorithm: it can decide the chromosome depth, the population size and the nodes of the GP tree which are the best suitable to provide the desired outputs. We have tested AdGP algorithm by solving some well-known problems in the field of digital circuits. Numerical experiments show that AdGP is able to perform very well on the considered test problems being able to successfully compete with standard GP having manually set parameters.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others


  1. Angeline, P.J.: Adaptive and Self-adaptive Evolutionary Computations. In: Palaniswami, M., Attikiouzel, Y. (eds.) Computational Intelligence: A Dynamic Systems Perspective, pp. 152–163. IEEE Press, Los Alamitos (1995)

    Google Scholar 

  2. Back, T.: Self-adaptation in Genetic Algorithms. In: Varela, F.J., Bourgine, P. (eds.) Toward a Practice of Autonomous Systems: Proceedings of the First European conference on Artificial Life, pp. 263–271. MIT Press, Cambridge (1992)

    Google Scholar 

  3. Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming - An Introduction; On the Automatic Evolution of Computer Programs and its Applications, 3rd edn. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  4. Colaco, M.J., Dulikravich, G.S., Martin, T.J.: Control of unsteady solidification via optimized magnetic fields. Materials and manufacturing processes 20(3), 435–458 (2005)

    Article  Google Scholar 

  5. Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter Control in Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation 3(2), 124–141 (1999)

    Article  Google Scholar 

  6. Fogel, L.J., Fogel, D.B., Angeline, P.J.: A Preliminary Investigation on Extending Evolutionary Programming to Include Self-adaptation on Finite State Machines. Informatica 18, 387–398 (1994)

    Google Scholar 

  7. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

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

    MATH  Google Scholar 

  9. Grosan, C., Oltean, M.: Adaptive Representation for Single Objective Optimization. Soft Computing 9(8), 594–605 (2005)

    Article  Google Scholar 

  10. Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Subprograms. MIT Press, Cambridge (1994)

    Google Scholar 

  11. Miller, J.F., Job, D., Vassilev, V.K.: Principles in the Evolutionary Design of Digital Circuits - Part I. Genetic Programming and Evolvable Machines 1(1), 7–35 (2000)

    Article  MATH  Google Scholar 

  12. Oltean, M., Diosan, L.: An autonomous GP-based system for regression and classification problems. Applied Soft Computing (in press, 2008)

    Google Scholar 

  13. Muntean, O., Dioşan, L., Oltean, M.: Solving the even-n-parity problems using Best Sub Tree Genetic Programming. In: AHS 2007, pp. 511–518 (2007)

    Google Scholar 

  14. Oltean, M., Groşan, C.: Evolving Digital Circuits using Multi Expression Programming. In: Zebulum, R., et al. (eds.) NASA/DoD Conference on Evolvable Hardware, Seatle, pp. 87–90. IEEE Press, NJ (2004)

    Chapter  Google Scholar 

  15. Poli, R., Page, J.: Solving high-order Boolean parity problems with smooth uniform crossover, sub-machine-code GP and demes. Genetic programming and evolvable machines 1, 37–56 (2000)

    Article  MATH  Google Scholar 

  16. Rosca, J.P., Ballard, D.H.: Genetic Programming with Adaptive Representations, Technical Report 489, University of Rochester, Computer Science Department (1994)

    Google Scholar 

  17. Shaefer, C.G.: The ARGOT System: Adaptive Representation Genetic Optimizing Technique. In: Grefenstette, J.J. (ed.) Proc. of the Second International Conference on Genetic Algorithms. Lawrence Erlbaum, Hillsdale (1987)

    Google Scholar 

  18. Teller, E.: Evolving programmers: The co-evolution of intelligent recombination operators. In: Angeline, P., Kinnear, K. (eds.) Advances in Genetic Programming, vol. 2 (1996)

    Google Scholar 

  19. Wolpert, D.H., McReady, W.G.: No Free Lunch Theorems for Optimisation. IEEE Transaction on Evolutionary Computation 1, 67–82 (1997)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Ignac Lovrek Robert J. Howlett Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Oltean, M., Dioşan, L. (2008). An Adaptive GP Strategy for Evolving Digital Circuits. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5179. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85566-8

  • Online ISBN: 978-3-540-85567-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics