An Adaptive GP Strategy for Evolving Digital Circuits

  • Mihai Oltean
  • Laura Dioşan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5179)


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.


Genetic Programming Digital Circuit Cartesian Genetic Programming Genetic Programming Algorithm Half Adder 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 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. 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. 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. 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)CrossRefGoogle Scholar
  5. 5.
    Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter Control in Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation 3(2), 124–141 (1999)CrossRefGoogle Scholar
  6. 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. 7.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  8. 8.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  9. 9.
    Grosan, C., Oltean, M.: Adaptive Representation for Single Objective Optimization. Soft Computing 9(8), 594–605 (2005)CrossRefGoogle Scholar
  10. 10.
    Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Subprograms. MIT Press, Cambridge (1994)Google Scholar
  11. 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)zbMATHCrossRefGoogle Scholar
  12. 12.
    Oltean, M., Diosan, L.: An autonomous GP-based system for regression and classification problems. Applied Soft Computing (in press, 2008)Google Scholar
  13. 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. 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)CrossRefGoogle Scholar
  15. 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)zbMATHCrossRefGoogle Scholar
  16. 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. 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. 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. 19.
    Wolpert, D.H., McReady, W.G.: No Free Lunch Theorems for Optimisation. IEEE Transaction on Evolutionary Computation 1, 67–82 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Mihai Oltean
    • 1
  • Laura Dioşan
    • 1
    • 2
  1. 1.Department of Computer Science, Faculty of Mathematics and Computer ScienceBabeş-Bolyai UniversityCluj-NapocaRomania
  2. 2.Laboratoire d’Informatique, de Traitement de l’Information et des Systèmes, EA 4108 Institut National des Sciences Appliquées RouenFrance

Personalised recommendations