Performance enhanced genetic programming

  • Chris Clack
  • Tina Yu
Genetic Programming: Issues and Applications
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1213)


Genetic Programming is increasing in popularity as the basis for a wide range of learning algorithms. However, the technique has to date only been successfully applied to modest tasks because of the performance overheads of evolving a large number of data structures, many of which do not correspond to a valid program. We address this problem directly and demonstrate how the evolutionary process can be achieved with much greater efficiency through the use of a formally-based representation and strong typing. We report initial experimental results which demonstrate that our technique exhibits significantly better performance than previous work.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    P.J. Angeline. Genetic Programming and Emergent Intelligence. Advances in Genetic Programming, K.E. Kinnear, Jr. (ed.), MIT Press, Cambridge, MA, pp. 75–98, 1994.Google Scholar
  2. 2.
    S. Brave. Evolving Recursive Programs for Tree Search. Advances in Genetic Programming II, P.J. Angeline and K.E. Kinnear, Jr. (eds.), MIT Press, Cambridge, MA, pp. 203–220, 1996.Google Scholar
  3. 3.
    L. Cardelli. Basic Polymorphic Typechecking. Science of Computer Programming. Vol. 8, pp. 147–172, 1987.CrossRefGoogle Scholar
  4. 4.
    A.L. Cox, Jr., L. Davis, & Y. Qiu. Dynamic Anticipatory Routing in Circuit-Switched Telecommunications Networks. Handbook of Genetic Algorithms. L. Davis (ed.), Van Nostrand Reinhold, New York, pp. 124–143, 1991.Google Scholar
  5. 5.
    K.E. Kinnear, Jr. Alternatives in Automatic Function Definition: A Comparison of Performance. Advances in Genetic Programming. K.E. Kinnear, Jr.(ed.), MIT Press, Cambridge, MA, pp. 119–141, 1994.Google Scholar
  6. 6.
    J.R. Koza. Hierarchical Genetic Algorithms Operating on Populations of Computer Programs. Proceedings of the 11th International Conference on Artificial Intelligence, Morgan Kaufmann, San Mateo, CA, Vol. I, pp 768–774, 1989.Google Scholar
  7. 7.
    J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, 1992.Google Scholar
  8. 8.
    J. R. Koza. Genetic Programming II, MIT Press, Cambridge, MA, 1994.Google Scholar
  9. 9.
    R. Milner. A Theory of Type Polymorphism in Programming. Journal of Computer and System Sciences, Vol. 17, pp. 348–375, 1978.CrossRefGoogle Scholar
  10. 10.
    D.J. Montana. Strongly Typed Genetic Programming. Journal of Evolutionary Computation, Vol. 3:3, pp. 199–230. 1995.Google Scholar
  11. 11.
    J.A. Robinson. A Machine-Oriented Logic Based on the Resolution Principle. Journal of ACM. Vol. 12:1, pp. 23–49, January 1965.CrossRefGoogle Scholar
  12. 12.
    G. Syswerda. Uniform Crossover in Genetic Algorithms. Proceedings of the Third International Conference on Genetic Algorithms and Their Applications, J.D. Schaffer (ed.), Morgan Kaufmann, San Mateo, CA, pp. 2–9, 1989.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Chris Clack
    • 1
  • Tina Yu
    • 1
  1. 1.Department of Computer ScienceUniversity College LondonLondonEngland

Personalised recommendations