Advertisement

The impact of external dependency in genetic programming primitives

  • Una-May O'Reilly
Genetic Algorithms
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1418)

Abstract

The power of genetic programming arises from its ability to identify and promote appropriate subprograms of the “true” solution via its fitness based selection and inheritance mechanism (“survival of the fittest”) and then combine them via blind variation in terms of subtree crossover. Both control and data dependencies among primitives impact the behavioural consistency of subprograms in genetic programming solutions which in turn taxes the efficiency of selection. We present the results of modelling dependency through a parameterized problem in which a subprogram exhibits internal and external dependency levels that change as the subprogram is successively incorporated into larger subsolutions. We find that the key difference between non-existent and “full” external dependency when a solution is composed of subsolutions with exponentially scaled fitness contributions is a longer time to solution identification and a lower likelihood of success as shown by increased difficulty in identifying and promoting correct subprograms.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [Goldberg, 1989]
    Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA.Google Scholar
  2. [Koza, 1992]
    Koza, J. R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA.Google Scholar
  3. [Mitchell et al., 1992]
    Mitchell, M., Forrest, S., and Holland, J. H. (1992). The royal road for genetic algorithms: Fitness landscapes and GA performance. In Varela, F. J. and Bourgine, P., editors, Proceedings of the First European Conference on Artificial Life. Toward a Practice of Autonomous Systems, pages 245–254, Cambridge, MA. MIT Press.Google Scholar
  4. [O'Reilly and Oppacher, 1995]
    O'Reilly, U.-M. and Oppacher, F. (1995). The troubling aspects of a building block hypothesis for genetic programming. In Whitley, L. D. and Vose, M. D., editors, Foundations of Genetic Algorithms, volume 3, San Mateo, CA. Morgan Kaufmann.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Una-May O'Reilly
    • 1
  1. 1.The Artificial Intelligence LabMassachusetts Institute of TechnologyCambridgeUSA

Personalised recommendations