Probabilistic Incremental Program Evolution: Stochastic search through program space

  • Rafał Sałustowicz
  • Jürgen Schmidhuber
Part II: Regular Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1224)

Abstract

Probabilistic Incremental Program Evolution (PIPE) is a novel technique for automatic program synthesis. We combine probability vector coding of program instructions [Schmidhuber, 1997], Population-Based Incremental Learning (PBIL) [Baluja and Caruana, 1995] and tree-coding of programs used in variants of Genetic Programming (GP) [Cramer, 1985; Koza, 1992]. PIPE uses a stochastic selection method for successively generating better and better programs according to an adaptive “probabilistic prototype tree”. No crossover operator is used. We compare PIPE to Koza's GP variant on a function regression problem and the 6-bit parity problem.

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References

  1. [Baluja and Caruana, 1995]
    Baluja, S. and Caruana, R. (1995). Removing the genetics from the standard genetic algorithm. In Prieditis, A. and Russell, S., editors, Machine Learning: Proceedings of the Twelfth International Conference, pages 38–46. Morgan Kaufmann Publishers, San Francisco, CA.Google Scholar
  2. [Cramer, 1985]
    Cramer, N. L. (1985). A representation for the adaptive generation of simple sequential programs. In Grefenstette, J., editor, Proceedings of an International Conference on Genetic Algorithms and Their Applications, Hillsdale NJ. Lawrence Erlbaum Associates.Google Scholar
  3. [Dickmanns et al., 1987]
    Dickmanns, D., Schmidhuber, J., and Winklhofer, A. (1987). Der genetische Algorithmus: Eine Implementierung in Prolog. Fortgeschrittenenpraktikum, Institut für Informatik, Lehrstuhl Prof. Radig, Technische Universität München.Google Scholar
  4. [Koza, 1992]
    Koza, J. R. (1992). Genetic Programming — On the Programming of Computers by Means of Natural Selection. MIT Press.Google Scholar
  5. [Schmidhuber, 1997]
    Schmidhuber, J. (1997). A general method for incremental self-improvement and multi-agent learning in unrestricted environments. In Yao, X., editor, Evolutionary Computation: Theory and Applications. Scientific Publ. Co., Singapore. In press.Google Scholar

Copyright information

© Springer-Verlag 1997

Authors and Affiliations

  • Rafał Sałustowicz
    • 1
  • Jürgen Schmidhuber
    • 1
  1. 1.IDSIALuganoSwitzerland

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