Probabilistic Incremental Program Evolution: Stochastic search through program space

  • Rafał Sałustowicz
  • Jürgen Schmidhuber
Part II: Regular Papers

DOI: 10.1007/3-540-62858-4_86

Volume 1224 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Sałustowicz R., Schmidhuber J. (1997) Probabilistic Incremental Program Evolution: Stochastic search through program space. In: van Someren M., Widmer G. (eds) Machine Learning: ECML-97. ECML 1997. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), vol 1224. Springer, Berlin, Heidelberg

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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Copyright information

© Springer-Verlag 1997

Authors and Affiliations

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