ML Anthology

Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space

Rafal Salustowicz, Jürgen Schmidhuber
ECML-PKDD 1997 pp. 213-220
doi:10.1007/3-540-62858-4_86 /ecmlpkdd/1997/salustowicz1997ecml-probabilistic/

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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Cite

Text

Salustowicz and Schmidhuber. "Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space." European Conference on Machine Learning, 1997. doi:10.1007/3-540-62858-4_86

Markdown

[Salustowicz and Schmidhuber. "Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space." European Conference on Machine Learning, 1997.](https://mlanthology.org/ecmlpkdd/1997/salustowicz1997ecml-probabilistic/) doi:10.1007/3-540-62858-4_86

BibTeX

@inproceedings{salustowicz1997ecml-probabilistic,
  title     = {{Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space}},
  author    = {Salustowicz, Rafal and Schmidhuber, Jürgen},
  booktitle = {European Conference on Machine Learning},
  year      = {1997},
  pages     = {213-220},
  doi       = {10.1007/3-540-62858-4_86},
  url       = {https://mlanthology.org/ecmlpkdd/1997/salustowicz1997ecml-probabilistic/}
}