Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space

Created by W.Langdon from gp-bibliography.bib Revision:1.7917

@InProceedings{Salustowicz:97ecml,

• author = "Rafal P. Salustowicz and Juergen Schmidhuber",
• title = "Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space",
• booktitle = "Machine Learning: ECML-97",
• editor = "Maarten {van Someren} and Gerhard Widmer",
• publisher = "Springer-Verlag",
• pages = "213--220",
• year = "1997",
• volume = "1224",
• series = "Lecture Notes in Artificial Intelligence",
• address = "Prague, Czech Republic",
• publisher_address = "Berlin",
• month = "23-26 " # apr,
• keywords = "genetic algorithms, genetic programming, Population-Based Incremental Learning, Stochastic Program Search",
• isbn13 = "978-3-540-62858-3",
• URL = "ftp://ftp.idsia.ch/pub/rafal/ECML_PIPE.ps.gz",
• DOI = "doi:10.1007/3-540-62858-4_86",
• size = "8 pages",
• 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) [ \cite{icga85:cramer} ; \cite{koza:book} ]. 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.",
• affiliation = "IDSIA Corso Elvezia 36 6900 Lugano Switzerland Corso Elvezia 36 6900 Lugano Switzerland",
• notes = "ECML-97",

}

Genetic Programming entries for Rafal Salustowicz Jurgen Schmidhuber

Citations