%0 Conference Paper %1 Salustowicz:97ecml %A Salustowicz, R. P. %A Schmidhuber, J. %B Machine Learning: ECML-97 %D 1997 %E van Someren, M. %E Widmer, G. %I Springer-Verlag %K Incremental Learning, Population-Based Program Search Stochastic algorithms, genetic programming, %P 213--220 %T Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space %U ftp://ftp.idsia.ch/pub/rafal/ECML_PIPE.ps.gz %V 1224 %X 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) icga85:cramer ; 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.

@inproceedings{Salustowicz:97ecml, 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.}, added-at = {2008-06-19T17:46:40.000+0200}, author = {Salustowicz, R. P. and Schmidhuber, J.}, biburl = {https://www.bibsonomy.org/bibtex/261c547a179958a8d0b58833b4e1c0d8a/brazovayeye}, booktitle = {Machine Learning: ECML-97}, editor = {van Someren, M. and Widmer, G.}, interhash = {83a8cecbc3fa9f5de0bb5e5e65dd28a4}, intrahash = {61c547a179958a8d0b58833b4e1c0d8a}, keywords = {Incremental Learning, Population-Based Program Search Stochastic algorithms, genetic programming,}, notes = {ECML-97}, pages = {213--220}, publisher = {Springer-Verlag}, publisher_address = {Berlin}, series = {Lecture Notes in Artificial Intelligence}, size = {9 pages}, timestamp = {2008-06-19T17:50:58.000+0200}, title = {Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space}, url = {ftp://ftp.idsia.ch/pub/rafal/ECML_PIPE.ps.gz}, volume = 1224, year = 1997 }