P-Tree programming

We propose a novel method for automatic program synthesis. P-Tree Programming represents the program search space through a single probabilistic prototype tree. From this prototype tree we form program instances which we evaluate on a given problem. The error values from the evaluations are propagat...

Full description

Saved in:
Bibliographic Details
Published in:SSCI : 2017 IEEE Symposium Series on Computational Intelligence : November 27, 2017-December 1, 2017 pp. 1 - 7
Main Author: Oesch, Christian
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2017
Subjects:
Benchmark testing
Open area test sites
Probability distribution
Programming
Prototypes
Sociology
Statistics
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract We propose a novel method for automatic program synthesis. P-Tree Programming represents the program search space through a single probabilistic prototype tree. From this prototype tree we form program instances which we evaluate on a given problem. The error values from the evaluations are propagated through the prototype tree. We use them to update the probability distributions that determine the symbol choices of further instances. The iterative method is applied to several symbolic regression benchmarks from the literature. It outperforms standard Genetic Programming to a large extent. Furthermore, it relies on a concise set of parameters which are held constant for all problems. The algorithm can be employed for most of the typical computational intelligence tasks such as classification, automatic program induction, and symbolic regression.
AbstractList We propose a novel method for automatic program synthesis. P-Tree Programming represents the program search space through a single probabilistic prototype tree. From this prototype tree we form program instances which we evaluate on a given problem. The error values from the evaluations are propagated through the prototype tree. We use them to update the probability distributions that determine the symbol choices of further instances. The iterative method is applied to several symbolic regression benchmarks from the literature. It outperforms standard Genetic Programming to a large extent. Furthermore, it relies on a concise set of parameters which are held constant for all problems. The algorithm can be employed for most of the typical computational intelligence tasks such as classification, automatic program induction, and symbolic regression.
Author Oesch, Christian
Author_xml – sequence: 1
  givenname: Christian
  surname: Oesch
  fullname: Oesch, Christian
  organization: University of Basel, Switzerland
BookMark eNotjk1Lw0AQQKegoK29eBMv_oHE2Y9MZo8laC0UFJp72ezOlhSTlk0v_nsFe3q3994cbsbTKABPCkul0L3uds2m1KjqkjUjWzeDuaoMk6412TtYTtMREZUjtrW5h8evos0iL-d8OmQ_DP14eIDb5L8nWV65gPb9rW0-iu3netOstkXv8FKYaChUZJ2y5DhYoc5Vxv5FU-Sg0cSYKHQqsU_edxQ8cpKOmLyWKkWzgOd_bS8i-3PuB59_9tdr8wtckjis
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/SSCI.2017.8280849
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Xplore
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Statistics
Computer Science
EISBN 1538627264
9781538627266
EndPage 7
ExternalDocumentID 8280849
Genre orig-research
GroupedDBID 6IE
6IF
6IK
6IL
6IN
AAJGR
AAWTH
ABLEC
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
IEGSK
OCL
RIE
RIL
ID FETCH-LOGICAL-i90t-3d36c564914698c4e6b9534280fd8c203ddf6cb1f8afaab6ca08feb686a2e5fd3
IEDL.DBID RIE
IngestDate Wed Aug 27 02:51:28 EDT 2025
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i90t-3d36c564914698c4e6b9534280fd8c203ddf6cb1f8afaab6ca08feb686a2e5fd3
PageCount 7
ParticipantIDs ieee_primary_8280849
PublicationCentury 2000
PublicationDate 2017-Nov.
PublicationDateYYYYMMDD 2017-11-01
PublicationDate_xml – month: 11
  year: 2017
  text: 2017-Nov.
PublicationDecade 2010
PublicationTitle SSCI : 2017 IEEE Symposium Series on Computational Intelligence : November 27, 2017-December 1, 2017
PublicationTitleAbbrev SSCI
PublicationYear 2017
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0001968473
Score 1.6441263
Snippet We propose a novel method for automatic program synthesis. P-Tree Programming represents the program search space through a single probabilistic prototype...
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms Benchmark testing
Open area test sites
Probability distribution
Programming
Prototypes
Sociology
Statistics
Title P-Tree programming
URI https://ieeexplore.ieee.org/document/8280849
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07a8MwED6S0CFTmkfpGw8dq0S2ZD3m0NAuwZAM2YIsnaBDk2KS_v5KtptQ6NJNCA5xJ6R73wfwhNI4z6UkFG1GuHGCaJcjUdxpZlmpc-Q12IRcLtVmo4sOPJ96YRCxLj7DaVzWuXy3t8cYKpsF74AqrrvQlVI0vVrneIoW4aNlbeIypXq2Ws3fYu2WnLZ0vwBUav2xGPzv5EuYnBvxkuKkYobQwd0IBj9IDEn7MEfQjzZjM3J5DMOCrCvEpC29-giUE1gvXtbzV9JCH5B3TQ-EOSZsLrhOI8Cj5SiC0FjwFKh3ymaUOeeFLVOvjDemFNZQ5bEUSpgMc-_YFfR2-x1eQ5J5FnW4Qx8sH4HUlKV23koTZWQZu4FxZHf72Qy32Lac3v69fQf9KNGmGe8eeofqiA9wYb8Cj9VjfSPfbNKMjw
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LawIxEB6sLdST9VH67h56bDRustnkLBWlVgT34E2yyQR6qBbR_v4mu1ul0EtvITCEmZDMez6AJ0y1dTxNCUUTE66tIMomSCS3ihmWqwR5ATaRzmZyuVTzGjwfemEQsSg-w15YFrl8uzH7ECrre--ASq5O4DThPKZlt9YxoqKE_2pZlbocUNVfLIaTUL2V9irKXxAqhQYZNf939gV0j6140fygZFpQw3Ubmj9YDFH1NNvQCFZjOXS5A605ybaIUVV89eEpu5CNXrLhmFTgB-Rd0R1hlgmTCK4GAeLRcBRebMz7CtRZaWLKrHXC5AMntdM6F0ZT6TAXUugYE2fZJdTXmzVeQRQ7FrS4RedtH4FU57myzqQ6yMgwdg2dwO7qsxxvsao4vfl7-xHOx9nbdDWdzF5voRGkW7bm3UF9t93jPZyZL8_v9qG4nW_xLY_W
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=SSCI+%3A+2017+IEEE+Symposium+Series+on+Computational+Intelligence+%3A+November+27%2C+2017-December+1%2C+2017&rft.atitle=P-Tree+programming&rft.au=Oesch%2C+Christian&rft.date=2017-11-01&rft.pub=IEEE&rft.spage=1&rft.epage=7&rft_id=info:doi/10.1109%2FSSCI.2017.8280849&rft.externalDocID=8280849