skip to main content
10.1145/1389095.1389353acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
poster

TREAD: a new genetic programming representation aimed at research of long term complexity growth

Published: 12 July 2008 Publication History

Abstract

Several forms of computer program (or representation) have been proposed for Genetic Programming (GP) systems to evolve, such as linear, tree based or graph based. Typically, GP representations are highly effective during the initial search phases of evolution but stagnate before deep levels of complexity are acquired. A new representation, TREAD, is proposed to combine aspects of flow of execution and flow of data systems. The distinguishing features of TREAD are designed for researching improvements to the long term acquisition of novel features in GP (at the expense of the speed of the initial search if necessary). TREAD is validated on a symbolic regression problem and is found to be capable of successfully developing solutions through artificial evolution.

References

[1]
W. Banzhaf, P. Nordin, R. E. Keller, and F. D. Francone. Genetic Programming . An Introduction; On the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann, San Francisco, CA, USA, Jan. 1998.
[2]
J. F. Miller and P. Thomson. Cartesian genetic programming. In R. Poli, W. Banzhaf, W. B. Langdon, J. F. Miller, P. Nordin, and T. C. Fogarty, editors, Genetic Programming, Proceedings of EuroGP'2000, volume 1802 of LNCS, pages 121--132, Edinburgh, 15-16 Apr. 2000. Springer-Verlag.
[3]
A. Teller and M. Veloso. PADO: A new learning architecture for object recognition. In K. Ikeuchi and M. Veloso, editors, Symbolic Visual Learning, pages 81--116. Oxford University Press, 1996.

Cited By

View all
  • (2010)Tweaking a tower of blocks leads to a TMBL: Pursuing long term fitness growth in program evolutionIEEE Congress on Evolutionary Computation10.1109/CEC.2010.5586375(1-8)Online publication date: Jul-2010

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
July 2008
1814 pages
ISBN:9781605581309
DOI:10.1145/1389095
  • Conference Chair:
  • Conor Ryan,
  • Editor:
  • Maarten Keijzer
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2008

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. artificial intelligence
  2. genetic programming
  3. representations

Qualifiers

  • Poster

Conference

GECCO08
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 08 Mar 2025

Other Metrics

Citations

Cited By

View all
  • (2010)Tweaking a tower of blocks leads to a TMBL: Pursuing long term fitness growth in program evolutionIEEE Congress on Evolutionary Computation10.1109/CEC.2010.5586375(1-8)Online publication date: Jul-2010

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media