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RGP: an open source genetic programming system for the R environment

Published:07 July 2010Publication History

ABSTRACT

RGP is a new genetic programming system based on the R environment. The system implements classical untyped tree-based genetic programming as well as more advanced variants including, for example, strongly typed genetic programming and Pareto genetic programming. It strives for high modularity through a consistent architecture that allows the customization and replacement of every algorithm component, while maintaining accessibility for new users by adhering to the "convention over configuration" principle. Typical GP applications are supported by standard R interfaces. For example, symbolic regression via GP is supported by the same "formula interface" as linear regression in R. RGP is freely available as an open source R package.

References

  1. W. Banzhaf, F. D. Francone, R. E. Keller, and P. Nordin. Genetic programming: an introduction: on the automatic evolution of computer programs and its applications. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. H. Barendregt, S. Abramsky, D. M. Gabbay, T. S. E. Maibaum, and H. P. Barendregt. Lambda calculi with types. In Handbook of Logic in Computer Science, pages 117--309. Oxford University Press, 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, editors. Empirical Methods for the Analysis of Optimization Algorithms. Springer, Berlin, Heidelberg, New York, 2009. In Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. R. Poli, W. B. Langdon, and N. F. McPhee. A field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk, 2008. (With con- tributions by J. R. Koza). Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2009. ISBN 3-900051-07-0.Google ScholarGoogle Scholar
  6. M. Schmidt and H. Lipson. Comparison of tree and graph encodings as function of problem complexity. In D. T. et. al., editor, GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, volume 2, pages 1674--1679, London, 7-11 July 2007. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Y. Sun, D. Wierstra, T. Schaul, and J. Schmidhuber. Efficient natural evolution strategies. In GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pages 539--546, New York, NY, USA, 2009. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. L. Tierney, A. J. Rossini, N. Li, and H. Sevcikova. snow: Simple Network of Workstations, 2009. R package version 0.3-3.Google ScholarGoogle Scholar

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    • Published in

      cover image ACM Conferences
      GECCO '10: Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
      July 2010
      1496 pages
      ISBN:9781450300735
      DOI:10.1145/1830761

      Copyright © 2010 Copyright is held by the author/owner(s)

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 7 July 2010

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