Linear-Graph GP - A New GP Structure

  • Wolfgang Kantschik
  • Wolfgang Banzhaf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2278)


In recent years different genetic programming (GP) structures have emerged. Today, the basic forms of representation for genetic programs are tree, linear and graphstructures. In this contribution we introduce a new kind of GP structure which we call linear-graph. This is a further development to the linear-tree structure that we developed earlier. We describe the linear-graph structure, as well as crossover and mutation for this new GP structure in detail. We compare linear-graph programs withlinear and tree programs by analyzing their structure and results on different test problems.


Genetic Programming Linear Structure Result Register Node Edge Left Child 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    P.J. Angeline. Subtree crossover: Building block engine or macromutation? In Genetic Programming 1997: Proceedings of the Second Annual Conference, pages 9–17, San Francisco, CA, 1997. Morgan Kaufmann.Google Scholar
  2. 2.
    P.J. Angeline. Multiple interacting programs: A representation for evolving complex behaviors. Cybernetics and Systems (in press), 1998.Google Scholar
  3. 3.
    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 und dpunkt verlag, Heidelberg, 1998.zbMATHGoogle Scholar
  4. 4.
    M. Brameier and W. Banzhaf. Evolving teams of mutiple predictors with Genetic Programming. Genetic Programming and Evolvable Maschines, 2(4):381–407, 2001.zbMATHCrossRefGoogle Scholar
  5. 5.
    M. Brameier, P. Dittrich, W. Kantschik, and W. Banzhaf. SYSGP’ A C++ library of different GP variants. Technical Report Internal Report of SFB 531,ISSN 1433-3325, Fachbereich Informatik, Universität Dortmund, 1998.Google Scholar
  6. 6.
    J. Holland. Adaption in Natural and Artifical Systems. MI:The University of Michigan Press, 1975.Google Scholar
  7. 7.
    W. Kantschik and W. Banzhaf. Linear-tree GP and its comparison with other GP structures. In J. F. Miller, M. Tomassini, P. Luca Lanzi, C. Ryan, A. G. B. Tettamanzi, and W. B. Langdon, editors, Genetic Programming, Proceedings of EuroGP’2001, volume 2038 of LNCS, pages 302–312, Lake Como, Italy, 18-20 April 2001. Springer-Verlag.CrossRefGoogle Scholar
  8. 8.
    J. Koza. Genetic Programming. MIT Press, Cambridge, MA, 1992.zbMATHGoogle Scholar
  9. 9.
    J. Koza. Genetic Programming II. MIT Press, Cambridge, MA, 1994.zbMATHGoogle Scholar
  10. 10.
    J. P. Nordin. A Compiling Genetic Programming System that Directly Manipulates the Machine code. MIT Press, Cambridge, 1994.Google Scholar
  11. 11.
    Riccardo Poli. Evolution of graph-like programs with parallel distributed genetic programming. In Thomas Back, editor, Genetic Algorithms: Proceedings of the Seventh International Conference, pages 346–353, Michigan State University, East Lansing, MI, USA, 19-23 July 1997. Morgan Kaufmann.Google Scholar
  12. 12.
    A. Teller and M. Veloso. Pado: A new learning architecture for object recognition. In Symbolic Visual Learning, pages 81–116. Oxford University Press, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Wolfgang Kantschik
    • 1
  • Wolfgang Banzhaf
    • 1
    • 2
  1. 1.Dept. of Computer ScienceUniversity of DortmundDortmundGermany
  2. 2.Informatik Centrum Dortmund (ICD)DortmundGermany

Personalised recommendations