A Comparison of Cartesian Genetic Programming and Linear Genetic Programming

  • Garnett Wilson
  • Wolfgang Banzhaf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4971)

Abstract

Two prominent genetic programming approaches are the graph-based Cartesian Genetic Programming (CGP) and Linear Genetic Programming (LGP). Recently, a formal algorithm for constructing a directed acyclic graph (DAG) from a classical LGP instruction sequence has been established. Given graph-based LGP and traditional CGP, this paper investigates the similarities and differences between the two implementations, and establishes that the significant difference between them is each algorithm’s means of restricting inter-connectivity of nodes. The work then goes on to compare the performance of two representations each (with varied connectivity) of LGP and CGP to a directed cyclic graph (DCG) GP with no connectivity restrictions on a medical classification and regression benchmark.

Keywords

Linear Genetic Programming Cartesian Genetic Programming 

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References

  1. 1.
    Miller, J.F., Job, D., Vassilev, V.K.: Principles in the Evolutionary Design of Digital Circuits - Part 1. Genetic Programming and Evolvable Machines 1, 8–35 (2000)Google Scholar
  2. 2.
    Miller, J.F., Smith, S.L.: Redundancy and Computational Efficiency in Cartesian Genetic Programming. IEEE Transactions on Evolutionary Computation 10, 167–174 (2006)CrossRefGoogle Scholar
  3. 3.
    Miller, J.F., Thomson, P.: Cartesian Genetic Programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 121–132. Springer, Heidelberg (2000)Google Scholar
  4. 4.
    Banzhaf, W., Nordin, P., Keller, R., Francone, F.: Genetic Programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications. Morgan Kaufmann Publishers, San Francisco (1998)MATHGoogle Scholar
  5. 5.
    Nordin, P.: Evolutionary Program Induction of Binary Mchine Code and its Application. Krehl Verlag, Munster (1997)Google Scholar
  6. 6.
    Brameier, M., Banzhaf, W.: Linear Genetic Programming. Springer, New York (2007)MATHGoogle Scholar
  7. 7.
    Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases. University of California, Department of Information and Computer Science, http://www.ics.uci.edu/~mlearn/MLRepository.html
  8. 8.
    Heer, J.: Prefuse Interactive Information Visualization Toolkit, http://prefuse.org

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Garnett Wilson
    • 1
    • 2
  • Wolfgang Banzhaf
    • 1
  1. 1.Memorial Univeristy of NewfoundlandSt. John’sCanada
  2. 2.Verafin, Inc.St. John’sCanada

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