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Cartesian Genetic Programming

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Genetic Programming (EuroGP 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1802))

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Abstract

This paper presents a new form of Genetic Programming called Cartesian Genetic Programming in which a program is represented as an indexed graph. The graph is encoded in the form of a linear string of integers. The inputs or terminal set and node outputs are numbered sequentially. The node functions are also separately numbered. The genotype is just a list of node connections and functions. The genotype is then mapped to an indexed graph that can be executed as a program. Evolutionary algorithms are used to evolve the genotype in a symbolic regression problem (sixth order polynomial) and the Santa Fe Ant Trail. The computational effort is calculated for both cases. It is suggested that hit effort is a more reliable measure of computational efficiency. A neutral search strategy that allows the fittest genotype to be replaced by another equally fit genotype (a neutral genotype) is examined and compared with non-neutral search for the Santa Fe ant problem. The neutral search proves to be much more effective.

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Author information

Authors and Affiliations

  1. School of Computer Science, University of Birmingham, Birmingham, B15 2TT, England

    Julian F. Miller

  2. School of Computing, Napier University, 219 Colinton Road, Edinburgh, EH14 1DJ, Scotland

    Peter Thomson

Authors
  1. Julian F. Miller
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  2. Peter Thomson
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Editor information

Editors and Affiliations

  1. Department of Computing and Electronic Systems, University of Essex, Wivenhoe Park, CO4 3SQ, Colchester, UK

    Riccardo Poli

  2. Department of Computer Science, Memorial University of Newfoundland, Canada

    Wolfgang Banzhaf

  3. Departments of Mathematical, Biological Sciences and Computing and Electronic Systems, University of Essex, UK

    William B. Langdon

  4. School of Computer Science, The University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK

    Julian Miller

  5. Dept. of Physical Rescource Theory, Chalmers University of Technology, 412 96, Göteborg, Sweden

    Peter Nordin

  6. School of Computing, Information Systems and Mathematics, South Bank University, SE1 0AA, London, UK

    Terence C. Fogarty

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© 2000 Springer-Verlag Berlin Heidelberg

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Miller, J.F., Thomson, P. (2000). Cartesian Genetic Programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds) Genetic Programming. EuroGP 2000. Lecture Notes in Computer Science, vol 1802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-46239-2_9

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  • DOI: https://doi.org/10.1007/978-3-540-46239-2_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67339-2

  • Online ISBN: 978-3-540-46239-2

  • eBook Packages: Springer Book Archive

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