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How to Choose Appropriate Function Sets for Gentic Programming

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Genetic Programming (EuroGP 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3003))

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Abstract

The choice of functions in a genetic program can have a significant effect on the GP’s performance, but there have been no systematic studies of how to select functions to optimize performance. In this paper, we investigate how to choose appropriate function sets for general genetic programming problems. For each problem multiple functions sets are tested. The results show that functions can be classified into function groups of equivalent functions. The most appropriate function set for a problem is one that is optimally diverse; a set that includes one function from each function group.

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References

  1. Angeline, P.J., Pollack, J.P.: Competitive Environments Evolve Better Solutions for Complex Tasks. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 264–270. Morgan Kaufmann, San Mateo (1993)

    Google Scholar 

  2. Gathercole, C., Ross, P.: Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness. In: Genetic Programming 1997: Proceedings of the Second Annual Conference, pp. 119–127. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  3. Keijzer, M.: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling. In: Proceedings of the Sixth European Conference on Genetic Programming, pp. 70–82. Springer, Essex (2003)

    Google Scholar 

  4. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  5. Koza, J.: A Genetic Approach to the Truck Backer Upper Problem and the Inter-Twined Spiral Problem. In: Proceedings of IJCNN International Joint Conference on Neural Networks, pp. 310–318. IEEE Press, Los Alamitos (1992)

    Chapter  Google Scholar 

  6. Soule, T., Heckendorn, R.B.: Function Sets in Genetic Programming. In: GECCO 2001: Proceedings of the Genetic and Evolutionary Computation Conference, p. 190. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Idaho, Moscow, ID, 83844-1010, USA

    Gang Wang & Terence Soule

Authors
  1. Gang Wang
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  2. Terence Soule
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Editor information

Editors and Affiliations

  1. No Affiliation,  

    Maarten Keijzer

  2. Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 02139, Cambridge, MA, USA

    Una-May O’Reilly

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

    Simon Lucas

  4. Centre of Informatics and Systems of the University of Coimbra,  

    Ernesto Costa

  5. Department of Computer Science, University of Idaho, ID 83844-1010, Moscow,  

    Terence Soule

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© 2004 Springer-Verlag Berlin Heidelberg

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Wang, G., Soule, T. (2004). How to Choose Appropriate Function Sets for Gentic Programming. In: Keijzer, M., O’Reilly, UM., Lucas, S., Costa, E., Soule, T. (eds) Genetic Programming. EuroGP 2004. Lecture Notes in Computer Science, vol 3003. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24650-3_18

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  • DOI: https://doi.org/10.1007/978-3-540-24650-3_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21346-8

  • Online ISBN: 978-3-540-24650-3

  • eBook Packages: Springer Book Archive

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