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Enhancing the Performance of GP Using an Ancestry-Based Mate Selection Scheme

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Genetic and Evolutionary Computation — GECCO 2003 (GECCO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2724))

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Abstract

The performance of genetic programming relies mostly on population-contained variation. If the population diversity is low then there will be a greater chance of the algorithm being unable to find the global optimum. We present a new method of approximating the genetic similarity between two individuals using ancestry information. We define a new diversity-preserving selection scheme, based on standard tournament selection, which encourages genetically dissimilar individuals to undergo genetic operation. The new method is illustrated by assessing its performance in a well-known problem domain: algebraic symbolic regression.

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References

  1. John R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, 1992.

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  4. L.J. Eshelman and J.D. Schaffer. Preventing premature convergence in genetic algorithms by preventing incest. In Proceedings of the Fourth International Conference on Genetic Algorithms, pages 115–122, San Diego, CA, 1991. Morgan Kaufmann.

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  5. Rob Craighurst and Worthy Martin. Enhancing ga performance through crossover prohibitions based on ancestry. In Larry J. Eshelman, editor, Proceedings of the Sixth International Conference on Genetic Algorithms, pages 130–135. Morgan Kaufmann, 1995.

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Author information

Authors and Affiliations

  1. Department of Electronics, University of York, UK

    Rodney Fry & Andy Tyrrell

Authors
  1. Rodney Fry
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  2. Andy Tyrrell
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Editor information

Editors and Affiliations

  1. Center for Applied Scientific Computing (CASC), Lawrence Livermore National Laboratory, 7000 East Avenue, L-561, Livermore, CA, 94550, USA

    Erick Cantú-Paz

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© 2003 Springer-Verlag Berlin Heidelberg

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Fry, R., Tyrrell, A. (2003). Enhancing the Performance of GP Using an Ancestry-Based Mate Selection Scheme. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_73

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  • DOI: https://doi.org/10.1007/3-540-45110-2_73

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40603-7

  • Online ISBN: 978-3-540-45110-5

  • eBook Packages: Springer Book Archive

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