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European Conference on Genetic Programming

EuroGP 2005: Genetic Programming pp 311–320Cite as

mGGA: The meta-Grammar Genetic Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3447))

Abstract

A novel Grammatical Genetic Algorithm, the meta-Grammar Genetic Algorithm (mGGA) is presented. The mGGA borrows a grammatical representation and the ideas of modularity and reuse from Genetic Programming, and in particular an evolvable grammar representation from Grammatical Evolution by Grammatical Evolution. We demonstrate its application to a number of benchmark problems where significant performance gains are achieved when compared to static grammars.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Systems, University of Limerick, Ireland

    Michael O’Neill

  2. Department of Accountancy, University College Dublin, Ireland

    Anthony Brabazon

Authors
  1. Michael O’Neill
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  2. Anthony Brabazon
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Editor information

Editors and Affiliations

  1. No Affiliation,  

    Maarten Keijzer

  2. Dipartimento di Tecnologie dell’Informazione, Università di Milano Crema,  

    Andrea Tettamanzi

  3. Université Louis Pasteur, LSIIT, FDBT, Pôle API, F-67400, Illkirch, France

    Pierre Collet

  4. National e-Science Centre, University of Edinburgh, United Kingdom

    Jano van Hemert

  5. Institut des Systèmes d’Information (ISI), Université de Lausanne, Hautes Etudes Commerciales (HEC),  

    Marco Tomassini

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© 2005 Springer-Verlag Berlin Heidelberg

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Cite this paper

O’Neill, M., Brabazon, A. (2005). mGGA: The meta-Grammar Genetic Algorithm. In: Keijzer, M., Tettamanzi, A., Collet, P., van Hemert, J., Tomassini, M. (eds) Genetic Programming. EuroGP 2005. Lecture Notes in Computer Science, vol 3447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31989-4_28

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  • DOI: https://doi.org/10.1007/978-3-540-31989-4_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25436-2

  • Online ISBN: 978-3-540-31989-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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