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Benchmarking Grammar-Based Genetic Programming Algorithms

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Research and Development in Intelligent Systems XXXI (SGAI 2014)

Abstract

The publication of Grammatical Evolution (GE) led to the development of numerous variants of this Grammar-Based approach to Genetic Programming (GP). In order for these variants to be compared, the community requires a rigorous means for benchmarking the algorithms. However, GP as a field is not known for the quality of its benchmarking, with many identified problems, making direct comparisons either difficult or impossible in many cases. Aside from there not being a single, agreed-upon, benchmarking test, the tests currently utilised have shown a lack of standardisation. We motivate the following research by identifying some key issues with current benchmarking approaches. We then propose a standardised set of metrics for future benchmarking and demonstrate the use of these metrics by running a comparison of three Grammar-Based Genetic Programming methods. We conclude the work by discussing the results and proposing directions for future benchmarking.

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Acknowledgments

The authors would like to thank HPC Wales for providing their facilities and technical support during the running of the experiments described in this research. Chris Headleand would also like to thank Fujitsu for their ongoing financial support.

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Authors and Affiliations

  1. Bangor University, Bangor, Wales

    Christopher J. Headleand, Llyr Ap Cenydd & William J. Teahan

Authors
  1. Christopher J. Headleand
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  2. Llyr Ap Cenydd
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  3. William J. Teahan
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Corresponding author

Correspondence to Christopher J. Headleand .

Editor information

Editors and Affiliations

  1. University of Portsmouth, Portsmouth, United Kingdom

    Max Bramer

  2. School of Computing, Eng & Mathematics, University of Brighton, Brighton, West Sussex, United Kingdom

    Miltos Petridis

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Headleand, C.J., Cenydd, L.A., Teahan, W.J. (2014). Benchmarking Grammar-Based Genetic Programming Algorithms. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXI. SGAI 2014. Springer, Cham. https://doi.org/10.1007/978-3-319-12069-0_9

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  • DOI: https://doi.org/10.1007/978-3-319-12069-0_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12068-3

  • Online ISBN: 978-3-319-12069-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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