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Genetic Programming Theory and Practice X pp 155–169Cite as

A Framework for the Empirical Analysis of Genetic Programming System Performance

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Part of the book series: Genetic and Evolutionary Computation ((GEVO))

Abstract

This chapter introduces a framework for statistically sound, reproducible empirical research in Genetic Programming (GP). It provides tools to understand GP algorithms and heuristics and their interaction with problems of varying difficulty. Following an approach where scientific claims are broken down to testable statistical hypotheses and GP runs are treated as experiments, the framework helps to achieve statistically verified results of high reproducibility.

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Notes

  1. 1.

    The RGP package is available via the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/ or directly at http://rsymbolic.org/projects/show/rgp.

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Acknowledgements

This work was supported by the Bundesministerium für Bildung und Forschung (BMBF) under the grant FIWA (AiF FKZ 17N2309). Many thanks to Boris Naujoks, Tobias Brandt, and Jörg Stork for valuable ideas and suggestions.

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Authors and Affiliations

  1. Cologne University of Applied Sciences, Cologne, Germany

    Oliver Flasch & Thomas Bartz-Beielstein

Authors
  1. Oliver Flasch
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  2. Thomas Bartz-Beielstein
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Corresponding author

Correspondence to Oliver Flasch .

Editor information

Editors and Affiliations

  1. Center for the Study of Complex Systems, University of Michigan, West Hall 323, Ann Arbor, 48109, Michigan, USA

    Rick Riolo

  2. Evolved Analytics Europe BVBA, Vossenlaan, Beerse, 2340, Belgium

    Ekaterina Vladislavleva

  3. , Department of Biochemistry and Molecular, The Pennsylvania State University, Wartik Laboratory, University Park, 16802, Pennsylvania, USA

    Marylyn D Ritchie

  4. , Institute for Quantitative, Dartmouth Medical School, Medical Center Drive, Lebanon, 03756, New Hampshire, USA

    Jason H. Moore

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Flasch, O., Bartz-Beielstein, T. (2013). A Framework for the Empirical Analysis of Genetic Programming System Performance. In: Riolo, R., Vladislavleva, E., Ritchie, M., Moore, J. (eds) Genetic Programming Theory and Practice X. Genetic and Evolutionary Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6846-2_11

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  • DOI: https://doi.org/10.1007/978-1-4614-6846-2_11

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-6845-5

  • Online ISBN: 978-1-4614-6846-2

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