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Gaining Deeper Insights in Symbolic Regression

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Book cover Genetic Programming Theory and Practice XI

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

Abstract

A distinguishing feature of symbolic regression using genetic programming is its ability to identify complex nonlinear white-box models. This is especially relevant in practice where models are extensively scrutinized in order to gain knowledge about underlying processes. This potential is often diluted by the ambiguity and complexity of the models produced by genetic programming. In this contribution we discuss several analysis methods with the common goal to enable better insights in the symbolic regression process and to produce models that are more understandable and show better generalization. In order to gain more information about the process we monitor and analyze the progresses of population diversity, building block information, and even more general genealogy information. Regarding the analysis of results, several aspects such as model simplification, relevance of variables, node impacts, and variable network analysis are presented and discussed.

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Acknowledgements

The work described in this chapter was done within the Josef Ressel Center for Heuristic Optimization Heureka! sponsored by the Austrian Research Promotion Agency (FFG).

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Authors and Affiliations

  1. Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, Softwarepark 11, 4232, Hagenberg, Austria

    Michael Affenzeller, Stephan M. Winkler, Gabriel Kronberger, Michael Kommenda, Bogdan Burlacu & Stefan Wagner

Authors
  1. Michael Affenzeller
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  2. Stephan M. Winkler
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  3. Gabriel Kronberger
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  4. Michael Kommenda
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  5. Bogdan Burlacu
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  6. Stefan Wagner
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Corresponding author

Correspondence to Michael Affenzeller .

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Editors and Affiliations

  1. University of Michigan, Ann Arbor, Michigan, USA

    Rick Riolo

  2. Inst for Quantitative Biomedical Science, Dartmouth Medical School, Lebanon, New Hampshire, USA

    Jason H. Moore

  3. Evolved Analytics, Midland, Michigan, USA

    Mark Kotanchek

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Affenzeller, M., Winkler, S.M., Kronberger, G., Kommenda, M., Burlacu, B., Wagner, S. (2014). Gaining Deeper Insights in Symbolic Regression. In: Riolo, R., Moore, J., Kotanchek, M. (eds) Genetic Programming Theory and Practice XI. Genetic and Evolutionary Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0375-7_10

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  • DOI: https://doi.org/10.1007/978-1-4939-0375-7_10

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4939-0374-0

  • Online ISBN: 978-1-4939-0375-7

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