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Analysis of Schema Frequencies in Genetic Programming

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Abstract

Genetic Programming (GP) schemas are structural templates equivalent to hyperplanes in the search space. Schema theories provide information about the properties of subsets of the population and the behavior of genetic operators. In this paper we propose a practical methodology to identify relevant schemas and measure their frequency in the population. We demonstrate our approach on an artificial symbolic regression benchmark where the parts of the formula are already known. Experimental results reveal how solutions are assembled within GP and explain diversity loss in GP populations through the proliferation of repeated patterns.

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References

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Acknowledgements

The work described in this paper was done within the COMET Project Heuristic Optimization in Production and Logistics (HOPL), #843532 funded 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

    Bogdan Burlacu, Michael Affenzeller, Michael Kommenda, Gabriel Kronberger & Stephan Winkler

  2. Institute for Formal Models and Verification, Johannes Kepler University, Altenbergerstr. 69, 4040, Linz, Austria

    Bogdan Burlacu, Michael Affenzeller & Michael Kommenda

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

Correspondence to Bogdan Burlacu .

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Editors and Affiliations

  1. University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Roberto Moreno-Díaz

  2. Johannes Kepler University Linz, Linz, Austria

    Franz Pichler

  3. University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Alexis Quesada-Arencibia

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Burlacu, B., Affenzeller, M., Kommenda, M., Kronberger, G., Winkler, S. (2018). Analysis of Schema Frequencies in Genetic Programming. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2017. EUROCAST 2017. Lecture Notes in Computer Science(), vol 10671. Springer, Cham. https://doi.org/10.1007/978-3-319-74718-7_52

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  • DOI: https://doi.org/10.1007/978-3-319-74718-7_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74717-0

  • Online ISBN: 978-3-319-74718-7

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