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International Conference on Computer Aided Systems Theory

EUROCAST 2017: Computer Aided Systems Theory – EUROCAST 2017 pp 424–431Cite as

Offspring Selection Genetic Algorithm Revisited: Improvements in Efficiency by Early Stopping Criteria in the Evaluation of Unsuccessful Individuals

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10671))

Abstract

This paper proposes some algorithmic extensions to the general concept of offspring selection which itself is an algorithmic extension of genetic algorithms and genetic programming. Offspring selection is characterized by the fact that many offspring solution candidates will not participate in the ongoing evolutionary process if they do not achieve the success criterion. The algorithmic enhancements proposed in this contribution aim to early estimate if a solution candidate will not be accepted based on partial solution evaluation. The qualitative characteristics of offspring selection are not affected by this means. The discussed variant of offspring selection is analyzed for several symbolic regression problems with offspring selection genetic programming. The achievable gains in terms of efficiency are remarkable especially for large data-sets.

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References

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Acknowledgments

The work described in this paper was done within the COMET Project #843532 Heuristic Optimization in Production and Logistics (HOPL) funded by the Austrian Research Promotion Agency (FFG) and the Government of Upper Austria and the COMET Project #843551 Advanced Engineering Design Automation (AEDA) funded by the Austrian Research Promotion Agency (FFG) and the Government of Vorarlberg.

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Authors and Affiliations

  1. Heuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg Campus, Softwarepark 11, 4232, Hagenberg, Austria

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

Authors
  1. Michael Affenzeller
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  2. Bogdan Burlacu
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  3. Stephan Winkler
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  4. Michael Kommenda
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  5. Gabriel Kronberger
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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 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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Affenzeller, M., Burlacu, B., Winkler, S., Kommenda, M., Kronberger, G., Wagner, S. (2018). Offspring Selection Genetic Algorithm Revisited: Improvements in Efficiency by Early Stopping Criteria in the Evaluation of Unsuccessful Individuals. 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_51

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  • DOI: https://doi.org/10.1007/978-3-319-74718-7_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74717-0

  • Online ISBN: 978-3-319-74718-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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