Skip to main content

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

Part of the Lecture Notes in Computer Science book series (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.

Keywords

  • Offspring Survival (OS)
  • Early Stopping Criterion
  • Partial Solution Evaluation
  • Offspring Candidate Solutions
  • Symbolic Regression

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-74718-7_51
  • Chapter length: 8 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   79.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-74718-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   99.99
Price excludes VAT (USA)
Fig. 1.

References

  1. Affenzeller, M., Wagner, S.: Offspring selection: a new self-adaptive selection scheme for genetic algorithms. In: Ribeiro, B., Albrecht, R.F., Dobnikar, A., Pearson, D.W., Steele, N.C. (eds.) Adaptive and Natural Computing Algorithms, pp. 218–221. Springer, Vienna (2005). https://doi.org/10.1007/3-211-27389-1_52

    CrossRef  Google Scholar 

  2. Holland, J.H.: Adaption in Natural and Artifical Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  3. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Longman, Boston (1989)

    MATH  Google Scholar 

  4. Affenzeller, M.: A new approach to evolutionary computation: segregative genetic algorithms (SEGA). In: Mira, J., Prieto, A. (eds.) IWANN 2001. LNCS, vol. 2084, pp. 594–601. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45720-8_71

    CrossRef  Google Scholar 

  5. Affenzeller, M., Wagner, S.: SASEGASA: an evolutionary algorithm for retarding premature convergence by self-adaptive selection pressure steering. In: Mira, J., Álvarez, J.R. (eds.) IWANN 2003. LNCS, vol. 2686, pp. 438–445. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-44868-3_56

    CrossRef  Google Scholar 

  6. Affenzeller, M., Winkler, S.M., Kronberger, G., Kommenda, M., Burlacu, B., Wagner, S.: Gaining deeper insights in symbolic regression. In: Riolo, R., Moore, J.H., Kotanchek, M. (eds.) Genetic Programming Theory and Practice XI. GEC, pp. 175–190. Springer, New York (2014). https://doi.org/10.1007/978-1-4939-0375-7_10

    CrossRef  Google Scholar 

  7. Affenzeller, M., Beham, A., Vonolfen, S., Pitzer, E., Winkler, S.M., Hutterer, S., Kommenda, M., Kofler, M., Kronberger, G., Wagner, S.: Simulation-based optimization with HeuristicLab: practical guidelines and real-world applications. In: Mujica Mota, M., De La Mota, I.F., Guimarans Serrano, D. (eds.) Applied Simulation and Optimization, pp. 3–38. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15033-8_1

    Google Scholar 

  8. White, D.R., McDermott, J., Castelli, M., Manzoni, L., Goldman, B.W., Kronberger, G., Jaskowski, W., O’Reilly, U.M., Luke, S.: Better GP benchmarks: community survey results and proposals. Genet. Program. Evolvable Mach. 14, 3–29 (2013)

    CrossRef  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michael Affenzeller .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74717-0

  • Online ISBN: 978-3-319-74718-7

  • eBook Packages: Computer ScienceComputer Science (R0)