Skip to main content
SpringerLink
Log in

LQ-R-SHADE: R-SHADE with Quadratic Surrogate Model

  • Conference paper
  • First Online:
Artificial Intelligence and Soft Computing (ICAISC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13588))

Included in the following conference series:

Abstract

The application of evolutionary algorithms in continuous optimization is a well-studied area of research. Nevertheless, recently there have been numerous works associated with surrogate-assisted approaches. This paper introduces LQ-R-SHADE: R-SHADE extended with a quadratic surrogate model. The principles of LQ-R-SHADE and its enhancements over the base R-SHADE are discussed in detail. The extension consists of the three main components: an archive of samples, a prescreening meta-model, and an initialization supported by the meta-model. In order to take advantage of the meta-model utilization as early as possible, a cascade of models is proposed: linear, quadratic, and quadratic with interactions. The proposed algorithm relies on multiple generation of mutated versions of each individual. The prescreening meta-model is then applied to select the most promising candidates for further evaluation with the use of a (costly) true fitness function. The performance of LQ-R-SHADE is evaluated on the well-known COCO BBOB benchmark and compared with the baseline R-SHADE method and its extension SHADE-LM, showing the advantage of the proposed algorithm. Besides numerical assessment, the impact of particular meta-model components on the obtained results is examined.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    LQ-R-RSHADE is maintained as an open source project available at: https://bitbucket.org/mateuszzaborski/lqrshade/.

References

  1. COCO data archives. https://numbbo.github.io/data-archive/. Accessed 14 Jan 2021

  2. Auger, A., Hansen, N.: A restart CMA evolution strategy with increasing population size. In: 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1769–1776. IEEE (2005)

    Google Scholar 

  3. Auger, A., Schoenauer, M., Vanhaecke, N.: LS-CMA-ES: a second-order algorithm for covariance matrix adaptation. In: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 182–191. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30217-9_19

    Chapter  Google Scholar 

  4. Bajer, L., Pitra, Z., Repickỳ, J., Holeňa, M.: Gaussian process surrogate models for the CMA evolution strategy. Evol. Comput. 27(4), 665–697 (2019)

    Article  Google Scholar 

  5. Can, B., Heavey, C.: A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models. Comput. Oper. Res. 39(2), 424–436 (2012)

    Article  Google Scholar 

  6. Fang, H., Horstemeyer, M.F.: Global response approximation with radial basis functions. Eng. Optim. 38(04), 407–424 (2006)

    Article  MathSciNet  Google Scholar 

  7. Finck, S., Hansen, N., Ros, R., Auger, A.: Real-parameter black-box optimization benchmarking 2009: Presentation of the noiseless functions. Technical Report, Citeseer (2010)

    Google Scholar 

  8. Hansen, N.: Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed. In: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, pp. 2389–2396 (2009)

    Google Scholar 

  9. Hansen, N.: A global surrogate assisted CMA-ES. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 664–672 (2019)

    Google Scholar 

  10. Hansen, N., Auger, A., Ros, R., Mersmann, O., Tušar, T., Brockhoff, D.: COCO: a platform for comparing continuous optimizers in a black-box setting. Optim. Methods Softw. 36(1), 114–144 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  11. Holland, J.H., et al.: Adaptation in Natural and Artificial Systems: an Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992)

    Google Scholar 

  12. Kern, S., Hansen, N., Koumoutsakos, P.: Local meta-models for optimization using evolution strategies. In: Runarsson, T.P., Beyer, H.-G., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 939–948. Springer, Heidelberg (2006). https://doi.org/10.1007/11844297_95

    Chapter  Google Scholar 

  13. Kleijnen, J.P.: Kriging metamodeling in simulation: a review. Eur. J. Oper. Res. 192(3), 707–716 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  14. Okulewicz, M., Zaborski, M.: Benchmarking SHADE algorithm enhanced with model based optimization on the BBOB noiseless testbed. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1259–1266 (2021)

    Google Scholar 

  15. Ren, Z., et al.: Surrogate model assisted cooperative coevolution for large scale optimization. Appl. Intell. 49(2), 513–531 (2019)

    Article  Google Scholar 

  16. Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  17. Tanabe, R., Fukunaga, A.: Success-history based parameter adaptation for differential evolution. In: 2013 IEEE congress on evolutionary computation, pp. 71–78. IEEE (2013)

    Google Scholar 

  18. Tanabe, R., Fukunaga, A.: Tuning differential evolution for cheap, medium, and expensive computational budgets. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 2018–2025. IEEE (2015)

    Google Scholar 

  19. Weisberg, S.: Applied Linear Regression. Wiley, New York (2013)

    Google Scholar 

  20. Yamaguchi, T., Akimoto, Y.: Benchmarking the novel CMA-ES restart strategy using the search history on the BBOB noiseless testbed. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1780–1787 (2017)

    Google Scholar 

  21. Zaborski, M., Mańdziuk, J.: Improving LSHADE by means of a pre-screening mechanism. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2022, pp. 884–892. Association for Computing Machinery, New York, USA (2022). ISBN 9781450392372. https://doi.org/10.1145/3512290.3528805

  22. Zaborski, M., Okulewicz, M., Mańdziuk, J.: Analysis of statistical model-based optimization enhancements in generalized self-adapting particle swarm optimization framework. Found. of Comput. Decis. Sci. 45(3), 233–254 (2020)

    Article  Google Scholar 

  23. Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)

    Article  Google Scholar 

Download references

Acknowledgments

Studies were funded by BIOTECHMED-1 project granted by Warsaw University of Technology under the program Excellence Initiative: Research University (ID-UB).

Author information

Authors and Affiliations

  1. Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, 00-662, Warsaw, Poland

    Mateusz Zaborski & Jacek Mańdziuk

Authors
  1. Mateusz Zaborski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jacek Mańdziuk
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mateusz Zaborski .

Editor information

Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zaborski, M., Mańdziuk, J. (2023). LQ-R-SHADE: R-SHADE with Quadratic Surrogate Model. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13588. Springer, Cham. https://doi.org/10.1007/978-3-031-23492-7_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-23492-7_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23491-0

  • Online ISBN: 978-3-031-23492-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation