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A comparison of quality measures for model selection in surrogate-assisted evolutionary algorithm

  • Haibo YuEmail author
  • Ying Tan
  • Chaoli Sun
  • Jianchao Zeng
Methodologies and Application
  • 21 Downloads

Abstract

Choosing a proper approximation model should be the first and the most fundamental problem to be solved when dealing with surrogate-assisted evolutionary algorithms. Till now, most of the model selection methods emphasize on obtaining the best surrogate model basing on model accuracy assessments. As the population ranking is of the most important part in evolutionary optimization, the target function of surrogate model should focus on the right ranking of candidate solutions. Therefore, in this paper, we make a comparison study on several model quality measures which basically dedicated to measuring the capability of surrogate model in selecting and ranking the candidate solutions. In order to investigate the compatibility between accuracy assessments and ranking correlation methods, four algorithms with different model selection strategies based on different quality measures are designed and comparative study is made by contrasting them to three specific surrogate-assisted evolutionary algorithms as well as the standard particle swarm optimization. Simulation results on ten commonly used benchmark problems and one engineering case demonstrate the efficacy of the designed model selection strategies and meanwhile provide further insight into the three model quality measures studied in this paper in model selection.

Keywords

Surrogate Model selection Bootstrap sampling Quality measure Particle swarm optimization 

Notes

Acknowledgements

This work is supported by National Natural Science Foundation of China (Grant Nos. 61472269 and 61403272) and the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, China, as well as the Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province.

Compliance with ethical standards

Conflict of interest

Author Haibo Yu declares that he has no conflict of interest. Author Ying Tan declares that she has no conflict of interest. Author Chaoli Sun declares that she has no conflict of interest. Author Jianchao Zeng declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mechanical EngineeringTaiyuan University of Science and TechnologyTaiyuanChina
  2. 2.Division of Industrial and System EngineeringTaiyuan University of Science and TechnologyTaiyuanChina
  3. 3.School of Computer Science and TechnologyTaiyuan University of Science and TechnologyTaiyuanChina
  4. 4.School of Computer Science and Control EngineeringNorth University of ChinaTaiyuanChina

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