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On Dealing with Uncertainties from Kriging Models in Offline Data-Driven Evolutionary Multiobjective Optimization

  • Atanu MazumdarEmail author
  • Tinkle Chugh
  • Kaisa Miettinen
  • Manuel López-Ibáñez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11411)

Abstract

Many works on surrogate-assisted evolutionary multiobjective optimization have been devoted to problems where function evaluations are time-consuming (e.g., based on simulations). In many real-life optimization problems, mathematical or simulation models are not always available and, instead, we only have data from experiments, measurements or sensors. In such cases, optimization is to be performed on surrogate models built on the data available. The main challenge there is to fit an accurate surrogate model and to obtain meaningful solutions. We apply Kriging as a surrogate model and utilize corresponding uncertainty information in different ways during the optimization process. We discuss experimental results obtained on benchmark multiobjective optimization problems with different sampling techniques and numbers of objectives. The results show the effect of different ways of utilizing uncertainty information on the quality of solutions.

Keywords

Machine learning Gaussian process Pareto optimality Metamodelling Surrogate 

Notes

Acknowledgements

This research is related to the thematic research area Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO) at the University of Jyvaskyla. This work was partially supported by the Natural Environment Research Council [NE/P017436/1].

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Atanu Mazumdar
    • 1
    Email author
  • Tinkle Chugh
    • 2
  • Kaisa Miettinen
    • 1
  • Manuel López-Ibáñez
    • 3
  1. 1.University of Jyvaskyla, Faculty of Information TechnologyUniversity of JyvaskylaFinland
  2. 2.Department of Computer ScienceUniversity of ExeterExeterUK
  3. 3.Alliance Manchester Business SchoolUniversity of ManchesterManchesterUK

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