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A population precreening strategy for Kriging-assisted evolutionary algorithms [1]

The proposed algorithm generates multiple candidate populations in each generation and uses the multi-point expected improvement (qEI) [2] to prescreening these candidates for selecting the best population to work with.

The DACE toolbox [3] is used for building the Kriging models;

Reference

  1. Dawei Zhan, Huanlai Xing. A population prescreening strategy for Kriging-assisted evolutionary computation, IEEE Congress on Evolutionary Computation, 2021.doi: 10.1109/CEC45853.2021.9504976.
  2. David Ginsbourger, Rodolphe Le Riche, Laurent Carraro. Kriging Is Well-Suited to Parallelize Optimization, in Computational Intelligence in Expensive Optimization Problems, 2010, Springer Berlin Heidelberg. p. 131-162.
  3. Lophaven SN, Nielsen HB, and Sodergaard J, DACE - A MATLAB Kriging Toolbox, Technical Report IMM-TR-2002-12, Informatics and Mathematical Modelling, Technical University of Denmark, 2002. Available at: http://www2.imm.dtu.dk/projects/dace/.