A Surrogate-Assisted Cooperative Co-evolutionary Algorithm Using Recursive Differential Grouping as Decomposition Strategy

Julien Blanchard, Charlotte Beauthier, Timoteo Carletti

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Résumé

Cooperative co-evolutionary algorithms, especially those able to uncover interaction structure between variables, have a great potential in optimizing large-scale problems. Nevertheless, they are expensive in terms of number of function evaluations and this issue can be quite problematic when dealing with computationally expensive optimization problems. An effective approach to deal with such problems lies in the exploitation of surrogate models. The latter ones work as cheap-to-evaluate alternatives to the expensive function reducing the computational cost, while still providing improved designs. This process, called surrogate-assisted optimization, is very effective on small-dimensional problems but is not suitable to solve large-scale problems due to the curse of dimensionality. In this paper, a new algorithm, taking benefit from cooperative coevolution and surrogate models, is introduced to efficiently solve high-dimensional, expensive and black-box problems. The proposed algorithm uses recursive differential grouping to perform an accurate problem decomposition. Experimental results are provided on a set of 1000-dimensional problems and show promising results.
langue originaleAnglais
Numéro d'article18888677
Pages (de - à)674-681
Nombre de pages8
journal2019 IEEE Congress on Evolutionary Computation (CEC)
Les DOIs
Etat de la publicationPublié - 8 août 2019
Evénement2019 IEEE Congress On Evolutionary Computation - Te Papa Tongarewa, Wellington, Nouvelle-Zélande
Durée: 10 juin 201913 juin 2019
http://cec2019.org/

mots-clés

  • global optimization
  • surrogate-assisted optimization
  • Large-scale optimization
  • high dimensional , expensive and black-box problems
  • cooperative co-evolutionary algorithm
  • differential grouping
  • evolutionary algorithm

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    CHAMPAGNE, B., Lazzaroni, R., Geuzaine , C., Chatelain, P. & Knaepen, B.

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