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Use of Static Surrogates in Hyperparameter Optimization

Author

Listed:
  • Dounia Lakhmiri

    (Polytechnique Montréal GERAD)

  • Sébastien Digabel

    (Polytechnique Montréal GERAD)

Abstract

Optimizing the hyperparameters and architecture of a neural network is a long yet necessary phase in most applications. This consuming process can benefit from strategies designed to discard low-quality configurations and quickly focus on more promising candidates. This work aims at enhancing HyperNOMAD, a library that adapts a direct search derivative-free optimization algorithm to tune both the architecture and the training of a neural network simultaneously. Two static surrogates are developed to trigger an early stopping during the configuration evaluation and strategically rank a pool of candidates. These additions to HyperNOMAD are shown to reduce its resource consumption by orders of magnitude without harming the quality of the proposed solutions.

Suggested Citation

  • Dounia Lakhmiri & Sébastien Digabel, 2022. "Use of Static Surrogates in Hyperparameter Optimization," SN Operations Research Forum, Springer, vol. 3(1), pages 1-18, March.
    • Handle: RePEc:spr:snopef:v:3:y:2022:i:1:d:10.1007_s43069-022-00128-w
    DOI: 10.1007/s43069-022-00128-w
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