Evaluation of Neural Architecture Search Approaches for Offshore Platform Offset Prediction

  • Tomaz M. Suller USP
  • Eric O. Gomes USP
  • Henrique B. Oliveira USP
  • Lucas P. Cotrim USP
  • Amir M. Sa’ad USP
  • Ismael H. F. Santos Petrobras
  • Rodrigo A. Barreira Petrobras
  • Eduardo A. Tannuri USP
  • Edson S. Gomi USP
  • Anna H. R. Costa USP


This paper proposes a solution based on Multi-Layer Perceptron (MLP) to predict the offset of the center of gravity of an offshore platform. It also performs a comparative study with three optimization algorithms – Random Search, Simulated Annealing, and Bayesian Optimization (BO) – to find the best MLP architecture. Although BO obtained the best architecture in the shortest time, ablation studies developed in this paper with hyperparameters of the optimization process showed that the result is sensitive to them and deserves attention in the Neural Architecture Search process.


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SULLER, Tomaz M. et al. Evaluation of Neural Architecture Search Approaches for Offshore Platform Offset Prediction. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 18. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 326-337. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2021.18264.