Hyper-Parameter Optimization Using MARS Surrogate for Machine-Learning Algorithms

Research output: Contribution to journalArticlepeer-review


  • Yangyang Li
  • Guangyuan Liu
  • Gao Lu
  • Licheng Jiao
  • Ronghua Shang

Colleges, School and Institutes

External organisations

  • Xidian University
  • University of Birmingham


Automatically searching for optimal hyper parameters is of crucial importance for applying machine learning algorithms in practice. However, there are concerns regarding the tradeoff between efficiency and effectiveness of current approaches when faced with the expensive function evaluations. In this paper, a novel efficient hyper-parameter optimization algorithm is proposed (called MARSAOP), in which multivariate spline functions are used as surrogate and dynamic coordinate search approach is employed to generate the candidate points. Empirical studies on benchmark problems and machine-learning models (e.g., SVM, RF, and NN) demonstrate that the proposed algorithm is able to find relatively high-quality solutions for benchmark problems and excellent hyper-parameter configurations for machine-learning models using a limited computational budget (few function evaluations).

Bibliographic note

Publisher Copyright: © 2017 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.


Original languageEnglish
Article number8735959
Pages (from-to)287-297
Number of pages11
JournalIEEE Transactions on Emerging Topics in Computational Intelligence
Issue number3
Publication statusPublished - Jun 2020


  • dynamic coordinate search, Hyper-parameter optimization, machine learning, MARS