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Exploratory Landscape Validation for Bayesian Optimization Algorithms

Author

Listed:
  • Taleh Agasiev

    (Department of Computer-Aided Design Systems, Bauman Moscow State Technical University, Moscow 105005, Russia)

  • Anatoly Karpenko

    (Department of Computer-Aided Design Systems, Bauman Moscow State Technical University, Moscow 105005, Russia)

Abstract

Bayesian optimization algorithms are widely used for solving problems with a high computational complexity in terms of objective function evaluation. The efficiency of Bayesian optimization is strongly dependent on the quality of the surrogate models of an objective function, which are built and refined at each iteration. The quality of surrogate models, and hence the performance of an optimization algorithm, can be greatly improved by selecting the appropriate hyperparameter values of the approximation algorithm. The common approach to finding good hyperparameter values for each iteration of Bayesian optimization is to build surrogate models with different hyperparameter values and choose the best one based on some estimation of the approximation error, for example, a cross-validation score. Building multiple surrogate models for each iteration of Bayesian optimization is computationally demanding and significantly increases the time required to solve an optimization problem. This paper suggests a new approach, called exploratory landscape validation, to find good hyperparameter values with less computational effort. Exploratory landscape validation metrics can be used to predict the best hyperparameter values, which can improve both the quality of the solutions found by Bayesian optimization and the time needed to solve problems.

Suggested Citation

  • Taleh Agasiev & Anatoly Karpenko, 2024. "Exploratory Landscape Validation for Bayesian Optimization Algorithms," Mathematics, MDPI, vol. 12(3), pages 1-21, January.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:3:p:426-:d:1328206
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    References listed on IDEAS

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    1. Jiangang Hao & Tin Kam Ho, 2019. "Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language," Journal of Educational and Behavioral Statistics, , vol. 44(3), pages 348-361, June.
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