Exploratory Landscape Validation for Bayesian Optimization Algorithms

Taleh Agasiev () and Anatoly Karpenko
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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

Mathematics, 2024, vol. 12, issue 3, 1-21

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.

Keywords: Bayesian optimization; Gaussian process; surrogate modeling; hyperparameter tuning; exploratory landscape analysis; exploratory landscape validation; variability map of objective function (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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