Exploratory Landscape Validation for Bayesian Optimization Algorithms
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 ref...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/12/3/426 |
| _version_ | 1797318451647217664 |
|---|---|
| author | Taleh Agasiev Anatoly Karpenko |
| author_facet | Taleh Agasiev Anatoly Karpenko |
| author_sort | Taleh Agasiev |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T03:52:35Z |
| format | Article |
| id | doaj.art-472caf2adec045dabf6cfc498129d551 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-08T03:52:35Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-472caf2adec045dabf6cfc498129d5512024-02-09T15:18:18ZengMDPI AGMathematics2227-73902024-01-0112342610.3390/math12030426Exploratory Landscape Validation for Bayesian Optimization AlgorithmsTaleh Agasiev0Anatoly Karpenko1Department of Computer-Aided Design Systems, Bauman Moscow State Technical University, Moscow 105005, RussiaDepartment of Computer-Aided Design Systems, Bauman Moscow State Technical University, Moscow 105005, RussiaBayesian 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.https://www.mdpi.com/2227-7390/12/3/426Bayesian optimizationGaussian processsurrogate modelinghyperparameter tuningexploratory landscape analysisexploratory landscape validation |
| spellingShingle | Taleh Agasiev Anatoly Karpenko Exploratory Landscape Validation for Bayesian Optimization Algorithms Mathematics Bayesian optimization Gaussian process surrogate modeling hyperparameter tuning exploratory landscape analysis exploratory landscape validation |
| title | Exploratory Landscape Validation for Bayesian Optimization Algorithms |
| title_full | Exploratory Landscape Validation for Bayesian Optimization Algorithms |
| title_fullStr | Exploratory Landscape Validation for Bayesian Optimization Algorithms |
| title_full_unstemmed | Exploratory Landscape Validation for Bayesian Optimization Algorithms |
| title_short | Exploratory Landscape Validation for Bayesian Optimization Algorithms |
| title_sort | exploratory landscape validation for bayesian optimization algorithms |
| topic | Bayesian optimization Gaussian process surrogate modeling hyperparameter tuning exploratory landscape analysis exploratory landscape validation |
| url | https://www.mdpi.com/2227-7390/12/3/426 |
| work_keys_str_mv | AT talehagasiev exploratorylandscapevalidationforbayesianoptimizationalgorithms AT anatolykarpenko exploratorylandscapevalidationforbayesianoptimizationalgorithms |