SMT 2.0: A Surrogate Modeling Toolbox with a focus on hierarchical and mixed variables Gaussian processes
Abstract The Surrogate Modeling Toolbox (SMT) is an open-source Python package that
offers a collection of surrogate modeling methods, sampling techniques, and a set of sample …
offers a collection of surrogate modeling methods, sampling techniques, and a set of sample …
Constrained discrete black-box optimization using mixed-integer programming
TP Papalexopoulos, C Tjandraatmadja… - International …, 2022 - proceedings.mlr.press
Discrete black-box optimization problems are challenging for model-based optimization
(MBO) algorithms, such as Bayesian optimization, due to the size of the search space and …
(MBO) algorithms, such as Bayesian optimization, due to the size of the search space and …
Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design
View Video Presentation: https://doi. org/10.2514/6.2022-0082. vid Multidisciplinary design
optimization methods aim at adapting numerical optimization techniques to the design of …
optimization methods aim at adapting numerical optimization techniques to the design of …
Bayesian Optimization for auto-tuning GPU kernels
FJ Willemsen, R van Nieuwpoort… - … and Simulation of …, 2021 - ieeexplore.ieee.org
Finding optimal parameter configurations for tunable GPU kernels is a non-trivial exercise
for large search spaces, even when automated. This poses an optimization task on a …
for large search spaces, even when automated. This poses an optimization task on a …
Effectiveness of surrogate-based optimization algorithms for system architecture optimization
View Video Presentation: https://doi. org/10.2514/6.2021-3095. vid The design of complex
system architectures brings with it a number of challenging issues, among others large …
system architectures brings with it a number of challenging issues, among others large …
[PDF][PDF] Hybrid models for mixed variables in bayesian optimization
We systematically describe the problem of simultaneous surrogate modeling of mixed
variables (ie, continuous, integer and categorical variables) in the Bayesian optimization …
variables (ie, continuous, integer and categorical variables) in the Bayesian optimization …
Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems
J Kůdela, L Dobrovský - … Conference on Parallel Problem Solving from …, 2024 - Springer
Surrogate-assisted evolutionary algorithms (SAEAs) are recently among the most widely
studied methods for their capability to solve expensive real-world optimization problems …
studied methods for their capability to solve expensive real-world optimization problems …
Customized Evolutionary Expensive Optimization: Efficient Search and Surrogate Strategies for Continuous and Categorical Variables
Surrogate-assisted evolutionary algorithms for addressing expensive optimization problems
with both continuous and categorical variables (EOPCCVs) are still in the early stages of …
with both continuous and categorical variables (EOPCCVs) are still in the early stages of …
High-dimensional multidisciplinary design optimization for aircraft eco-design/Optimisation multi-disciplinaire en grande dimension pour l'\'eco-conception avion en …
P Saves - arXiv preprint arXiv:2402.04711, 2024 - arxiv.org
Résumé D e nos jours, un intérêt significatif et croissant pour améliorer les processus de
conception de véhicules s' observe dans le domaine de l'optimisation multidisciplinaire …
conception de véhicules s' observe dans le domaine de l'optimisation multidisciplinaire …
EXPObench: benchmarking surrogate-based optimisation algorithms on expensive black-box functions
Surrogate algorithms such as Bayesian optimisation are especially designed for black-box
optimisation problems with expensive objectives, such as hyperparameter tuning or …
optimisation problems with expensive objectives, such as hyperparameter tuning or …