Generating Cheap Representative Functions for Expensive Automotive Crashworthiness Optimization
FX Long, B van Stein, M Frenzel, P Krause… - ACM Transactions on …, 2024 - dl.acm.org
Solving real-world engineering optimization problems, such as automotive crashworthiness
optimization, is extremely challenging, because the problem characteristics are oftentimes …
optimization, is extremely challenging, because the problem characteristics are oftentimes …
A Survey of Meta-features Used for Automated Selection of Algorithms for Black-box Single-objective Continuous Optimization
The selection of the most appropriate algorithm to solve a given problem instance, known as
algorithm selection, is driven by the potential to capitalize on the complementary …
algorithm selection, is driven by the potential to capitalize on the complementary …
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization
Bayesian Optimization (BO) is a class of surrogate-based, sample-efficient algorithms for
optimizing black-box problems with small evaluation budgets. The BO pipeline itself is highly …
optimizing black-box problems with small evaluation budgets. The BO pipeline itself is highly …
Landscape Analysis Based vs. Domain-Specific Optimization for Engineering Design Applications: A Clear Case
R de Winter, FX Long, A Thomaser… - … IEEE Conference on …, 2024 - ieeexplore.ieee.org
Traditional methods in black-box optimization often prescribe general-purpose algorithms
for broad problem categories, which overlooks the significant variability in optimization …
for broad problem categories, which overlooks the significant variability in optimization …
Hyperparameter Adaptive Search for Surrogate Optimization: A Self-Adjusting Approach
Surrogate Optimization (SO) algorithms have shown promise for optimizing expensive black-
box functions. However, their performance is heavily influenced by hyperparameters related …
box functions. However, their performance is heavily influenced by hyperparameters related …
Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization
In optimization, we often encounter expensive black-box problems with unknown problem
structures. Bayesian Optimization (BO) is a popular, surrogate-assisted and thus sample …
structures. Bayesian Optimization (BO) is a popular, surrogate-assisted and thus sample …