109 citations · 277 across the 52 of their papers we have counts for
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Benchmarking that Matters: Rethinking Benchmarking for Practical Impact
Anna V. Kononova, Niki van Stein, Olaf Mersmann +14
Benchmarking has driven scientific progress in Evolutionary Computation, yet current practices fall short of real-world needs. Widely used synthetic suites such as BBOB and CEC iso…
A Standardized Benchmark Set of Clustering Problem Instances for Comparing Black-Box Optimizers
Diederick Vermetten, Catalin-Viorel Dinu, Marcus Gallagher
One key challenge in optimization is the selection of a suitable set of benchmark problems. A common goal is to find functions which are representative of a class of real-world opt…
Abnormal Mutations: Evolution Strategies Don't Require Gaussianity
Jacob de Nobel, Diederick Vermetten, Hao Wang +3
The mutation process in evolution strategies has been interlinked with the normal distribution since its inception. Many lines of reasoning have been given for this strong dependen…
Transfer Learning of Surrogate Models: Integrating Domain Warping and Affine Transformations
Shuaiqun Pan, Diederick Vermetten, Manuel López-Ibáñez +2
Surrogate models provide efficient alternatives to computationally demanding real world processes but often require large datasets for effective training. A promising solution to t…
Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks
Shuaiqun Pan, Diederick Vermetten, Manuel López-Ibáñez +2
Surrogate models are frequently employed as efficient substitutes for the costly execution of real-world processes. However, constructing a high-quality surrogate model often deman…