no code implementations • 2 Sep 2024 • Fu Xing Long, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas Bäck, Niki van Stein
Overall, configurations with better performance can be best identified by using NN models trained on a combination of RGF and MA-BBOB functions.
no code implementations • 24 May 2023 • Fu Xing Long, Diederick Vermetten, Anna V. Kononova, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki van Stein
Within the optimization community, the question of how to generate new optimization problems has been gaining traction in recent years.
1 code implementation • 31 Mar 2023 • Bas van Stein, Fu Xing Long, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas Bäck
We propose DoE2Vec, a variational autoencoder (VAE)-based methodology to learn optimization landscape characteristics for downstream meta-learning tasks, e. g., automated selection of optimization algorithms.
no code implementations • 29 Nov 2022 • Fu Xing Long, Diederick Vermetten, Bas van Stein, Anna V. Kononova
Benchmarking is a key aspect of research into optimization algorithms, and as such the way in which the most popular benchmark suites are designed implicitly guides some parts of algorithm design.