4 citations · 6 across the 8 of their papers we have counts for
6 papers · 1 filter
Adversarial Latent Autoencoder with Self-Attention for Structural Image Synthesis
Jiajie Fan, Laure Vuaille, Hao Wang +1
Generative Engineering Design approaches driven by Deep Generative Models (DGM) have been proposed to facilitate industrial engineering processes. In such processes, designs often…
Representation-agnostic distance-driven perturbation for optimizing ill-conditioned problems
Kirill Antonov, Anna V. Kononova, Thomas Bäck +1
Locality is a crucial property for efficiently optimising black-box problems with randomized search heuristics. However, in practical applications, it is not likely to always find…
Challenges of ELA-guided Function Evolution using Genetic Programming
Fu Xing Long, Diederick Vermetten, Anna V. Kononova +4
Within the optimization community, the question of how to generate new optimization problems has been gaining traction in recent years. Within topics such as instance space analysi…
When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems
André Thomaser, Jacob de Nobel, Diederick Vermetten +3
The domain of an optimization problem is seen as one of its most important characteristics. In particular, the distinction between continuous and discrete optimization is rather im…
Application of quantum-inspired generative models to small molecular datasets
C. Moussa, H. Wang, M. Araya-Polo +2
Quantum and quantum-inspired machine learning has emerged as a promising and challenging research field due to the increased popularity of quantum computing, especially with near-t…
Modular Differential Evolution
Diederick Vermetten, Fabio Caraffini, Anna V. Kononova +1
New contributions in the field of iterative optimisation heuristics are often made in an iterative manner. Novel algorithmic ideas are not proposed in isolation, but usually as an…