59 citations · 144 across the 61 of their papers we have counts for
5 papers · 1 filter
Clustering-based Domain-Incremental Learning
Christiaan Lamers, Rene Vidal, Nabil Belbachir +3
We consider the problem of learning multiple tasks in a continual learning setting in which data from different tasks is presented to the learner in a streaming fashion. A key chal…
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…
Deep-BIAS: Detecting Structural Bias using Explainable AI
Bas van Stein, Diederick Vermetten, Fabio Caraffini +1
Evaluating the performance of heuristic optimisation algorithms is essential to determine how well they perform under various conditions. Recently, the BIAS toolbox was introduced…
DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis
Bas van Stein, Fu Xing Long, Moritz Frenzel +3
We propose DoE2Vec, a variational autoencoder (VAE)-based methodology to learn optimization landscape characteristics for downstream meta-learning tasks, e.g., automated selection…