11 citations · 11 across the 7 of their papers we have counts for
7 papers
A Fresh Look at Lamarckian Evolution and the Baldwin Effect
Inès Benito, Johannes F. Lutzeyer, Benjamin Doerr
Baldwinian and Lamarckian evolution have existed for a long time in evolutionary algorithms (EAs) without ever dominating the academic literature or practical applications. In this…
GASS: Geometry-Aware Spherical Sampling for Disentangled Diversity Enhancement in Text-to-Image Generation
Ye Zhu, Kaleb S. Newman, Johannes F. Lutzeyer +3
Despite high semantic alignment, modern text-to-image (T2I) generative models still struggle to synthesize diverse images from a given prompt. In this work, we enhance the T2I dive…
Position: Don't be Afraid of Over-Smoothing And Over-Squashing
Niklas Kormann, Benjamin Doerr, Johannes F. Lutzeyer
Over-smoothing and over-squashing have been extensively studied in the literature on Graph Neural Networks (GNNs) over the past years. We challenge this prevailing focus in GNN res…
HIEGNet: A Heterogenous Graph Neural Network Including the Immune Environment in Glomeruli Classification
Niklas Kormann, Masoud Ramuz, Zeeshan Nisar +6
Graph Neural Networks (GNNs) have recently been found to excel in histopathology. However, an important histopathological task, where GNNs have not been extensively explored, is th…
Speeding Up Hyper-Heuristics With Markov-Chain Operator Selection and the Only-Worsening Acceptance Operator
Abderrahim Bendahi, Benjamin Doerr, Adrien Fradin +1
The move-acceptance hyper-heuristic was recently shown to be able to leave local optima with astonishing efficiency (Lissovoi et al., Artificial Intelligence (2023)). In this work,…
Hyper-Heuristics Can Profit From Global Variation Operators
Benjamin Doerr, Johannes F. Lutzeyer
In recent work, Lissovoi, Oliveto, and Warwicker (Artificial Intelligence (2023)) proved that the Move Acceptance Hyper-Heuristic (MAHH) leaves the local optimum of the multimodal…