activity
20182022
most citedHeed the Noise in Performance Evaluations in Neural Architecture Search

3 citations · 12 across the 16 of their papers we have counts for

collaborators

26 papers

cs.CV20221 cited

Evolutionary Neural Cascade Search across Supernetworks

Alexander Chebykin, Tanja Alderliesten, Peter A. N. Bosman

To achieve excellent performance with modern neural networks, having the right network architecture is important. Neural Architecture Search (NAS) concerns the automatic discovery…

cs.NE20223 cited

Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning

Marco Virgolin, Eric Medvet, Tanja Alderliesten +1

Interpretability can be critical for the safe and responsible use of machine learning models in high-stakes applications. So far, evolutionary computation (EC), in particular in th…

cs.NE2022

Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models

E. M. C. Sijben, T. Alderliesten, P. A. N. Bosman

Explainable artificial intelligence (XAI) is an important and rapidly expanding research topic. The goal of XAI is to gain trust in a machine learning (ML) model through clear insi…

cs.NE2022

Adaptive Objective Configuration in Bi-Objective Evolutionary Optimization for Cervical Cancer Brachytherapy Treatment Planning

Leah R. M. Dickhoff, Ellen M. Kerkhof, Heloisa H. Deuzeman +3

The Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA) has been proven effective and efficient in solving real-world problems. A prime exampl…

cs.NE20223 cited

On genetic programming representations and fitness functions for interpretable dimensionality reduction

Thomas Uriot, Marco Virgolin, Tanja Alderliesten +1

Dimensionality reduction (DR) is an important technique for data exploration and knowledge discovery. However, most of the main DR methods are either linear (e.g., PCA), do not pro…

cs.NE20222 cited

Solving Multi-Structured Problems by Introducing Linkage Kernels into GOMEA

Arthur Guijt, Dirk Thierens, Tanja Alderliesten +1

Model-Based Evolutionary Algorithms (MBEAs) can be highly scalable by virtue of linkage (or variable interaction) learning. This requires, however, that the linkage model can captu…