123 citations · 284 across the 59 of their papers we have counts for
11 papers · 1 filter
Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation
C. J. Rodriguez, S. L. Thomson, T. Alderliesten +1
Many real-world problems have expensive-to-compute fitness functions and are multi-objective in nature. Surrogate-assisted evolutionary algorithms are often used to tackle such pro…
Hyperparameter-Free Medical Image Synthesis for Sharing Data and Improving Site-Specific Segmentation
Alexander Chebykin, Peter A. N. Bosman, Tanja Alderliesten
Sharing synthetic medical images is a promising alternative to sharing real images that can improve patient privacy and data security. To get good results, existing methods for med…
Stitching for Neuroevolution: Recombining Deep Neural Networks without Breaking Them
Arthur Guijt, Dirk Thierens, Tanja Alderliesten +1
Traditional approaches to neuroevolution often start from scratch. This becomes prohibitively expensive in terms of computational and data requirements when targeting modern, deep…
Deep learning-based auto-segmentation of paraganglioma for growth monitoring
E. M. C. Sijben, J. C. Jansen, M. de Ridder +2
Volume measurement of a paraganglioma (a rare neuroendocrine tumor that typically forms along major blood vessels and nerve pathways in the head and neck region) is crucial for mon…
Multi-Objective Learning for Deformable Image Registration
Monika Grewal, Henrike Westerveld, Peter A. N. Bosman +1
Deformable image registration (DIR) involves optimization of multiple conflicting objectives, however, not many existing DIR algorithms are multi-objective (MO). Further, while the…
MultiFIX: An XAI-friendly feature inducing approach to building models from multimodal data
Mafalda Malafaia, Thalea Schlender, Peter A. N. Bosman +1
In the health domain, decisions are often based on different data modalities. Thus, when creating prediction models, multimodal fusion approaches that can extract and combine relev…
Learning Discretized Bayesian Networks with GOMEA
Damy M. F. Ha, Tanja Alderliesten, Peter A. N. Bosman
Bayesian networks model relationships between random variables under uncertainty and can be used to predict the likelihood of events and outcomes while incorporating observed evide…
Fitness-based Linkage Learning and Maximum-Clique Conditional Linkage Modelling for Gray-box Optimization with RV-GOMEA
Georgios Andreadis, Tanja Alderliesten, Peter A. N. Bosman
For many real-world optimization problems it is possible to perform partial evaluations, meaning that the impact of changing a few variables on a solution's fitness can be computed…
Improving the efficiency of GP-GOMEA for higher-arity operators
Thalea Schlender, Mafalda Malafaia, Tanja Alderliesten +1
Deploying machine learning models into sensitive domains in our society requires these models to be explainable. Genetic Programming (GP) can offer a way to evolve inherently inter…
Function Class Learning with Genetic Programming: Towards Explainable Meta Learning for Tumor Growth Functionals
E. M. C. Sijben, J. C. Jansen, P. A. N. Bosman +1
Paragangliomas are rare, primarily slow-growing tumors for which the underlying growth pattern is unknown. Therefore, determining the best care for a patient is hard. Currently, if…
A Tournament of Transformation Models: B-Spline-based vs. Mesh-based Multi-Objective Deformable Image Registration
Georgios Andreadis, Joas I. Mulder, Anton Bouter +2
The transformation model is an essential component of any deformable image registration approach. It provides a representation of physical deformations between images, thereby defi…