123 citations · 284 across the 59 of their papers we have counts for
63 papers
Evolving Features vs Evolving Entire Trees with GP for Interpretable Survival Analysis
Thalea Schlender, Peter A. N. Bosman, Tanja Alderliesten
Survival analysis concerns the task of predicting the time until an event occurs. Often used in the medical field, survival analysis deals with incomplete (i.e., censored) data, fo…
Parallel Adaptive Multi-Objective Evolutionary Learning of Discretized Bayesian Network Classifiers for Clinical Data
Damy M. F. Ha, Thalea Schlender, Yvette M. van der Linden +2
Bayesian Networks (BNs) are of interest from an explainable AI viewpoint, offering transparent probabilistic models for decision support. Baymex is a recently introduced multi-obje…
Simultaneous Model-Based Evolution of Constants and Expression Structure in GP-GOMEA for Symbolic Regression
Johannes Koch, Tanja Alderliesten, Peter A. N. Bosman
Genetic programming (GP) approaches are among the state-of-the-art for symbolic regression, the task of constructing symbolic expressions that fit well with data. To find highly ac…
GP-GOMEA with GPU-Based Fitness Evaluations: Design and Performance Analysis
Jasper Post, Johannes Koch, Anton Bouter +2
GP-GOMEA is a state-of-the-art evolutionary algorithm for symbolic regression, known for discovering small and interpretable models. However, its computational cost remains substan…
End-user validation of BRIGHT with custom-developed graphical user interface applied to cervical cancer brachytherapy
Leah R. M. Dickhoff, Ellen M. Kerkhof, Heloisa H. Deuzeman +12
Multi-objective optimisation using BRIGHT has proven insightful and effective in prostate cancer brachytherapy treatment planning. BRachytherapy via artificially Intelligent GOMEA-…
Introns and Templates Matter: Rethinking Linkage in GP-GOMEA
Johannes Koch, Tanja Alderliesten, Peter A. N. Bosman
GP-GOMEA is among the state-of-the-art for symbolic regression, especially when it comes to finding small and potentially interpretable solutions. A key mechanism employed in any G…
Sharing Knowledge without Sharing Data: Stitches can improve ensembles of disjointly trained models
Arthur Guijt, Dirk Thierens, Ellen Kerkhof +3
Deep learning has been shown to be very capable at performing many real-world tasks. However, this performance is often dependent on the presence of large and varied datasets. In s…
Iterated Population Based Training with Task-Agnostic Restarts
Alexander Chebykin, Tanja Alderliesten, Peter A. N. Bosman
Hyperparameter Optimization (HPO) can lift the burden of tuning hyperparameters (HPs) of neural networks. HPO algorithms from the Population Based Training (PBT) family are efficie…
Automated and Interpretable Survival Analysis from Multimodal Data
Mafalda Malafaia, Peter A. N. Bosman, Coen Rasch +1
Accurate and interpretable survival analysis remains a core challenge in oncology. With growing multimodal data and the clinical need for transparent models to support validation a…
PISA: An AI Pipeline for Interpretable-by-design Survival Analysis Providing Multiple Complexity-Accuracy Trade-off Models
Thalea Schlender, Catharina J. A. Romme, Yvette M. van der Linden +3
Survival analysis is central to clinical research, informing patient prognoses, guiding treatment decisions, and optimising resource allocation. Accurate time-to-event predictions…
Dealing with Segmentation Errors in Needle Reconstruction for MRI-Guided Brachytherapy
Vangelis Kostoulas, Arthur Guijt, Ellen M. Kerkhof +3
Brachytherapy involves bringing a radioactive source near tumor tissue using implanted needles. Image-guided brachytherapy planning requires amongst others, the reconstruction of t…
A Better Multi-Objective GP-GOMEA -- But do we Need it?
Joe Harrison, Tanja Alderliesten. Peter A. N. Bosman
In Symbolic Regression (SR), achieving a proper balance between accuracy and interpretability remains a key challenge. The Genetic Programming variant of the Gene-pool Optimal Mixi…
More Efficient Real-Valued Gray-Box Optimization through Incremental Distribution Estimation in RV-GOMEA
Renzo J. Scholman, Tanja Alderliesten, Peter A. N. Bosman
The Gene-pool Optimal Mixing EA (GOMEA) family of EAs offers a specific means to exploit problem-specific knowledge through linkage learning, i.e., inter-variable dependency detect…
A Step towards Interpretable Multimodal AI Models with MultiFIX
Mafalda Malafaia, Thalea Schlender, Tanja Alderliesten +1
Real-world problems are often dependent on multiple data modalities, making multimodal fusion essential for leveraging diverse information sources. In high-stakes domains, such as…
Thinking Outside the Template with Modular GP-GOMEA
Joe Harrison, Peter A. N. Bosman, Tanja Alderliesten
The goal in Symbolic Regression (SR) is to discover expressions that accurately map input to output data. Because often the intent is to understand these expressions, there is a tr…
Multi-Objective Deep-Learning-based Biomechanical Deformable Image Registration with MOREA
Georgios Andreadis, Eduard Ruiz Munné, Thomas H. W. Bäck +2
When choosing a deformable image registration (DIR) approach for images with large deformations and content mismatch, the realism of found transformations often needs to be traded…
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…