3 citations · 10 across the 21 of their papers we have counts for
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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…
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…
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…
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…
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…
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…