123 citations · 284 across the 59 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…
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…
The Impact of Asynchrony on Parallel Model-Based EAs
Arthur Guijt, Dirk Thierens, Tanja Alderliesten +1
In a parallel EA one can strictly adhere to the generational clock, and wait for all evaluations in a generation to be done. However, this idle time limits the throughput of the al…
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…
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…
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…
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…
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…
Obtaining Smoothly Navigable Approximation Sets in Bi-Objective Multi-Modal Optimization
Renzo J. Scholman, Anton Bouter, Leah R. M. Dickhoff +2
Even if a Multi-modal Multi-Objective Evolutionary Algorithm (MMOEA) is designed to find solutions well spread over all locally optimal approximation sets of a Multi-modal Multi-ob…
Evolvability Degeneration in Multi-Objective Genetic Programming for Symbolic Regression
Dazhuang Liu, Marco Virgolin, Tanja Alderliesten +1
Genetic programming (GP) is one of the best approaches today to discover symbolic regression models. To find models that trade off accuracy and complexity, the non-dominated sortin…
Heed the Noise in Performance Evaluations in Neural Architecture Search
Arkadiy Dushatskiy, Tanja Alderliesten, Peter A. N. Bosman
Neural Architecture Search (NAS) has recently become a topic of great interest. However, there is a potentially impactful issue within NAS that remains largely unrecognized: noise.…
A Novel Surrogate-assisted Evolutionary Algorithm Applied to Partition-based Ensemble Learning
Arkadiy Dushatskiy, Tanja Alderliesten, Peter A. N. Bosman
We propose a novel surrogate-assisted Evolutionary Algorithm for solving expensive combinatorial optimization problems. We integrate a surrogate model, which is used for fitness va…
Real-valued Evolutionary Multi-modal Multi-objective Optimization by Hill-Valley Clustering
S. C. Maree, T. Alderliesten, P. A. N. Bosman
In model-based evolutionary algorithms (EAs), the underlying search distribution is adapted to the problem at hand, for example based on dependencies between decision variables. Hi…