4 citations · 16 across the 19 of their papers we have counts for
30 papers
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…
Coefficient Mutation in the Gene-pool Optimal Mixing Evolutionary Algorithm for Symbolic Regression
Marco Virgolin, Peter A. N. Bosman
Currently, the genetic programming version of the gene-pool optimal mixing evolutionary algorithm (GP-GOMEA) is among the top-performing algorithms for symbolic regression (SR). A…
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…
GPU-Accelerated Parallel Gene-pool Optimal Mixing in a Gray-Box Optimization Setting
Anton Bouter, Peter A. N. Bosman
In a Gray-Box Optimization (GBO) setting that allows for partial evaluations, the fitness of an individual can be updated efficiently after a subset of its variables has been modif…