144 citations · 367 across the 18 of their papers we have counts for
3 papers · 1 filter
Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms
Marco Virgolin, Ziyuan Wang, Tanja Alderliesten +1
Machine Learning (ML) is proving extremely beneficial in many healthcare applications. In pediatric oncology, retrospective studies that investigate the relationship between treatm…
On Explaining Machine Learning Models by Evolving Crucial and Compact Features
Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
Feature construction can substantially improve the accuracy of Machine Learning (ML) algorithms. Genetic Programming (GP) has been proven to be effective at this task by evolving n…
Improving Model-based Genetic Programming for Symbolic Regression of Small Expressions
Marco Virgolin, Tanja Alderliesten, Cees Witteveen +1
The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based EA framework that has been shown to perform well in several domains, including Genetic Programming (GP)…