123 citations · 284 across the 59 of their papers we have counts for
4 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…
Benchmarking HillVallEA for the GECCO 2019 Competition on Multimodal Optimization
S. C. Maree, T. Alderliesten, P. A. N. Bosman
This report presents benchmarking results of the Hill-Valley Evolutionary Algorithm version 2019 (HillVallEA19) on the CEC2013 niching benchmark suite under the restrictions of the…
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)…