Print Email Facebook Twitter Design and Application of Gene-pool Optimal Mixing Evolutionary Algorithms for Genetic Programming Title Design and Application of Gene-pool Optimal Mixing Evolutionary Algorithms for Genetic Programming Author Virgolin, M. (TU Delft Algorithmics) Contributor Bosman, P.A.N. (promotor) Witteveen, C. (promotor) Alderliesten, T. (copromotor) Degree granting institution Delft University of Technology Date 2020-06-08 Abstract Machine learning is impacting modern society at large, thanks to its increasing potential to effciently and effectively model complex and heterogeneous phenomena. While machine learning models can achieve very accurate predictions in many applications, they are not infallible. In some cases, machine learning models can deliver unreasonable outcomes. For example, deep neural networks for self-driving cars have been found to provide wrong steering directions based on the lighting conditions of street lanes (e.g., due to cloudy weather). In other cases, models can capture and reflect unwanted biases thatwere concealed in the training data. For example, deep neural networks used to predict likely jobs and social status of people based on their pictures, were found to consistently discriminate based on gender and ethnicity–this was later attributed to human bias in the labels of the training data. Subject evolutionary algorithmsgenetic programmingmachine learningpediatric cancerradiotherapy To reference this document use: https://doi.org/10.4233/uuid:03641b5f-f8f6-4ff9-be7f-11948f6d3cc7 ISBN 978-94-6384-138-2 Part of collection Institutional Repository Document type doctoral thesis Rights © 2020 M. Virgolin Files PDF dissertation.pdf 21.95 MB Close viewer /islandora/object/uuid:03641b5f-f8f6-4ff9-be7f-11948f6d3cc7/datastream/OBJ/view