- author = "Marco Virgolin",
- title = "Design and Application of Gene-pool Optimal Mixing Evolutionary Algorithms for Genetic Programming",
- school = "Delft University of Technology, Netherlands",
- year = "2020",
- month = "8 " # jun,
- note = "Winner Best PhD thesis GECCO 2021",
- keywords = "genetic algorithms, genetic programming, evolutionary algorithms, machine learning, pediatric cancer, radiotherapy, GP-GOMEA",
- isbn13 = "978-94-6384-138-2",
-
URL = "
http://resolver.tudelft.nl/uuid:03641b5f-f8f6-4ff9-be7f-11948f6d3cc7",
-
URL = "https://repository.tudelft.nl/islandora/object/uuid:03641b5f-f8f6-4ff9-be7f-11948f6d3cc7/datastream/OBJ/download",
-
DOI = "
doi:10.4233/uuid:03641b5f-f8f6-4ff9-be7f-11948f6d3cc7",
- timestamp = "Tue, 09 Jun 2020 01:00:00 +0200",
-
biburl = "https://dblp.org/rec/phd/basesearch/Virgolin20.bib",
- bibsource = "dblp computer science bibliography, https://dblp.org",
- size = "263 pages",
- abstract = "Machine learning is impacting modern society at large, thanks to its increasing potential to efficiently 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 that were 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.",
-
notes = "Winner 2021 SIGEVO Dissertation Award
https://sig.sigevo.org/index.html/tiki-index.php?page=SIGEVO+Dissertation+Award
supervisors: P.A.N. Bosman C. Witteveen, T. Alderliesten",
