Function Class Learning with Genetic Programming: Towards Explainable Meta Learning for Tumor Growth Functionals
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{sijben:2024:GECCO,
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author = "Evi Sijben and Jeroen Jansen and Peter Bosman and
Tanja Alderliesten",
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title = "Function Class Learning with Genetic Programming:
Towards Explainable Meta Learning for Tumor Growth
Functionals",
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booktitle = "Proceedings of the 2024 Genetic and Evolutionary
Computation Conference",
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year = "2024",
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editor = "Ruhul Sarker and Patrick Siarry and Julia Handl and
Xiaodong Li and Markus Wagner and Mario Garza-Fabre and
Kate Smith-Miles and Richard Allmendinger and
Ying Bi and Grant Dick and Amir H Gandomi and
Marcella Scoczynski Ribeiro Martins and Hirad Assimi and
Nadarajen Veerapen and Yuan Sun and
Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and
Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva",
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pages = "1354--1362",
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address = "Melbourne, Australia",
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series = "GECCO '24",
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month = "14-18 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, function
class learning, explainable AI, XAI, Real World
Applications",
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isbn13 = "979-8-4007-0494-9",
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DOI = "doi:10.1145/3638529.3654145",
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size = "9 pages",
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abstract = "Paragangliomas are rare, primarily slow-growing tumors
for which the underlying growth pattern is unknown.
Therefore, determining the best care for a patient is
hard. Currently, if no significant tumor growth is
observed, treatment is often delayed, as treatment
itself is not without risk. However, by doing so, the
risk of (irreversible) adverse effects due to tumor
growth may increase. Being able to predict the growth
accurately could assist in determining whether a
patient will need treatment during their lifetime and,
if so, the timing of this treatment. The aim of this
work is to learn the general underlying growth pattern
of paragangliomas from multiple tumor growth data sets,
in which each data set contains a tumor's volume over
time. To do so, we propose a novel approach based on
genetic programming to learn a function class, i.e., a
parameterized function that can be fit anew for each
tumor. We do so in a unique, multi-modal,
multi-objective fashion to find multiple potentially
interesting function classes in a single run. We
evaluate our approach on a synthetic and a real-world
data set. By analyzing the resulting function classes,
we can effectively explain the general patterns in the
data.",
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notes = "GECCO-2024 RWA A Recombination of the 33rd
International Conference on Genetic Algorithms (ICGA)
and the 29th Annual Genetic Programming Conference
(GP)",
- }
Genetic Programming entries for
Evi Sijben
Jeroen Jansen
Peter A N Bosman
Tanja Alderliesten
Citations