Structured Grammatical Evolution for Glucose Prediction in Diabetic Patients
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Lourenco:2019:GECCO,
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author = "Nuno Lourenco and J. Manuel Colmenar and
J. Ignacio Hidalgo and Oscar Garnica",
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title = "Structured Grammatical Evolution for Glucose
Prediction in Diabetic Patients",
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booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary
Computation Conference",
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year = "2019",
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editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and
Anne Auger and Petr Posik and Leslie {Peprez Caceres} and
Andrew M. Sutton and Nadarajen Veerapen and
Christine Solnon and Andries Engelbrecht and Stephane Doncieux and
Sebastian Risi and Penousal Machado and
Vanessa Volz and Christian Blum and Francisco Chicano and
Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and
Jonathan Fieldsend and Jose Antonio Lozano and
Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and
Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and
Robin Purshouse and Thomas Baeck and Justyna Petke and
Giuliano Antoniol and Johannes Lengler and
Per Kristian Lehre",
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isbn13 = "978-1-4503-6111-8",
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pages = "1250--1257",
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address = "Prague, Czech Republic",
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DOI = "doi:10.1145/3321707.3321782",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = "13-17 " # jul,
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organisation = "SIGEVO",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution, Structured Grammatical Evolution,
Performance.",
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size = "8 pages",
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abstract = "Structured grammatical evolution is a recent
grammar-based genetic programming variant that tackles
the main drawbacks of Grammatical Evolution, by relying
on a one-to-one mapping between each gene and a
non-terminal symbol of the grammar. It was applied,
with success, in previous works with a set of classical
benchmarks problems. However, assessing performance on
hard real-world problems is still missing. In this
paper, we fill in this gap, by analysing the
performance of SGE when generating predictive models
for the glucose levels of diabetic patients. Our
algorithm uses features that take into account the past
glucose values, insulin injections, and the amount of
carbohydrate ingested by a patient. The results show
that SGE can evolve models that can predict the glucose
more accurately when compared with previous
grammar-based approaches used for the same problem.
Additionally, we also show that the models tend to be
more robust, since the behaviour in the training and
test data is very similar, with a small variance.",
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notes = "Also known as \cite{3321782} GECCO-2019 A
Recombination of the 28th International Conference on
Genetic Algorithms (ICGA) and the 24th Annual Genetic
Programming Conference (GP)",
- }
Genetic Programming entries for
Nuno Lourenco
J Manuel Colmenar
Jose Ignacio Hidalgo Perez
Oscar Garnica
Citations