Hardware Design of a Model Generator Based on Grammars and Cartesian Genetic Programming for Blood Glucose Prediction
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{cano:2023:GECCOcomp,
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author = "Jorge Cano and J. Ignacio Hidalgo and
Oscar Garnica and Juan Lanchares",
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title = "Hardware Design of a Model Generator Based on Grammars
and Cartesian Genetic Programming for Blood Glucose
Prediction",
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booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
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year = "2023",
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editor = "Justyna Petke and Aniko Ekart",
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pages = "55--56",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # 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, cartesian
genetic programming",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583133.3596427",
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size = "2 pages",
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abstract = "People with diabetes need to control their blood
glucose levels to avoid dangerous situations such as
getting into hypoglycemia or hyperglycemia, which can
lead to long-term and short-term complications. One of
the most important daily tasks of people with diabetes
is to estimate or predict the glucose in a near future
as a consequence of medication, eating, or insulin
administration events. We present a parameterized
hardware implementation of a blood glucose level
predictor generator. The design was implemented over a
Field Programmable Gate Array and uses as input
variables a set of data from the person (blood glucose
levels, carbohydrates, and insulin units). Our
implementation produces personal devices the patient
can use whenever new readings of the variable are
available. Moreover, it could be combined with insulin
pumps and continuous glucose monitoring systems to
develop an artificial pancreas. For the model
generation, we designed a novel technique based on
grammars, cartesian genetic programming with an
evolutionary strategy (1+λ) and a fitness function
based on the Clarke Error Grid Analysis. Preliminary
results show that our hardware implementation achieved
higher speeds and lower power consumption than its
software counterparts while preserving or even
improving the accuracy of the predictions.",
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notes = "GECCO-2023 A Recombination of the 32nd International
Conference on Genetic Algorithms (ICGA) and the 28th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Jorge Cano
Jose Ignacio Hidalgo Perez
Oscar Garnica
J Lanchares
Citations