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:1.8051

@InProceedings{cano:2023:GECCOcomp,

• author = "Jorge Cano and J. Ignacio Hidalgo and Oscar Garnica and Juan Lanchares",
• title = "Hardware Design of a Model Generator Based on Grammars and Cartesian Genetic Programming for Blood Glucose Prediction",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• editor = "Justyna Petke and Aniko Ekart",
• pages = "55--56",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3596427",
• size = "2 pages",
• 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.",
• 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