Structured Grammatical Evolution for Glucose Prediction in Diabetic Patients

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Lourenco:2019:GECCO,

• author = "Nuno Lourenco and J. Manuel Colmenar and J. Ignacio Hidalgo and Oscar Garnica",
• title = "Structured Grammatical Evolution for Glucose Prediction in Diabetic Patients",
• booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference",
• year = "2019",
• 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",
• isbn13 = "978-1-4503-6111-8",
• pages = "1250--1257",
• address = "Prague, Czech Republic",
• DOI = "doi:10.1145/3321707.3321782",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• month = "13-17 " # jul,
• organisation = "SIGEVO",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, Structured Grammatical Evolution, Performance.",
• size = "8 pages",
• 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.",
• 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