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Enhancing Grammatical Evolution Through Data Augmentation: Application to Blood Glucose Forecasting.

, , , , , , , and . EvoApplications (1), volume 10199 of Lecture Notes in Computer Science, page 142-157. (2017)

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Swarm hybrid optimization for a piecewise model fitting applied to a glucose model., , , , , and . J. Systems and IT, 20 (4): 404-416 (2018)Enhancing Grammatical Evolution Through Data Augmentation: Application to Blood Glucose Forecasting., , , , , , , and . EvoApplications (1), volume 10199 of Lecture Notes in Computer Science, page 142-157. (2017)Modeling glycemia in humans by means of Grammatical Evolution., , , , , , and . Appl. Soft Comput., (2014)Clarke and parkes error grid analysis of diabetic glucose models obtained with evolutionary computation., , , , , , , , and . GECCO (Companion), page 1305-1312. ACM, (2014)Glucose forecasting combining Markov chain based enrichment of data, random grammatical evolution and Bagging., , , , , , , , and . Appl. Soft Comput., (2020)Modeling glycemia in humans by means of Grammatical Evolution., , , , , , and . CoRR, (2023)glUCModel: A monitoring and modeling system for chronic diseases applied to diabetes., , , , , and . J. Biomed. Informatics, (2014)Data augmentation and evolutionary algorithms to improve the prediction of blood glucose levels in scarcity of training data., , , , , , and . CEC, page 2193-2200. IEEE, (2017)Data-Based Identification of Prediction Models for Glucose., , , , , , , , and . GECCO (Companion), page 1327-1334. ACM, (2015)Predicting Glycemia in Diabetic Patients By Evolutionary Computation and Continuous Glucose Monitoring., , , , , and . GECCO (Companion), page 1393-1400. ACM, (2016)