Diabetes mellitus is a disease that affects to hundreds of millions of people worldwide. Maintaining a good control of the disease is critical to avoid severe long-term complications. In recent years, several artificial pancreas systems have been proposed and developed, which are increasingly advanced. However there is still a lot of research to do. One of the main problems that arises in the (semi) automatic control of diabetes, is to get a model explaining how glycemia (glucose levels in blood) varies with insulin, food intakes and other factors, fitting the characteristics of each individual or patient. This paper proposes the application of evolutionary computation techniques to obtain customized models of patients, unlike most of previous approaches which obtain averaged models. The proposal is based on a kind of genetic programming based on grammars known as Grammatical Evolution (GE). The proposal has been tested with in-silico patient data and results are clearly positive. We present also a study of four different grammars and five objective functions. In the test phase the models characterized the glucose with a mean percentage average error of 13.69\%, modeling well also both hyper and hypoglycemic situations.

翻译：糖尿病是一种影响全球数亿人的疾病。保持对病情的良好控制对于避免严重的长期并发症至关重要。近年来，多种日益先进的人工胰腺系统已被提出并开发。然而，仍有大量研究工作亟待开展。在糖尿病（半）自动控制中面临的主要问题之一是，需要建立一个能够解释血糖（血液中的葡萄糖水平）如何随胰岛素、食物摄入及其他因素变化的模型，该模型需适应每个个体或患者的特征。与以往多数方法仅能获得平均模型不同，本文提出应用进化计算技术来获取患者的定制化模型。该方法基于一种称为语法演化（Grammatical Evolution, GE）的语法指导型遗传编程技术。我们使用计算机模拟患者数据对该方法进行了测试，结果明确显示其有效性。本文还研究了四种不同语法和五种目标函数。在测试阶段，该模型对葡萄糖的表征平均百分比误差为13.69%，同时对高血糖和低血糖情况也具有良好的建模效果。