  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.

Neural and Evolutionary Computing;Artificial Intelligence