A practical methodology for reproducible experimentation: an application to the Double-row Facility Layout Problem.

Reproducibility of experiments is a complex task in stochastic methods such as evolutionary algorithms or metaheuristics in general. Many works from the literature give general guidelines to favor reproducibility. However, none of them provide both a practical set of steps and also software tools to help on this process. In this paper, we propose a practical methodology to favor reproducibility in optimization problems tackled with stochastic methods. This methodology is divided into three main steps, where the researcher is assisted by software tools which implement state-of-theart techniques related to this process. The methodology has been applied to study the Double Row Facility Layout Problem, where we propose a new algorithm able to obtain better results than the state-of-the-art methods. To this aim, we have also replicated the previous methods in order to complete the study with a new set of larger instances. All the produced artifacts related to the methodology and the study of the target problem are available in Zenodo.

Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods.

Predicting glucose values on the basis of insulin and food intakes is a difficult task that people with diabetes need to do daily. This is necessary as it is important to maintain glucose levels at appropriate values to avoid not only short-term, but also long-term complications of the illness. Artificial intelligence in general and machine learning techniques in particular have already lead to promising results in modeling and predicting glucose concentrations. In this work, several machine learning techniques are used for the modeling and prediction of glucose concentrations using as inputs the values measured by a continuous monitoring glucose system as well as also previous and estimated future carbohydrate intakes and insulin injections. In particular, we use the following four techniques: genetic programming, random forests, k-nearest neighbors, and grammatical evolution. We propose two new enhanced modeling algorithms for glucose prediction, namely (i) a variant of grammatical evolution which uses an optimized grammar, and (ii) a variant of tree-based genetic programming which uses a three-compartment model for carbohydrate and insulin dynamics. The predictors were trained and tested using data of ten patients from a public hospital in Spain. We analyze our experimental results using the Clarke error grid metric and see that 90% of the forecasts are correct (i.e., Clarke error categories A and B), but still even the best methods produce 5 to 10% of serious errors (category D) and approximately 0.5% of very serious errors (category E). We also propose an enhanced genetic programming algorithm that incorporates a three-compartment model into symbolic regression models to create smoothed time series of the original carbohydrate and insulin time series.

glUCModel: a monitoring and modeling system for chronic diseases applied to diabetes.

Chronic patients must carry out a rigorous control of diverse factors in their lives. Diet, sport activity, medical analysis or blood glucose levels are some of them. This is a hard task, because some of these controls are performed very often, for instance some diabetics measure their glucose levels several times every day, or patients with chronic renal disease, a progressive loss in renal function, should strictly control their blood pressure and diet. In order to facilitate this task to both the patient and the physician, we have developed a web application for chronic diseases control which we have particularized to diabetes. This system, called glUCModel, improves the communication and interaction between patients and doctors, and eventually the quality of life of the former. Through a web application, patients can upload their personal and medical data, which are stored in a centralized database. In this way, doctors can consult this information and have a better control over patient records. glUCModel also presents three novelties in the disease management: a recommender system, an e-learning course and a module for automatic generation of glucose levels model. The recommender system uses Case Based Reasoning. It provides automatic recommendations to the patient, based on the recorded data and physician preferences, to improve their habits and knowledge about the disease. The e-learning course provides patients a space to consult information about the illness, and also to assess their own knowledge about the disease. Blood glucose levels are modeled by means of evolutionary computation, allowing to predict glucose levels using particular features of each patient. glUCModel was developed as a system where a web layer allows the access of the users from any device connected to the Internet, like desktop computers, tablets or mobile phones.