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.
Embedded consumer devices are increasing their capabilities and can now implement new multimedia applications reserved only for powerful desktops a few years ago. These applications share complex and intensive dynamic memory use. Thus, dynamic memory optimizations are a requirement when porting these applications. Within these optimizations, the refinement of the Dynamically (de)allocated Data Type (or DDT) implementations is one of the most important and difficult parts for an efficient mapping onto low-power embedded devices.In this paper, we describe a new automatic optimization approach for the DDTs of object-oriented multimedia applications. It is based on an analytical pre-characterization of the possible elementary DDT blocks, and a multi-objective genetic algorithm to explore the design space and to select the best implementation according to different optimization criteria (i.e., memory accesses, memory footprint and energy consumption). Our results in real-life multimedia applications show that the best implementations of DDTs can be obtained in an automated way in few hours, while typically designers would require days to find a suitable implementation, achieving important savings in exploration time with respect to other state-of-the-art heuristics-based optimization methods for this task.
Hardware-Software partitioning is one of the most important issues of Codesign of embedded systems because it is made at the beginning of the cycle of design. In terms of costs and delays, $nul results will strongly depend on partitioning. In this work we address the functional partitioning problem of Hardware-Software Codesign using a genetic algorithm. Experimental results includes a comparative study with 3 algorithms such are Simulated Annealing, Fiduccia -Matheyses and a Modified version of this with an improvement of results.
In this paper, we present a study on the fault tolerance nature of the island model when applied to Genetic Algorithms. Parallel and distributed models have been extensively applied to GAs when researchers tackle hard problems. The idea is both to reduce computing time while also improving diversity of populations and therefore quality of solutions. Nevertheless, there are few works dealing with the problem of faults that are usually present when a distributed infrastructure is employed for running the parallel algorithm. This paper studies the behavior of the Island Model when faults appear on a parallel computer or a network of computers. Two benchmark problems have been employed, and good results obtained for each of them allow us to reliably consider Island Model as a fault tolerant parallel algorithm.
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