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A novel application to the optimization of neural networks is presented in this paper. Here, the weight and architecture optimization of neural networks can be formulated as a mixed-integer optimization problem. And then a mixed-integer evolutionary algorithm (Mixed-Integer Hybrid Differential Evolution, MIHDE) is used to optimize the neural network. Finally, the optimized neural network is applied...
Many methods for solving optimization problems, whether direct or indirect, rely upon gradient information and therefore may converge to a local optimum. Global optimization methods like Evolutionary algorithms, overcome this problem although these techniques are computationally expensive due to slow nature of the evolutionary process. In this work, a new concept is investigated to accelerate the...
Distributed systems have been developing rapidly in the past few years and their automatic control is a real challenge being a very active research field. In order to assure the load balancing and to optimize the resource utilization, a distributed system is using different software components, such as management tools, schedulers or monitoring tools. Considering the prediction of future behavior...
A novel application to the optimization of artificial neural networks (ANNs) is presented in this paper. Here, the weight and architecture optimization of ANNs can be formulated as a mixed-integer optimization problem. And then a mixed-integer evolutionary algorithm (Mixed-Integer Hybrid Differential Evolution, MIHDE) is used to optimize the ANN. Finally, the optimized ANN is applied to the prediction...
In this work new improvements from a previous approach of an automatic design of artificial neural networks applied to forecast time series is tackled. The automatic process to design artificial neural networks is carried out by a genetic algorithm. These improvements, in order to get an accurate forecasting, are related with: to shuffle train and test patterns obtained from time series values and...
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