Practical genetic algorithms
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
Automatic Generation of Neural Network Architecture Using Evolutionary Computation
Automatic Generation of Neural Network Architecture Using Evolutionary Computation
Review: Neural networks and statistical techniques: A review of applications
Expert Systems with Applications: An International Journal
Prediction of indoor temperature and relative humidity using neural network models: model comparison
Neural Computing and Applications
A prediction interval-based approach to determine optimal structures of neural network metamodels
Expert Systems with Applications: An International Journal
Constructing prediction intervals for neural network metamodels of complex systems
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Confidence estimation methods for neural networks: a practical comparison
IEEE Transactions on Neural Networks
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The delta technique has been proposed in literature for constructing prediction intervals for targets estimated by neural networks. Quality of constructed prediction intervals using this technique highly depends on neural network characteristics. Unfortunately, literature is void of information about how these dependences can be managed in order to optimize prediction intervals. This study attempts to optimize length and coverage probability of prediction intervals through modifying structure and parameters of the underlying neural networks. In an evolutionary optimization, genetic algorithm is applied for finding the optimal values of network size and training hyper-parameters. The applicability and efficiency of the proposed optimization technique is examined and demonstrated using a real case study. It is shown that application of the proposed optimization technique significantly improves quality of constructed prediction intervals in term of length and coverage probability.