Neural computing: theory and practice
Neural computing: theory and practice
Extensions of a theory of networks for approximation and learning: outliers and negative examples
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
An intelligent neural system for predicting structural response subject to earthquakes
Advances in Engineering Software
Computationally efficient analysis of cable-stayed bridge for GA-based optimization
Engineering Applications of Artificial Intelligence
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Optimum design of large-scale structures by standard genetic algorithm (GA) makes the computational burden of the process very high. To reduce the computational cost of standard GA, two different strategies are used. The first strategy is by modifying the standard GA, called virtual sub-population method (VSP). The second strategy is by using artificial neural networks for approximating the structural analysis. In this study, radial basis function (RBF), counter propagation (CP) and generalized regression (GR) neural networks are used. Using neural networks within the framework of VSP creates a robust tool for optimum design of structures.