ASAGA: an adaptive surrogate-assisted genetic algorithm
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evolutionary Bi-objective Learning with Lowest Complexity in Neural Networks: Empirical Comparisons
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Adaptive fuzzy fitness granulation for evolutionary optimization
International Journal of Approximate Reasoning
Meta-Modeling in Multiobjective Optimization
Multiobjective Optimization
A systems approach to evolutionary multiobjective structural optimization and beyond
IEEE Computational Intelligence Magazine
Evolutionary hidden information detection by granulation-based fitness approximation
Applied Soft Computing
Evolving robust controller parameters using covariance matrix adaptation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Multi-objective reliability-based optimization with stochastic metamodels
Evolutionary Computation
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We study the use of neural networks as approximate models for the fitness evaluation in evolutionary design optimization. To improve the quality of the neural network models, structure optimization of these networks is performed with respect to two different criteria: One is the commonly used approximation error with respect to all available data, and the other is the ability of the networks to learn different problems of a common class of problems fast and with high accuracy. Simulation results from turbine blade optimizations using the structurally optimized neural network models are presented to show that the performance of the models can be improved significantly through structure optimization.