Y.S. Lian1, M.S. Liou2
CMES-Computer Modeling in Engineering & Sciences, Vol.8, No.1, pp. 61-72, 2005, DOI:10.3970/cmes.2005.008.061
Abstract This paper focuses on integration of computational methods for design optimization based on data mining and knowledge discovery. We propose to use radial basis function neural networks to analyze the large database generated from evolutionary algorithms and to extract the cause-effect relationship, between the objective functions and the input design variables. The aim is to improve the optimization process by either reducing the computation cost or improving the optimal. Also, it is hoped to provide designers with the salient design pattern about the problem under consideration, from the physics-based simulations. The proposed technique is applied More >
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