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- W2149490617 abstract "This paper is aimed at optimization of black-box functions. We assume that these functions are time demanding and therefore our goal is to minimize the number of evaluations of these functions. As one of the today’s most promising algorithms, the radial basis function network (RBFN) is presented. Our particular implementation is based on the methodology presented in 12. The novelty in our approach is the use of an evolutionary algorithm GRADE 7. Also several scenarios of creating new points in the process of the approximation are presented. In comparison with the original approach 12, the number of needed evaluations of a test function is reduced approximately by a factor of two. To show the ability of the proposed methodology, the suite of twenty multi-modal functions is used along with one real-world problem of optimal control of structures undergoing large displacements." @default.
- W2149490617 created "2016-06-24" @default.
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- W2149490617 date "2008-06-14" @default.
- W2149490617 modified "2023-09-25" @default.
- W2149490617 title "Evolutionary Optimization Of Multimodal Functions Using Radial Basis Function Networks" @default.
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- W2149490617 doi "https://doi.org/10.2514/6.2008-5922" @default.
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