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Demonstrates optimizing shape parameters in radial basis function interpolation using genetic algorithms, suggesting improved computational efficiency.
In this study, we address the non-trivial problem of determining the optimal shape parameter in radial basis function interpolation. We propose the use of a genetic algorithm, an optimization technique inspired by evolutionary selection, to identify this parameter. Numerical experiments are presented to evaluate the effectiveness of this approach, with direct comparisons to the leave-one-out cross-validation method, thereby highlighting its computational efficiency.
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Cavoretto et al. (2026) studied this question.
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