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<p>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.</p>

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Genetic Algorithm-Based Shape Parameter Tuning for Radial Basis Function Interpolation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月16日

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  • Roberto Cavoretto,
  • Alessandra De Rossi,
  • Sandro Lancellotti,
  • Domenico Mezzanotte

摘要

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.