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DOE OSTI · 1659396

Genetic Algorithm for Hyperparameter Optimization in Gaussian Process Modeling

Filippov, A.·Goumiri, I.·Priest, B.
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Abstract

A genetic algorithm is developed and applied to optimize hyperparameters of convolutional recursively determined dual neural network-Gaussian process (NNGP) kernels. As a specific application of the combined GPNN-GA algorithm, it is applied to image classification in publicly available data of Hyper Suprime-Cam Subaru Strategic Program. Matthews correlation coefficient is calculated based on results of binary star-galaxy classification and used as a fitting function of the GA module of the algorithm. The simulation results confirm significant improvement of the classification accuracy with optimized hyperparameters.

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79 ASTRONOMY AND ASTROPHYSICS97 MATHEMATICS AND COMPUTINGgaussian processesgenetic algorithms

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Filippov, A., Goumiri, I., Priest, B.. 2020-08-25. Genetic Algorithm for Hyperparameter Optimization in Gaussian Process Modeling. https://doi.org/10.2172/1659396

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