TSGP uses a pre-trained transformer as a semantic variation operator in genetic programming, generalizes across d-dimensional problems, and outperforms standard GP, SLIM_GSGP, Deep Symbolic Regression, and Denoising Autoencoder GP on 24 datasets while producing more compact solutions.
Henrik Voigt, Paul Kahlmeyer, Kai Lawonn, Michael Habeck, and Joachim Giesen
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Transformer Semantic Genetic Programming for d-dimensional Symbolic Regression Problems
TSGP uses a pre-trained transformer as a semantic variation operator in genetic programming, generalizes across d-dimensional problems, and outperforms standard GP, SLIM_GSGP, Deep Symbolic Regression, and Denoising Autoencoder GP on 24 datasets while producing more compact solutions.