Differentiable Genetic Programming for High-dimensional Symbolic Regression.
DGP proposes a differentiable symbolic tree that relaxes the discrete structure of GP trees to a continuous representation, thus allowing for efficient gradient based optimization, and designs a sampling strategy that ensures structural validity and introduces a di versification mechanism to escape local optima and enh...