Differentiable Genetic Programming

Understand

We introduce the use of high order automatic differentiation, implemented via the algebra of truncated Taylor polynomials, in genetic programming.

• Using the Cartesian Genetic Programming encoding we obtain a high-order Taylor representation of the program output that is then used to back-propagate errors during learning.
• The resulting machine learning framework is called differentiable Cartesian Genetic Programming (dCGP).
• In the context of symbolic regression, dCGP offers a new approach to the long unsolved problem of constant representation in GP expressions.