d-CGP / dcgpy
Implementation of differentiable Cartesian Genetic Programming (d-CGP)
The d-CGP is a recent development in the field of Genetic Programming that adds the information about the derivatives of the output nodes (the programs, or expressions encoded) with respect to the input nodes (the input values) and weights. In doing so, it enables a number of new applications currently the subject of active research.
The evolution of the genetic program can now be helped by using the information on the derivatives, enabling for the equivalent of backpropagation in Neural Networks. The fitness function can be defined in terms of the derivatives, allowing to go beyond simple regression tasks and, instead, solve differential equations, learn differential models, capture conserved quantities in dynamical systems.
dcgpy can be installed, in some commonly used platform, using pip and conda package managers.
We have developed a port of the d-CPG to javascript that allows to experiment with some limited functionality in a web application without the need of installation.