Cartesian genetic programming

Cartesian genetic programming is a form of genetic programming that uses a graph representation to encode computer programs. It grew from a method of evolving digital circuits developed by Julian F. Miller and Peter Thomson in 1997.^[1] The term ‘Cartesian genetic programming’ first appeared in 1999^[2] and was proposed as a general form of genetic programming in 2000.^[3] It is called ‘Cartesian’ because it represents a program using a two-dimensional grid of nodes.^[4]

Miller's keynote^[5] explains how CGP works. He edited a book entitled Cartesian Genetic Programming,^[6] published in 2011 by Springer.

The open source project dCGP^[7] implements a differentiable version of CGP developed at the European Space Agency by Dario Izzo, Francesco Biscani and Alessio Mereta ^[8] able to approach symbolic regression tasks, to find solution to differential equations, find prime integrals of dynamical systems, represent variable topology artificial neural networks and more.

See also

• Genetic programming
• Gene expression programming
• Grammatical evolution
• Linear genetic programming
• Multi expression programming