Cartesian genetic programming

Cartesian genetic programming is a form of <a href="/facts/Genetic_programming/kGa2jr0w">genetic programming</a> that uses a <a href="/facts/Graph_representation/b8qcddXa">graph representation</a> to encode <a href="/facts/Computer_program/e38jH9N9">computer programs</a>. It grew from a method of evolving <a href="/facts/Digital_circuits/3kJcKbkw">digital circuits</a> developed by Julian F. Miller and Peter Thomson in 1997. The term ‘Cartesian genetic programming’ first appeared in 1999 and was proposed as a general form of genetic programming in 2000. It is called ‘<a href="/facts/Cartesian_coordinate_system/lkTqvMmB">Cartesian</a>’ because it represents a program using a two-dimensional grid of <a href="/facts/Node_(computer_science)/eYlvVE0z">nodes</a>.
Miller's keynote explains how CGP works. He edited a book entitled Cartesian Genetic Programming, published in 2011 by <a href="/facts/Springer_Science%2BBusiness_Media/nAesf6nT">Springer</a>.
The open source project dCGP implements a differentiable version of CGP developed at the European Space Agency by Dario Izzo, Francesco Biscani and Alessio Mereta 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.

Cartesian genetic programming open-in-new

Cartesian genetic programming