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.
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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.
{{
cite book}}
: |journal=
ignored (
help)