Cartesian genetic programming explained

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

Notes and References

1. Miller, J.F., Thomson, P., Fogarty, T.C.: Designing Electronic Circuits Using Evolutionary Algorithms: Arithmetic Circuits: A Case Study. In: D. Quagliarella, J. Periaux, C. Poloni, G. Winter (eds.) Genetic Algorithms and Evolution Strategies in Engineering and Computer Science: Recent Advancements and Industrial Applications, pp. 105–131. Wiley (1998)
2. Miller, J.F.: An Empirical Study of the Efficiency of Learning Boolean Functions using a Cartesian Genetic Programming Approach. In: Proc. Genetic and Evolutionary Computation Conference, pp. 1135–1142. Morgan Kaufmann (1999)
3. Miller, J.F., Thomson, P.: Cartesian Genetic Programming. In: Proc. European Conference on Genetic Programming, LNCS, vol. 1802, pp. 121–132. Springer (2000)
4. Book: S. Sumathi. T. Hamsapriya. P. Surekha. Evolutionary Intelligence: An Introduction to Theory and Applications with Matlab. 15 May 2008. Springer Science & Business Media. 978-3-540-75382-7. 201–.
5. Web site: Julian Miller - Tutorial: Cartesian Genetic Programming. .
6. Book: 2011. Miller. Julian F.. Cartesian Genetic Programming. Natural Computing Series. en-gb. 10.1007/978-3-642-17310-3. 1619-7127. 978-3-642-17309-7. 10.1.1.8.3777.
7. Web site: dCGP v1.5. github.com. 2018-08-02.
8. Izzo, D. and Biscani, F. and Mereta, A.: Differentiable Genetic Programming. In: Proc. European Conference on Genetic Programming, LNCS, vol. 10196, pp. 35–51. Springer (2017)