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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.cite-ref-1[1] The term ‘Cartesian genetic programming’ first appeared in 1999cite-ref-2[2] and was proposed as a general form of genetic programming in 2000.cite-ref-3[3] It is called ‘Cartesian’ because it represents a program using a two-dimensional grid of nodes.cite-ref-sumathihamsapriya2008-4-0[4]

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

The open source project dCGPcite-ref-7[7] implements a differentiable version of CGP developed at the European Space Agency by Dario Izzo, Francesco Biscani and Alessio Mereta cite-ref-8[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

• References

• Genetic programming

• Gene expression programming

• Grammatical evolution

• Linear genetic programming

• Multi expression programming

cite-note-11. ↑ 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)

cite-note-22. ↑ 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)

cite-note-33. ↑ Miller, J.F., Thomson, P.: Cartesian Genetic Programming. In: Proc. European Conference on Genetic Programming, LNCS, vol. 1802, pp. 121–132. Springer (2000)

cite-note-sumathihamsapriya2008-44. ↑ citerefs-sumathit-hamsapriyap-surekha2008S. Sumathi; T. Hamsapriya; P. Surekha (15 May 2008). Evolutionary Intelligence: An Introduction to Theory and Applications with Matlab. Springer Science & Business Media. pp. 201–. ISBN 978-3-540-75382-7.

cite-note-55. ↑ "Julian Miller - Tutorial: Cartesian Genetic Programming". YouTube. 30 July 2020.

cite-note-66. ↑ citerefmiller2011Miller, Julian F., ed. (2011). Cartesian Genetic Programming. CiteSeerX 10.1.1.8.3777. doi:10.1007/978-3-642-17310-3. ISBN 978-3-642-17309-7. ISSN 1619-7127. {{cite book}}: |journal= ignored (help)

cite-note-77. ↑ "dCGP v1.5". github.com. Retrieved 2018-08-02.

cite-note-88. ↑ 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)