Cartesian genetic programming: Difference between revisions

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{{Evolutionary algorithms}}

'''Cartesian genetic programming''' is a form of [[genetic programming]], whichthat uses a [[graph representation]] to encode [[computer programsprogram]]s. It grew from a method of evolving [[digital circuits]] developed by Julian F. Miller etand al.Peter Thomson in 1997.<ref>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)</ref>. However theThe term ‘Cartesian genetic programming’ first appeared in 1999<ref> 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)</ref> and was proposed as a general form of genetic programming in 2000.<ref> Miller, J.F., Thomson, P.: Cartesian Genetic Programming. In: Proc. European Conference on Genetic Programming, LNCS, vol. 1802, pp. 121–132. Springer (2000)</ref> It is called ‘[[Cartesian coordinate system|Cartesian]]’ because it represents a program using a two-dimensional grid of [[Node (computer science)|nodes]].<ref name="SumathiHamsapriya2008">{{cite book|author1=S. Sumathi|author2=T. Hamsapriya|author3=P. Surekha|title=Evolutionary Intelligence: An Introduction to Theory and Applications with Matlab|url=https://books.google.com/books?id=_w7jx5KS0b8C|date=15 May 2008|publisher=Springer Science & Business Media|isbn=978-3-540-75382-7|pages=201–}}</ref>

Genetic Programming, LNCS, vol. 1802, pp. 121–132. Springer (2000)</ref>. It is called ‘Cartesian’ because it represents a program using a two-dimensional grid of nodes.

Julian F. Miller's (the inventor of CGP) has a web sitekeynote<ref>{{Cite web|url=httphttps://www.cartesiangpyoutube.co.ukcom/watch?v=qb2R0rL4OHQ|title=CGPJulian homeMiller - Tutorial: Cartesian Genetic Programming|website=www.cartesiangp.co.uk[[YouTube]] |access-date=2018-08-0230 July 2020 }}</ref> which explains how CGP works. You can download many publications about CGP from this site. He also edited a book titledentitled ''Cartesian Genetic Programming'',<ref>{{Cite journalbook|date=2011|editor-last=Miller|editor-first=Julian F.|title=Cartesian Genetic Programming|url=https://link.springer.com/book/10.1007/978-3-642-17310-3|journal=Natural Computing Series|language=en-gb|doi=10.1007/978-3-642-17310-3|issn=1619-7127|isbn=978-3-642-17309-7|citeseerx=10.1.1.8.3777}}</ref>', published in 2011 by [[Springer Science+Business Media|Springer]].

The open source project dCGP<ref>{{Cite web|url=https://darioizzo.github.io/dcgp/index.html|website=github.com|access-date=2018-08-02|title=dCGP v1.5}}</ref> implements a differentiable version of CGP developed at the European Space Agency by Dario Izzo, Francesco Biscani and Alessio Mereta <ref>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)</ref> 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 ==

* [[Category:Genetic programming]]

* [[Gene expression programming]]

* [[Grammatical evolution]]

* [[Linear genetic programming]]

* [[Multi expression programming]]

== References ==

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{{Evolutionary computation}}

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