Cartesian genetic programming
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. The term ‘Cartesian genetic programming’ first appeared in 1999 and was proposed as a general form of genetic programming in 2000. It is called ‘Cartesian’ because it represents a program using a two-dimensional grid of nodes.
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.
- 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)
- 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)
- Miller, J.F., Thomson, P.: Cartesian Genetic Programming. In: Proc. European Conference on Genetic Programming, LNCS, vol. 1802, pp. 121–132. Springer (2000)
- "CGP home". www.cartesiangp.com. Retrieved 2018-08-02.
- Miller, Julian F., ed. (2011). Cartesian Genetic Programming. Natural Computing Series. CiteSeerX 10.1.1.8.3777. doi:10.1007/978-3-642-17310-3. ISBN 978-3-642-17309-7. ISSN 1619-7127.
- "dCGP v1.5". github.com. Retrieved 2018-08-02.
- 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)
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