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.[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

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. ^ S. 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.
  5. ^ "Julian Miller - Tutorial: Cartesian Genetic Programming". YouTube. 30 July 2020.
  6. ^ Miller, 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 uses deprecated parameter |citeseerx= (help)
  7. ^ "dCGP v1.5". github.com. Retrieved 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)


Stub icon

This computer-programming-related article is a stub. You can help Wikipedia by adding missing information.

Stub icon

This machine learning–related article is a stub. You can help Wikipedia by adding missing information.

Content Disclaimer

Informasi ini disarikan dari Wikipedia dan disajikan kembali untuk tujuan edukasi. Konten tersedia di bawah lisensi CC BY-SA 3.0. Kami tidak bertanggung jawab atas ketidakakuratan data yang bersumber dari kontribusi publik tersebut.

  1. The information displayed on this website is sourced in part or in whole from Wikipedia and has been adapted for the purpose of restating it. We strive to provide accurate and relevant information, however:
  2. There is no guarantee of absolute accuracy. Wikipedia is an open, collaborative project that can be edited by anyone, so information is subject to change.
  3. It is not intended to constitute professional advice. The content displayed is for informational and educational purposes only. For important decisions (e.g., medical, legal, or financial), please consult a professional.
  4. Content copyright. Wikipedia is licensed under the Creative Commons Attribution-ShareAlike License (CC BY-SA). This means that content may be reused with appropriate attribution and shared under a similar license.
  5. Responsible use. Any risk arising from the use of information from this website is entirely the responsibility of the user.