Cartesian genetic programming

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Evolutionary algorithm

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* Natural evolution strategy
* Promoter based genetic algorithm
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Genetic algorithm

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Genetic programming

* Cartesian genetic programming
* Linear genetic programming
* Multi expression programming
* Schema
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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.[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

## 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. https://books.google.com/books?id=_w7jx5KS0b8C.
5. ↑ "Julian Miller - Tutorial: Cartesian Genetic Programming". https://www.youtube.com/watch?v=qb2R0rL4OHQ.
6. ↑ Miller, Julian F., ed (2011) (in en-gb). Cartesian Genetic Programming. doi:10.1007/978-3-642-17310-3. ISBN 978-3-642-17309-7.
7. ↑ "dCGP v1.5". https://darioizzo.github.io/dcgp/index.html.
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)

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Evolutionary computation

Main Topics|

* Convergence (evolutionary computing)
* Evolutionary algorithm
* Evolutionary data mining
* Evolutionary multimodal optimization
* Human-based evolutionary computation
* Interactive evolutionary computation

Algorithms|

* Cellular evolutionary algorithm
* Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
* Differential evolution
* Evolutionary programming
* Genetic algorithm
* Genetic programming
* Gene expression programming
* Evolution strategy
* Natural evolution strategy
* Neuroevolution
* Learning classifier system

Related techniques|

* Swarm intelligence
* Ant colony optimization
* Bees algorithm
* Cuckoo search
* Particle swarm optimization
* Bacterial Colony Optimization

Metaheuristic methods|

* Grey Wolf Optimizer
* Firefly algorithm
* Harmony search
* Gaussian adaptation
* Memetic algorithm

Related topics|

* Artificial development
* Artificial intelligence
* Artificial life
* Digital organism
* Evolutionary robotics
* Fitness function
* Fitness landscape
* Fitness approximation
* Genetic operators
* Interactive evolutionary computation
* No free lunch in search and optimization
* Machine learning
* Mating pool
* Program synthesis

Journals|

* Evolutionary Computation (journal)

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