By Julian F. Miller
Cartesian Genetic Programming (CGP) is a powerful and more and more well known type of genetic programming. It represents courses within the kind of directed graphs, and a selected attribute is that it has a hugely redundant genotype–phenotype mapping, in that genes will be noncoding. It has spawned a few new types, every one enhancing at the potency, between them modular, or embedded, CGP, and self-modifying CGP. it's been utilized to many difficulties in either machine technology and utilized sciences.
This ebook includes chapters written via the top figures within the improvement and alertness of CGP, and it'll be crucial interpreting for researchers in genetic programming and for engineers and scientists fixing functions utilizing those concepts. it's going to even be valuable for complicated undergraduates and postgraduates looking to comprehend and make the most of a hugely effective type of genetic programming.
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Additional resources for Cartesian Genetic Programming
Efficient representation of Recurrent Neural Networks for Markovian/Non-Markovian Non-linear Control Problems. E. Hassanien, A. Abraham, F. Marcelloni, H. Hagras, M. P. ) Proc. International Conference on Intelligent Systems Design and Applications, pp. 615–620. IEEE (2010) 4. : A Self-Scaling Instruction Generator Using Cartesian Genetic Programming. In: Proc. European Conference on Genetic Programming, LNCS, vol. 6621, pp. 299–310. Springer (2011) 5. : An Empirical Study of the Efficiency of Learning Boolean Functions using a Cartesian Genetic Programming Approach.
Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge, Massachusetts (1994) 22. : Using Genetic Algorithms to Design Structures. Tech. Rep. 326, Department of Computer Science, Indiana University (1990) 23. : Designer Genetic Algorithms: Genetic Algorithms in Structure Design. In: Proc. International Conference on Genetic Algorithms, pp. 53–60. Morgan Kauffmann (1991) 24. : Genetic algorithms as a computational tool for design. D. thesis, Department of Computer Science, Indiana University (1993) 25.
A Push language reference can be found in . 3 2 The language Forth is a well-known example. Source code is available for research versions of the Push interpreter and PushGP from Lee Spector’s website. 3 1 Intro. 5 Cartesian Graph-Based GP Unlike trees, where there is always a unique path between any pair of nodes, graphs allow more than one path between any pair of nodes. If we assume all nodes carry out some computational function, representing functions in the form of graphs is more compact than trees since they allow the reuse of previously calculated subgraphs.