Research output: Contribution to journal › Article

**Developments in Cartesian Genetic Programming : self-modifying CGP.** / Harding, Simon; Miller, Julian F.; Banzhaf, Wolfgang.

Research output: Contribution to journal › Article

Harding, S, Miller, JF & Banzhaf, W 2010, 'Developments in Cartesian Genetic Programming: self-modifying CGP', *Genetic programming and evolvable machines*, vol. 11, no. 3-4, pp. 397-439. https://doi.org/10.1007/s10710-010-9114-1

Harding, S., Miller, J. F., & Banzhaf, W. (2010). Developments in Cartesian Genetic Programming: self-modifying CGP. *Genetic programming and evolvable machines*, *11*(3-4), 397-439. https://doi.org/10.1007/s10710-010-9114-1

Harding S, Miller JF, Banzhaf W. Developments in Cartesian Genetic Programming: self-modifying CGP. Genetic programming and evolvable machines. 2010 Sep;11(3-4):397-439. https://doi.org/10.1007/s10710-010-9114-1

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title = "Developments in Cartesian Genetic Programming: self-modifying CGP",

abstract = "Self-modifying Cartesian Genetic Programming (SMCGP) is a general purpose, graph-based, developmental form of Genetic Programming founded on Cartesian Genetic Programming. In addition to the usual computational functions, it includes functions that can modify the program encoded in the genotype. This means that programs can be iterated to produce an infinite sequence of programs (phenotypes) from a single evolved genotype. It also allows programs to acquire more inputs and produce more outputs during this iteration. We discuss how SMCGP can be used and the results obtained in several different problem domains, including digital circuits, generation of patterns and sequences, and mathematical problems. We find that SMCGP can efficiently solve all the problems studied. In addition, we prove mathematically that evolved programs can provide general solutions to a number of problems: n-input even-parity, n-input adder, and sequence approximation to pi.",

keywords = "Cartesian Genetic Programming, Developmental systems, EVOLUTION, ACQUISITION, MODULES",

author = "Simon Harding and Miller, {Julian F.} and Wolfgang Banzhaf",

year = "2010",

month = "9",

doi = "10.1007/s10710-010-9114-1",

language = "English",

volume = "11",

pages = "397--439",

journal = "Genetic programming and evolvable machines",

issn = "1389-2576",

publisher = "Springer New York",

number = "3-4",

}

TY - JOUR

T1 - Developments in Cartesian Genetic Programming

T2 - self-modifying CGP

AU - Harding, Simon

AU - Miller, Julian F.

AU - Banzhaf, Wolfgang

PY - 2010/9

Y1 - 2010/9

N2 - Self-modifying Cartesian Genetic Programming (SMCGP) is a general purpose, graph-based, developmental form of Genetic Programming founded on Cartesian Genetic Programming. In addition to the usual computational functions, it includes functions that can modify the program encoded in the genotype. This means that programs can be iterated to produce an infinite sequence of programs (phenotypes) from a single evolved genotype. It also allows programs to acquire more inputs and produce more outputs during this iteration. We discuss how SMCGP can be used and the results obtained in several different problem domains, including digital circuits, generation of patterns and sequences, and mathematical problems. We find that SMCGP can efficiently solve all the problems studied. In addition, we prove mathematically that evolved programs can provide general solutions to a number of problems: n-input even-parity, n-input adder, and sequence approximation to pi.

AB - Self-modifying Cartesian Genetic Programming (SMCGP) is a general purpose, graph-based, developmental form of Genetic Programming founded on Cartesian Genetic Programming. In addition to the usual computational functions, it includes functions that can modify the program encoded in the genotype. This means that programs can be iterated to produce an infinite sequence of programs (phenotypes) from a single evolved genotype. It also allows programs to acquire more inputs and produce more outputs during this iteration. We discuss how SMCGP can be used and the results obtained in several different problem domains, including digital circuits, generation of patterns and sequences, and mathematical problems. We find that SMCGP can efficiently solve all the problems studied. In addition, we prove mathematically that evolved programs can provide general solutions to a number of problems: n-input even-parity, n-input adder, and sequence approximation to pi.

KW - Cartesian Genetic Programming

KW - Developmental systems

KW - EVOLUTION

KW - ACQUISITION

KW - MODULES

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U2 - 10.1007/s10710-010-9114-1

DO - 10.1007/s10710-010-9114-1

M3 - Article

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JO - Genetic programming and evolvable machines

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