Developments in Cartesian Genetic Programming: self-modifying CGP

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

In: Genetic programming and evolvable machines, Vol. 11, No. 3-4, 09.2010, p. 397-439.

Research output: Contribution to journalArticle

Harvard

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

APA

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

Vancouver

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

Author

Harding, Simon ; Miller, Julian F. ; Banzhaf, Wolfgang. / Developments in Cartesian Genetic Programming : self-modifying CGP. In: Genetic programming and evolvable machines. 2010 ; Vol. 11, No. 3-4. pp. 397-439.

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@article{d406f1a9a77c4d559066905e378ebf8e,
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",

}

RIS (suitable for import to EndNote) - Download

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

UR - http://www.scopus.com/inward/record.url?scp=77954959457&partnerID=8YFLogxK

U2 - 10.1007/s10710-010-9114-1

DO - 10.1007/s10710-010-9114-1

M3 - Article

VL - 11

SP - 397

EP - 439

JO - Genetic programming and evolvable machines

JF - Genetic programming and evolvable machines

SN - 1389-2576

IS - 3-4

ER -