Home > Research > Publications & Outputs > EmerGen(e)tic

Associated organisational unit

Electronic data

Links

View graph of relations

EmerGen(e)tic: Exploring the use of genetic algorithms in emergent distributed systems

Research output: Other contribution

Unpublished

Standard

EmerGen(e)tic: Exploring the use of genetic algorithms in emergent distributed systems. / Goldsworthy, Ben; Porter, Barry (Editor).
47 p. 2017, BSc. (Hons) Final Year Project.

Research output: Other contribution

Harvard

APA

Vancouver

Author

Bibtex

@misc{970e912cf131431b9eb47f36a220088f,
title = "EmerGen(e)tic: Exploring the use of genetic algorithms in emergent distributed systems",
abstract = "Adaptive and emergent systems exist to attempt to answer the deficiencies inherent to distributed systems, and the necessarily finite ability of any programmer to predict all possible eventualities in which his software may one day find itself. This paper argues that these systems fail to go far enough, and then proposes a further development—genetic systems—which utilises genetic programming to extend the versatility of a given system massively, if not infinitely. This paper then proceeds to detail the EmerGen(e)tic framework for rapidly testing genetic algorithm modules within emergent systems, as well as an example module pertaining to the cache updating behaviour of a web server. This paper concludes by proposing further avenues of potentially-fruitful research based upon these programs and its findings.",
author = "Ben Goldsworthy and Barry Porter",
year = "2017",
language = "English",
type = "Other",

}

RIS

TY - GEN

T1 - EmerGen(e)tic

T2 - Exploring the use of genetic algorithms in emergent distributed systems

AU - Goldsworthy, Ben

A2 - Porter, Barry

PY - 2017

Y1 - 2017

N2 - Adaptive and emergent systems exist to attempt to answer the deficiencies inherent to distributed systems, and the necessarily finite ability of any programmer to predict all possible eventualities in which his software may one day find itself. This paper argues that these systems fail to go far enough, and then proposes a further development—genetic systems—which utilises genetic programming to extend the versatility of a given system massively, if not infinitely. This paper then proceeds to detail the EmerGen(e)tic framework for rapidly testing genetic algorithm modules within emergent systems, as well as an example module pertaining to the cache updating behaviour of a web server. This paper concludes by proposing further avenues of potentially-fruitful research based upon these programs and its findings.

AB - Adaptive and emergent systems exist to attempt to answer the deficiencies inherent to distributed systems, and the necessarily finite ability of any programmer to predict all possible eventualities in which his software may one day find itself. This paper argues that these systems fail to go far enough, and then proposes a further development—genetic systems—which utilises genetic programming to extend the versatility of a given system massively, if not infinitely. This paper then proceeds to detail the EmerGen(e)tic framework for rapidly testing genetic algorithm modules within emergent systems, as well as an example module pertaining to the cache updating behaviour of a web server. This paper concludes by proposing further avenues of potentially-fruitful research based upon these programs and its findings.

M3 - Other contribution

ER -