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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 -