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    Rights statement: © ACM, 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in GI '18 Proceedings of the 4th International Workshop on Genetic Improvement Workshop, 2018 10.1145/3194810.3194813

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Experiments in Genetic Divergence for Emergent Systems

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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Experiments in Genetic Divergence for Emergent Systems. / McGowan, Christopher; Wild, Alexander; Porter, Barry Francis.

GI '18 Proceedings of the 4th International Workshop on Genetic Improvement Workshop. ACM, 2018. p. 9-16.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

McGowan, C, Wild, A & Porter, BF 2018, Experiments in Genetic Divergence for Emergent Systems. in GI '18 Proceedings of the 4th International Workshop on Genetic Improvement Workshop. ACM, pp. 9-16. https://doi.org/10.1145/3194810.3194813

APA

McGowan, C., Wild, A., & Porter, B. F. (2018). Experiments in Genetic Divergence for Emergent Systems. In GI '18 Proceedings of the 4th International Workshop on Genetic Improvement Workshop (pp. 9-16). ACM. https://doi.org/10.1145/3194810.3194813

Vancouver

McGowan C, Wild A, Porter BF. Experiments in Genetic Divergence for Emergent Systems. In GI '18 Proceedings of the 4th International Workshop on Genetic Improvement Workshop. ACM. 2018. p. 9-16 https://doi.org/10.1145/3194810.3194813

Author

McGowan, Christopher ; Wild, Alexander ; Porter, Barry Francis. / Experiments in Genetic Divergence for Emergent Systems. GI '18 Proceedings of the 4th International Workshop on Genetic Improvement Workshop. ACM, 2018. pp. 9-16

Bibtex

@inproceedings{d8d01888a53749a98aefac6485a1609c,
title = "Experiments in Genetic Divergence for Emergent Systems",
abstract = "Emergent software systems take a step towards tackling the ever-increasing complexity of modern software, by having systems self-assemble from a library of building blocks, and then continually re-assemble themselves from alternative building blocks to learn which compositions of behaviour work best in each deployment environment. One of the key challenges in emergent systems is populating the library of building blocks, and particularly a set of alternative implementations of particular building blocks, which form the runtime search space of optimal behaviour. We present initial work in using a fusion of genetic improvement and genetic synthesis to automatically populate a divergent set of implementations of the same functionality, allowing emergent systems to explore new behavioural alternatives without human input. Our early results indicate this approach is able to successfully yield useful divergent implementations of building blocks which are more suited than any existing alternative for particular operating conditions.",
author = "Christopher McGowan and Alexander Wild and Porter, {Barry Francis}",
note = "{\textcopyright} ACM, 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in GI '18 Proceedings of the 4th International Workshop on Genetic Improvement Workshop, 2018 10.1145/3194810.3194813",
year = "2018",
month = jun,
day = "2",
doi = "10.1145/3194810.3194813",
language = "English",
isbn = "9781450357531",
pages = "9--16",
booktitle = "GI '18 Proceedings of the 4th International Workshop on Genetic Improvement Workshop",
publisher = "ACM",

}

RIS

TY - GEN

T1 - Experiments in Genetic Divergence for Emergent Systems

AU - McGowan, Christopher

AU - Wild, Alexander

AU - Porter, Barry Francis

N1 - © ACM, 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in GI '18 Proceedings of the 4th International Workshop on Genetic Improvement Workshop, 2018 10.1145/3194810.3194813

PY - 2018/6/2

Y1 - 2018/6/2

N2 - Emergent software systems take a step towards tackling the ever-increasing complexity of modern software, by having systems self-assemble from a library of building blocks, and then continually re-assemble themselves from alternative building blocks to learn which compositions of behaviour work best in each deployment environment. One of the key challenges in emergent systems is populating the library of building blocks, and particularly a set of alternative implementations of particular building blocks, which form the runtime search space of optimal behaviour. We present initial work in using a fusion of genetic improvement and genetic synthesis to automatically populate a divergent set of implementations of the same functionality, allowing emergent systems to explore new behavioural alternatives without human input. Our early results indicate this approach is able to successfully yield useful divergent implementations of building blocks which are more suited than any existing alternative for particular operating conditions.

AB - Emergent software systems take a step towards tackling the ever-increasing complexity of modern software, by having systems self-assemble from a library of building blocks, and then continually re-assemble themselves from alternative building blocks to learn which compositions of behaviour work best in each deployment environment. One of the key challenges in emergent systems is populating the library of building blocks, and particularly a set of alternative implementations of particular building blocks, which form the runtime search space of optimal behaviour. We present initial work in using a fusion of genetic improvement and genetic synthesis to automatically populate a divergent set of implementations of the same functionality, allowing emergent systems to explore new behavioural alternatives without human input. Our early results indicate this approach is able to successfully yield useful divergent implementations of building blocks which are more suited than any existing alternative for particular operating conditions.

U2 - 10.1145/3194810.3194813

DO - 10.1145/3194810.3194813

M3 - Conference contribution/Paper

SN - 9781450357531

SP - 9

EP - 16

BT - GI '18 Proceedings of the 4th International Workshop on Genetic Improvement Workshop

PB - ACM

ER -