Evolutionary Learning of Scheduling Heuristics for Heterogeneous Wireless Communications Networks
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Lynch:2016:GECCO,
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author = "David Lynch and Michael Fenton and Stepan Kucera and
Holger Claussen and Michael O'Neill",
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title = "Evolutionary Learning of Scheduling Heuristics for
Heterogeneous Wireless Communications Networks",
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booktitle = "GECCO '16: Proceedings of the 2016 Annual Conference
on Genetic and Evolutionary Computation",
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year = "2016",
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editor = "Tobias Friedrich and Frank Neumann and
Andrew M. Sutton and Martin Middendorf and Xiaodong Li and
Emma Hart and Mengjie Zhang and Youhei Akimoto and
Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and
Daniele Loiacono and Julian Togelius and
Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and
Faustino Gomez and Carlos M. Fonseca and
Heike Trautmann and Alberto Moraglio and William F. Punch and
Krzysztof Krawiec and Zdenek Vasicek and
Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and
Boris Naujoks and Enrique Alba and Gabriela Ochoa and
Simon Poulding and Dirk Sudholt and Timo Koetzing",
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pages = "949--956",
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keywords = "genetic algorithms, genetic programming",
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month = "20-24 " # jul,
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organisation = "SIGEVO",
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address = "Denver, USA",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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isbn13 = "978-1-4503-4206-3",
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DOI = "doi:10.1145/2908812.2908903",
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abstract = "Network operators are struggling to cope with
exponentially increasing demand. Capacity can be
increased by densifying existing Macro Cell deployments
with Small Cells. The resulting two-tiered architecture
is known as a Heterogeneous Network or HetNet.
Significant inter-tier interference in channel sharing
HetNets is managed by resource interleaving in the time
domain. A key task in this regard is scheduling User
Equipment to receive data at Small Cells. Grammar-based
Genetic Programming (GBGP) is employed to evolve models
that map measurement reports to schedules on a
millisecond timescale. Two different fitness functions
based on evaluative and instructive feedback are
compared. The former expresses an industry standard
utility of downlink rates. Instructive feedback is
obtained by computing highly optimised schedules
offline using a Genetic Algorithm, which then act as
target semantics for evolving models. This paper also
compares two schemes for mapping the GBGP parse trees
to Boolean schedules. Simulations show that the
proposed system outperforms a state of the art
benchmark and is within 17percent of the estimated
theoretical optimum. The impressive performance of GBGP
illustrates an opportunity for the further use of
evolutionary techniques in software-defined wireless
communications networks.",
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notes = "GECCO-2016 A Recombination of the 25th International
Conference on Genetic Algorithms (ICGA-2016) and the
21st Annual Genetic Programming Conference (GP-2016)",
- }
Genetic Programming entries for
David Lynch
Michael Fenton
Stepan Kucera
Holger Claussen
Michael O'Neill
Citations