Evolutionary Learning of Scheduling Heuristics for Heterogeneous Wireless Communications Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Lynch:2016:GECCO,
  author =       "David Lynch and Michael Fenton and Stepan Kucera and 
                 Holger Claussen and Michael O'Neill",
  title =        "Evolutionary Learning of Scheduling Heuristics for
                 Heterogeneous Wireless Communications Networks",
  booktitle =    "GECCO '16: Proceedings of the 2016 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2016",
  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",
  pages =        "949--956",
  keywords =     "genetic algorithms, genetic programming",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4206-3",
  DOI =          "doi:10.1145/2908812.2908903",
  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.",
  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