Evolutionary Learning of Scheduling Heuristics for Heterogeneous Wireless Communications Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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