A symbolic regression approach to manage femtocell coverage using grammatical genetic programming

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Abstract

We present a novel application of Grammatical Evolution to the real-world application of femtocell coverage. A symbolic regression approach is adopted in which we wish to uncover an expression to automatically manage the power settings of individual femtocells in a larger femtocell group to optimise the coverage of the network under time varying load. The generation of symbolic expressions is important as it facilitates the analysis of the evolved solutions. Given the multi-objective nature of the problem we hybridise Grammatical Evolution with NSGA-II connected to tabu search. The best evolved solutions have superior power consumption characteristics than a fixed coverage femtocell deployment.

Original languageEnglish
Title of host publicationGenetic and Evolutionary Computation Conference, GECCO'11 - Companion Publication
Pages639-646
Number of pages8
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event13th Annual Genetic and Evolutionary Computation Conference, GECCO'11 - Dublin, Ireland
Duration: 12 Jul 201116 Jul 2011

Publication series

NameGenetic and Evolutionary Computation Conference, GECCO'11 - Companion Publication

Conference

Conference13th Annual Genetic and Evolutionary Computation Conference, GECCO'11
Country/TerritoryIreland
CityDublin
Period12/07/1116/07/11

Keywords

  • femtocell
  • grammatical evolution
  • symbolic regression
  • wireless networks

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