Hemberg, ErikErikHembergHo, LesterLesterHoO'Neill, MichaelMichaelO'NeillClaussen, HolgerHolgerClaussen2012-02-162012-02-162011 ACM2011978-1-4503-0690-4http://hdl.handle.net/10197/3511Paper presented at the ACM Genetic and Evolutionary Computation Conference GECCO 2011 Symbolic Regression and Modelling Workshop, Dublin, Ireland, 12-16, JulyWe 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.2491337 bytesapplication/pdfenThis 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 the GECCO '11 Proceedings of the 13th annual conference companion on Genetic and evolutionary computation http://doi.acm.org/10.1145/2001858.2002061FemtocellGrammatical evolutionSymbolic regressionWireless networksFemtocellsEvolutionary computationWireless sensor networksConference Publication10.1145/2001858.2002061https://creativecommons.org/licenses/by-nc-sa/1.0/