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

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Title: A symbolic regression approach to manage femtocell coverage using grammatical genetic programming
Authors: Hemberg, Erik
Ho, Lester
O'Neill, Michael
Claussen, Holger
Permanent link: http://hdl.handle.net/10197/3511
Date: 2011
Online since: 2012-02-16T12:03:30Z
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.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2011 ACM
Keywords: FemtocellGrammatical evolutionSymbolic regressionWireless networks
Subject LCSH: Femtocells
Evolutionary computation
Wireless sensor networks
DOI: 10.1145/2001858.2002061
Other versions: http://doi.acm.org/10.1145/2001858.2002061
Language: en
Status of Item: Peer reviewed
Is part of: GECCO '11 : Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Conference Details: Paper presented at the ACM Genetic and Evolutionary Computation Conference GECCO 2011 Symbolic Regression and Modelling Workshop, Dublin, Ireland, 12-16, July
ISBN: 978-1-4503-0690-4
Appears in Collections:Computer Science Research Collection
CASL Research Collection

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