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

Authors:

Michael O'Neill,

Holger ClaussenAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

Pages 639 - 646

https://doi.org/10.1145/2001858.2002061

Published: 12 July 2011 Publication History

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.

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Datta SRokade SRajput S(2021)Retracted: Uncontrolled Intersection Operational Assessment under Mixed Traffic ConditionsJournal of Transportation Engineering, Part A: Systems10.1061/JTEPBS.0000490147:3Online publication date: Mar-2021
https://doi.org/10.1061/JTEPBS.0000490
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A symbolic regression approach to manage femtocell coverage using grammatical genetic programming
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies

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cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

July 2011

1548 pages

ISBN:9781450306904

DOI:10.1145/2001858

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 July 2011

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GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Patnaik AChaulia SBhuyan P(2021)Roundabout entry capacity models: genetic programming approachProceedings of the Institution of Civil Engineers - Transport10.1680/jtran.17.00089(1-15)Online publication date: 7-Jan-2021
https://doi.org/10.1680/jtran.17.00089
Datta SRokade SRajput S(2021)Retracted: Uncontrolled Intersection Operational Assessment under Mixed Traffic ConditionsJournal of Transportation Engineering, Part A: Systems10.1061/JTEPBS.0000490147:3Online publication date: Mar-2021
https://doi.org/10.1061/JTEPBS.0000490
Yang BBao WChen Y(2020)Time Series Prediction Based on Complex-Valued S-System ModelComplexity10.1155/2020/63938052020Online publication date: 28-May-2020
https://dl.acm.org/doi/10.1155/2020/6393805
Fenton MLynch DFagan DKucera SClaussen HO'Neill M(2019)Towards Automation and Augmentation of the Design of Schedulers for Cellular Communications NetworksEvolutionary Computation10.1162/evco_a_0022127:2(345-375)Online publication date: Jun-2019
https://doi.org/10.1162/evco_a_00221
O’Neill MFagan D(2019)The Elephant in the Room: Towards the Application of Genetic Programming to Automatic ProgrammingGenetic Programming Theory and Practice XVI10.1007/978-3-030-04735-1_9(179-192)Online publication date: 24-Jan-2019
https://doi.org/10.1007/978-3-030-04735-1_9
Fenton MLynch DFagan DKucera SClaussen HO'Neill MTakadama K(2018)Towards automation & augmentation of the design of schedulers for cellular communications networksProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208211(17-18)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3208211
Nicolau MFenton MNeumann FSutton A(2016)Managing Repetition in Grammar-Based Genetic ProgrammingProceedings of the Genetic and Evolutionary Computation Conference 201610.1145/2908812.2908904(765-772)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908812.2908904
Lynch DFenton MKucera SClaussen HO'Neill MNeumann FSutton A(2016)Evolutionary Learning of Scheduling Heuristics for Heterogeneous Wireless Communications NetworksProceedings of the Genetic and Evolutionary Computation Conference 201610.1145/2908812.2908903(949-956)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908812.2908903
Fenton MLynch DKucera SClaussen HO’Neill M(2016)Evolving Coverage Optimisation Functions for Heterogeneous Networks Using Grammatical Genetic ProgrammingApplications of Evolutionary Computation10.1007/978-3-319-31204-0_15(219-234)Online publication date: 15-Mar-2016
https://doi.org/10.1007/978-3-319-31204-0_15
Lynch DFenton MKucera SClaussen HO’Neill M(2016)Scheduling in Heterogeneous Networks Using Grammar-Based Genetic ProgrammingGenetic Programming10.1007/978-3-319-30668-1_6(83-98)Online publication date: 24-Mar-2016
https://doi.org/10.1007/978-3-319-30668-1_6
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