Abstract
Co-ordination of Inter-Cell Interference through scheduling enables telecommunication companies to better exploit their Heterogeneous Networks. However, it requires from these entities to implement an effective scheduling algorithm. The state-of-the-art for the scheduling in Heterogeneous Networks is a Grammar-Guided Genetic Programming algorithm which evolves, from a given grammar, an expression that maps to the scheduling of transmissions. We evaluate in our work the possibility of improving the results obtained by the state-of-the-art using a layered grammar approach. We show that starting with a small restricted grammar and introducing the full functionality after 10 generations outperforms the state-of-the-art, even when varying the algorithm used to generate the initial population and the maximum initial tree depth.
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Number of mobile phone users worldwide from 2013 to 2019 (in billions). www.statista.com/statistics/274774/forecast-of-mobile-phone-users-worldwide
Andrews, J.G., Buzzi, S., Choi, W., Hanly, S.V., Lozano, A., Soong, A.C., Zhang, J.C.: What will 5G be? IEEE J. Sel. Areas Commun. 32(6), 1065–1082 (2014)
3GPP: The 3rd generation partnership project. www.3gpp.org
Lynch, D., Fenton, M., Kucera, S., Claussen, H., O’Neill, M.: Scheduling in heterogeneous networks using grammar-based genetic programming. In: EuroGP, pp. 83–98 (2016)
Saber, T., Marques-Silva, J., Thorburn, J., Ventresque, A.: Exact and hybrid solutions for the multi-objective vm reassignment problem. Int. J. Artif. Intell. Tools 26, 1760004 (2017)
Saber, T., Ventresque, A., Gandibleux, X., Murphy, L.: Genepi: A multi-objective machine reassignment algorithm for data centres. In: HM, pp. 115–129 (2014)
McKay, R.I.B., Hoang, T.H., Essam, D.L., Nguyen, X.H.: Developmental evaluation in genetic programming: the preliminary results. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 280–289. Springer, Heidelberg (2006). https://doi.org/10.1007/11729976_25
Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002)
Saber, T., Ventresque, A., Brandic, I., Thorburn, J., Murphy, L.: Towards a multi-objective VM reassignment for large decentralised data centres. In: UCC, pp. 65–74 (2015)
Saber, T., Thorburn, J., Murphy, L., Ventresque, A.: VM reassignment in hybrid clouds for large decentralised companies: a multi-objective challenge. Future Gener. Comput. Syst. 79, 751–764 (2018)
Nicolau, M.: Understanding grammatical evolution: initialisation. Genet. Program Evolvable Mach. 18, 467–507 (2017)
Shannon, C.E.: Communication in the presence of noise. IRE 37(1), 10–21 (1949)
Weber, A., Stanze, O.: Scheduling strategies for HetNets using eICIC. In: ICC, pp. 6787–6791 (2012)
Fagan, D., Fenton, M., Lynch, D., Kucera, S., Claussen, H., O’Neill, M.: Deep learning through evolution: a hybrid approach to scheduling in a dynamic environment. In: IJCNN, pp. 775–782 (2017)
Jiang, L., Lei, M.: Resource allocation for eICIC scheme in heterogeneous networks. In: PIMRC. pp. 448–453 (2012)
Lopez-Perez, D., Claussen, H.: Duty cycles and load balancing in HetNets with eICIC almost blank subframes. In: PIMRC, pp. 173–178 (2013)
Dempsey, I., O’Neill, M., Brabazon, A.: Foundations in Grammatical Evolution for Dynamic Environments, vol. 194. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00314-1
Hemberg, E.A.P.: An exploration of grammars in grammatical evolution. Ph.D. thesis, University College Dublin (2010)
McKay, R.I., Hoai, N.X., Whigham, P.A., Shan, Y., O’Neill, M.: Grammar-based genetic programming: a survey. Genet. Program Evolvable Mach. 11, 365–396 (2010)
Fenton, M., McDermott, J., Fagan, D., Forstenlechner, S., Hemberg, E., O’Neill, M.: PonyGE2: grammatical evolution in python. In: GECCO, pp. 1194–1201 (2017)
Saber, T., Brevet, D., Botterweck, G., Ventresque, A.: Is seeding a good strategy in multi-objective feature selection when feature models evolve? Information and Software Technology (2017)
Ryan, C., Azad, R.M.A.: Sensible initialisation in grammatical evolution. In: GECCO, pp. 142–145 (2003)
Fagan, D., Fenton, M., O’Neill, M.: Exploring position independent initialisation in grammatical evolution. In: CEC, pp. 5060–5067 (2016)
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This research is based upon works supported by the Science Foundation Ireland under Grant No. 13/IA/1850.
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Saber, T., Fagan, D., Lynch, D., Kucera, S., Claussen, H., O’Neill, M. (2018). Multi-level Grammar Genetic Programming for Scheduling in Heterogeneous Networks. In: Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2018. Lecture Notes in Computer Science(), vol 10781. Springer, Cham. https://doi.org/10.1007/978-3-319-77553-1_8
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DOI: https://doi.org/10.1007/978-3-319-77553-1_8
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