A Hierarchical Approach to Grammar-Guided Genetic Programming: The Case of Scheduling in Heterogeneous Networks

  • Takfarinas SaberEmail author
  • David Fagan
  • David Lynch
  • Stepan Kucera
  • Holger Claussen
  • Michael O’Neill
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11324)


Grammar-Guided Genetic Programming has shown its capability to evolve beyond human-competitive transmission schedulers for the benefit of large and heterogeneous communications networks. Despite this performance, a large margin of improvement is demonstrated to still exist. We have recently proposed a multi-level grammar approach which evolves structurally interesting individuals using a small grammar, before introducing a thorough grammar to probe a larger search space and evolve better-performing individuals. We investigate the advantage of using a hierarchical approach with multiple small grammars at the lower level instead of a unique one, in conjunction with a full grammar at the upper level. While we confirm in our experiment that the multi-level approach outperforms the use of a unique grammar, we demonstrate that two hierarchical grammar configurations achieve significantly better results than the multi-level approach. We also show the existence of an ideal number of small grammars that could be used in the lower level of the hierarchical approach to achieve the best performance.


Genetic programming Telecommunications Hierarchical grammar-guided genetic programming Heterogeneous network 



This research is based upon works supported by the Science Foundation Ireland under Grant No. 13/IA/1850.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Natural Computing Research and Applications Group, School of BusinessUniversity College DublinDublinIreland
  2. 2.Bell LaboratoriesNokiaDublinIreland

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