A comparison of grammatical genetic programming grammars for controlling femtocell network coverage

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@Article{Hemberg:2013:GPEM,
  author =       "Erik Hemberg and Lester Ho and Michael O'Neill and 
                 Holger Claussen",
  title =        "A comparison of grammatical genetic programming
                 grammars for controlling femtocell network coverage",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2013",
  volume =       "14",
  number =       "1",
  pages =        "65--93",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 evolution, Grammars, Femtocell, Symbolic regression",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-012-9171-8",
  size =         "20 pages",
  abstract =     "We study grammars used in grammatical genetic
                 programming (GP) which create algorithms that control
                 the base station pilot power in a femtocell network.
                 The overall goal of evolving algorithms for femtocells
                 is to create a continuous online evolution of the
                 femtocell pilot power control algorithm in order to
                 optimise their coverage. We compare the performance of
                 different grammars and analyse the femtocell simulation
                 model using the grammatical genetic programming method
                 called grammatical evolution. The grammars consist of
                 conditional statements or mathematical functions as are
                 used in symbolic regression applications of GP, as well
                 as a hybrid containing both kinds of statements. To
                 benchmark and gain further information about our
                 femtocell network simulation model we also perform
                 random sampling and limited enumeration of femtocell
                 pilot power settings. The symbolic regression based
                 grammars require the most configuration of the
                 evolutionary algorithm and more fitness evaluations,
                 whereas the conditional statement grammar requires more
                 domain knowledge to set the parameters. The content of
                 the resulting femtocell algorithms shows that the
                 evolutionary computation (EC) methods are exploiting
                 the assumptions in the model. The ability of EC to
                 exploit bias in both the fitness function and the
                 underlying model is vital for identifying the current
                 system and improves the model and the EC method.
                 Finally, the results show that the best fitness and
                 engineering performances for the grammars are similar
                 over both test and training scenarios. In addition, the
                 evolved solutions' performance is superior to those
                 designed by humans.",
  notes =        "Recommended by Una-May O'Reilly and Steven
                 Gustafson.",
  affiliation =  "Complex and Adaptive Systems Laboratory, School of
                 Computer Science and Informatics, University College
                 Dublin, Dublin, Ireland",
}

Genetic Programming entries for Erik Hemberg Lester T W Ho Michael O'Neill Holger Claussen

Citations