Evolving Femtocell Algorithms with Dynamic \& Stationary Training Scenarios

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{conf/ppsn/HembergHOC12,
  author =       "Erik Hemberg and Lester Ho and Michael O'Neill and 
                 Holger Claussen",
  title =        "Evolving Femtocell Algorithms with Dynamic \&
                 Stationary Training Scenarios",
  booktitle =    "Parallel Problem Solving from Nature, PPSN XII (part
                 2)",
  year =         "2012",
  editor =       "Carlos A. {Coello Coello} and Vincenzo Cutello and 
                 Kalyanmoy Deb and Stephanie Forrest and 
                 Giuseppe Nicosia and Mario Pavone",
  volume =       "7492",
  series =       "Lecture Notes in Computer Science",
  pages =        "518--527",
  address =      "Taormina, Italy",
  month =        sep # " 1-5",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, femtocell",
  isbn13 =       "978-3-642-32963-0",
  DOI =          "doi:10.1007/978-3-642-32964-7_52",
  size =         "10 pages",
  abstract =     "We analyse the impact of dynamic training scenarios
                 when evolving algorithms for femtocells, which are low
                 power, low-cost, user-deployed cellular base stations.
                 Performance is benchmarked against an alternative
                 stationary training strategy where all scenarios are
                 presented to each individual in the evolving population
                 during each fitness evaluation. In the dynamic setup,
                 different training scenarios are gradually exposed to
                 the population over successive generations. The results
                 show that the solutions evolved using the stationary
                 training scenarios have the best out-of-sample
                 performance. Moreover, the use of a grammar which
                 produces discrete changes to the pilot power generate
                 better solutions on the training and out-of-sample
                 scenarios.",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  affiliation =  "Natural Computing Research and Applications Group,
                 University College Dublin, Ireland",
}

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

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