Evolving Femtocell Algorithms with Dynamic \& Stationary Training Scenarios

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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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