Comparing the robustness of grammatical genetic programming solutions for femtocell algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Erik Hemberg and Lester Ho and Michael O'Neill and 
                 Holger Clausssen",
  title =        "Comparing the robustness of grammatical genetic
                 programming solutions for femtocell algorithms",
  booktitle =    "GECCO Companion '12: Proceedings of the fourteenth
                 international conference on Genetic and evolutionary
                 computation conference companion",
  year =         "2012",
  editor =       "Terry Soule and Anne Auger and Jason Moore and 
                 David Pelta and Christine Solnon and Mike Preuss and 
                 Alan Dorin and Yew-Soon Ong and Christian Blum and 
                 Dario Landa Silva and Frank Neumann and Tina Yu and 
                 Aniko Ekart and Will Browne and Tim Kovacs and 
                 Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and 
                 Giovanni Squillero and Nicolas Bredeche and 
                 Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and 
                 Martin Pelikan and Silja Meyer-Nienberg and 
                 Christian Igel and Greg Hornby and Rene Doursat and 
                 Steve Gustafson and Gustavo Olague and Shin Yoo and 
                 John Clark and Gabriela Ochoa and Gisele Pappa and 
                 Fernando Lobo and Daniel Tauritz and Jurgen Branke and 
                 Kalyanmoy Deb",
  isbn13 =       "978-1-4503-1178-6",
  keywords =     "genetic algorithms, genetic programming, Real world
                 applications: Poster",
  pages =        "1525--1526",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330784.2331028",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Methods for evolving robust solutions are necessary
                 when the evolved solutions are algorithms which are
                 deployed in actual consumer products, e.g. Femtocells,
                 low power, low-cost, user-deployed cellular base
                 stations. We compare how multiple and dynamic
                 applications of training scenarios in the evolutionary
                 search produce different solutions and performance on
                 training and test scenarios. For Femtocells, robustness
                 is especially important since each fitness evaluation
                 is a simulation that is computationally expensive.
                 Previous studies in robustness and dynamic environments
                 have not shown differences in the robustness of the
                 solution when a dynamic or multiple setup is used, or
                 if they are negligible. In the dynamic setup the
                 solution gets exposed to a multitude of scenarios
                 during the evolution. Therefore a solution could be
                 evolved which is capable of surviving, and is also more
                 general. The experiments use grammar based Genetic
                 Programming on the Femtocell problem with one grammar
                 for generating real-values and another grammar for
                 generating discrete values for changing the pilot
                 power. The results show that the solutions evolved
                 using multiple scenarios have the best test
                 performance. Moreover, the use of a grammar which
                 produces discrete changes to the pilot power generate
                 better solutions on the training and the test
  notes =        "Also known as \cite{2331028} Distributed at

                 ACM Order Number 910122.",

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