- 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 scenarios.",
-
notes = "Also known as \cite{2331028} Distributed at
GECCO-2012.
ACM Order Number 910122.",
