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| Content Provider | ACM Digital Library |
|---|---|
| Author | Ho, Lester Claussen, Holger Hemberg, Erik O'Neill, Michael |
| 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. |
| Starting Page | 1525 |
| Ending Page | 1526 |
| Page Count | 2 |
| File Format | |
| ISBN | 9781450311786 |
| DOI | 10.1145/2330784.2331028 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-07-07 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Symbolic regression Grammatical evolution Femtocell |
| Content Type | Text |
| Resource Type | Article |
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