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Zusammenfassung

The general challenges of experimenting with simulation algorithms have already been discussed in chapter 3 (p. 93). This chapter builds up on that by briefly outlining how these challenges are currently met by the experimentation layer of JAMES II (sec. 7.1). Section 7.2 introduces a simple yet powerful algorithm selection technique that can be plugged into the experimentation layer. It both explores and exploits algorithm performance at the processing time of a simulation problem, but without relying on the performance database or the data analysis methods presented in chapter 5 (p. 153) and chapter 6 (p. 177). Then, section 7.3 shows how combining this technique with additional components facilitates the execution of large-scale experiments to investigate the runtime performance of simulation algorithms. A calibration method that yields significant speed-up for such experiments is also introduced (sec. 7.3.2).

Keywords

Algorithm Selection Portfolio Selection Simulation Algorithm Experimentation Methodology Benchmark Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden GmbH 2012

Authors and Affiliations

  • Roland Ewald

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