Systematic adoption of genetic programming for deriving software performance curves

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{FaHa2012-ICPE,
  author =       "Michael Faber and Jens Happe",
  title =        "Systematic adoption of genetic programming for
                 deriving software performance curves",
  booktitle =    "Proceedings of the third joint WOSP/SIPEW
                 international conference on Performance Engineering",
  year =         "2012",
  pages =        "33--44",
  address =      "Boston, USA",
  month =        apr # " 22-25",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, SBSE,
                 black-box approach, machine learning, model inference,
                 software performance engineering",
  isbn13 =       "978-1-4503-1202-8",
  URL =          "http://sdqweb.ipd.kit.edu/publications/pdfs/FaHa2012-ICPE.pdf",
  DOI =          "doi:10.1145/2188286.2188295",
  size =         "12 pages",
  abstract =     "Measurement-based approaches to software performance
                 engineering apply analysis methods (e.g., statistical
                 inference or machine learning) on raw measurement data
                 with the goal to build a mathematical model describing
                 the performance-relevant behaviour of a system under
                 test (SUT). The main challenge for such approaches is
                 to find a reasonable trade-off between minimising the
                 amount of necessary measurement data used to build the
                 model and maximising the model's accuracy. Most
                 existing methods require prior knowledge about
                 parameter dependencies or their models are limited to
                 only linear correlations. In this paper, we investigate
                 the applicability of genetic programming (GP) to derive
                 a mathematical equation expressing the performance
                 behaviour of the measured system (software performance
                 curve). We systematically optimised the parameters of
                 the GP algorithm to derive accurate software
                 performance curves and applied techniques to prevent
                 overfitting. We conducted an evaluation with a
                 representative MySQL database system. The results
                 clearly show that the GP algorithm outperforms other
                 analysis techniques like inverse distance weighting
                 (IDW) and multivariate adaptive regression splines
                 (MARS) in terms of model accuracy.",
  acmid =        "2188295",
  notes =        "p43 'In a final evaluation, we show that the optimized
                 GP algorithm outperforms MARS and IDW in terms of model
                 accuracy.'",
}

Genetic Programming entries for Michael Faber Jens Happe

Citations