Systematic adoption of genetic programming for deriving software performance curves
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{FaHa2012-ICPE,
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author = "Michael Faber and Jens Happe",
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title = "Systematic adoption of genetic programming for
deriving software performance curves",
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booktitle = "Proceedings of the third joint WOSP/SIPEW
international conference on Performance Engineering",
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year = "2012",
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pages = "33--44",
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address = "Boston, USA",
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month = apr # " 22-25",
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publisher = "ACM",
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keywords = "genetic algorithms, genetic programming, SBSE,
black-box approach, machine learning, model inference,
software performance engineering",
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isbn13 = "978-1-4503-1202-8",
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URL = "http://sdqweb.ipd.kit.edu/publications/pdfs/FaHa2012-ICPE.pdf",
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DOI = "doi:10.1145/2188286.2188295",
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size = "12 pages",
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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.",
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acmid = "2188295",
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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