Systematic adoption of genetic programming for deriving software performance curves

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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