Optimising Quantisation Noise in Energy Measurement

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9921)


We give a model of parallel distributed genetic improvement. With modern low cost power monitors; high speed Ethernet LAN latency and network jitter have little effect. The model calculates a minimum usable mutation effect based on the analogue to digital converter (ADC)’s resolution and shows the optimal test duration is inversely proportional to smallest impact we wish to detect. Using the example of a 1 kHz 12 bit 0.4095 Amp ADC optimising software energy consumption we find: it will be difficult to detect mutations which an average effect less than 58 \(\mu \)A, and typically experiments should last well under a second.


Theory Genetic improvement Genetic programming Software engineering SBSE Parallel EC Distributed power monitoring 


  1. Bruce, B.R.: Energy optimisation via genetic improvement a SBSE technique for a new era in software development. In: GECCO GI-2015 Workshop, pp. 819–820 (2015).
  2. Goodman, L.A.: On the exact variance of products. J. Am. Stat. Assoc. 55(292), 708–713 (1960). MathSciNetCrossRefzbMATHGoogle Scholar
  3. Langdon, W.B., Petke, J.: Software is not fragile. In: CS-DC 2015 Proceedings in Complexity. Springer (2015, Forthcoming).
  4. Langdon, W.B.: Genetically improved software. In: Gandomi, A.H., Alavi, A.H., Ryan, C. (eds.) Handbook of Genetic Programming Applications, Chap. 8, pp. 181–220. Springer, Heidelberg (2015). Google Scholar
  5. Li, D., et al.: Optimizing display energy consumption for hybrid Android apps. In: DeMobile 2015, Bergamo, Italy, 31 August, pp. 35–36. ACM, Invited Talk (2015).
  6. Schulte, E., et al.: Post-compiler software optimization for reducing energy. In: ASPLOS 2014, Salt Lake City, Utah, USA, 1–5 March, pp. 639–652. ACM (2014a).
  7. Schulte, E., et al.: Software mutational robustness. GP&EM 15(3), 281–312 (2014b). Google Scholar
  8. White, D.R., et al.: Searching for resource-efficient programs: low-power pseudorandom number generators. In: GECCO 2008, pp. 1775–1782. ACM (2008).

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.CREST, Department of Computer ScienceUniversity College LondonLondonUK

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