Optimising Quantisation Noise in Energy Measurement

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Langdon:2016:PPSN,

• author = "William B. Langdon and Justyna Petke and Bobby R. Bruce",
• title = "Optimising Quantisation Noise in Energy Measurement",
• booktitle = "14th International Conference on Parallel Problem Solving from Nature",
• year = "2016",
• editor = "Julia Handl and Emma Hart and Peter R. Lewis and Manuel Lopez-Ibanez and Gabriela Ochoa and Ben Paechter",
• volume = "9921",
• series = "LNCS",
• pages = "249--259",
• address = "Edinburgh",
• month = "17-21 " # sep,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, theory, genetic improvement, software engineering, SBSE, parallel evolutionary computing, search, heuristic methods, artificial intelligence, distributed power monitoring",
• isbn13 = "978-3-319-45823-6",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Langdon_2016_PPSN.pdf",
• DOI = "doi:10.1007/978-3-319-45823-6_23",
• size = "10 pages",
• abstract = "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 1KHz 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 microA, and typically experiments should last well under a second.",
• notes = "Based on \cite{langdon:RN1601}, PPSN2016",

}

Genetic Programming entries for William B Langdon Justyna Petke Bobby R Bruce

Citations