Optimising Quantisation Noise in Energy Measurement
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Langdon:2016:PPSN,
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author = "William B. Langdon and Justyna Petke and
Bobby R. Bruce",
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title = "Optimising Quantisation Noise in Energy Measurement",
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booktitle = "14th International Conference on Parallel Problem
Solving from Nature",
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year = "2016",
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editor = "Julia Handl and Emma Hart and Peter R. Lewis and
Manuel Lopez-Ibanez and Gabriela Ochoa and
Ben Paechter",
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volume = "9921",
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series = "LNCS",
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pages = "249--259",
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address = "Edinburgh",
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month = "17-21 " # sep,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, theory,
genetic improvement, software engineering, SBSE,
parallel evolutionary computing, search, heuristic
methods, artificial intelligence, distributed power
monitoring",
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isbn13 = "978-3-319-45823-6",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Langdon_2016_PPSN.pdf",
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DOI = "doi:10.1007/978-3-319-45823-6_23",
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size = "10 pages",
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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.",
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notes = "Based on \cite{langdon:RN1601}, PPSN2016",
- }
Genetic Programming entries for
William B Langdon
Justyna Petke
Bobby R Bruce
Citations