Approximate Oracles and Synergy in Software Energy Search Spaces

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@TechReport{bruce:RN1701,
  author =       "Bobby R Bruce and Justyna Petke and Mark Harman and 
                 Earl T Barr",
  title =        "Approximate Oracles and Synergy in Software Energy
                 Search Spaces",
  institution =  "University College, London",
  year =         "2017",
  number =       "RN/17/01",
  address =      "London, UK",
  month =        "25 " # jan,
  keywords =     "genetic algorithms, genetic programming, genetic
                 improvement",
  URL =          "http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/RN_17_01.PDF",
  abstract =     "There is a growing interest in using evolutionary
                 computation to reduce software systems' energy
                 consumption by using techniques such as genetic
                 improvement. However, efficient and effective
                 evolutionary optimisation of software systems requires
                 a better understanding of the energy search landscape.
                 One important choice practitioners have is whether to
                 preserve the system's original output or permit
                 approximation; each of which has its own search space
                 characteristics. When output preservation is a hard
                 constraint, we report that the maximum energy reduction
                 achievable by evolutionary mutation is 2.69percent
                 (0.76percent on average). By contrast, this figure
                 increases dramatically to 95.60percent (33.90percent on
                 average) when approximation is permitted, indicating
                 the critical importance of approximate output quality
                 assessment for effective evolutionary optimisation. We
                 investigate synergy, a phenomenon that occurs when
                 simultaneously applied evolutionary mutations produce a
                 effect greater than their individual sum. Our results
                 reveal that 12.0percent of all joint code modifications
                 produced such a synergistic effect though 38.5percent
                 produce an antagonistic interaction in which
                 simultaneously applied mutations are less effective
                 than when applied individually. This highlights the
                 need for an evolutionary approach over more greedy
                 alternatives.",
  size =         "21 pages",
}

Genetic Programming entries for Bobby R Bruce Justyna Petke Mark Harman Earl Barr

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