Performance Testing of Automated Modeling for Industrial Applications

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

@InProceedings{Sherry:2017:GECCO,
  author =       "Dylan Sherry and Michael Schmidt",
  title =        "Performance Testing of Automated Modeling for
                 Industrial Applications",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "1605--1612",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3067695.3082534",
  DOI =          "doi:10.1145/3067695.3082534",
  acmid =        "3082534",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, benchmark,
                 case study, machine learning, performance test",
  month =        "15-19 " # jul,
  abstract =     "We present a case study of the performance testing of
                 a commercially engineered genetic programming algorithm
                 applied to the automated modelling of industrial
                 machine learning problems. This paper summarizes some
                 of what has been learned over the past five years of
                 working with a large number of industrial machine
                 learning challenges in a commercial or enterprise
                 setting. Automation and parallelism via cloud computing
                 is used to reduce test time. Two frameworks for
                 conducting performance tests are discussed,
                 highlighting the advantages of collecting statistics
                 throughout the search. A performance test suite of
                 industrial machine learning problems is described, and
                 examples of performance test results are shown.
                 Finally, a summary of challenges and open questions is
                 provided.",
  notes =        "Also known as \cite{Sherry:2017:PTA:3067695.3082534}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",
}

Genetic Programming entries for Dylan Sherry Michael D Schmidt

Citations