Using Algorithm Configuration Tools to Optimize Genetic Programming Parameters: A Case Study

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

@InProceedings{McPhee:2017:GECCOa,
  author =       "Nicholas Freitag McPhee and Thomas Helmuth and 
                 Lee Spector",
  title =        "Using Algorithm Configuration Tools to Optimize
                 Genetic Programming Parameters: A Case Study",
  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 =        "243--244",
  size =         "2 pages",
  URL =          "http://doi.acm.org/10.1145/3067695.3076097",
  DOI =          "doi:10.1145/3067695.3076097",
  acmid =        "3076097",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, SMAC,
                 parameter optimization, pushGP, software synthesis",
  month =        "15-19 " # jul,
  abstract =     "We use Sequential Model-based Algorithm Configuration
                 (SMAC) to optimize a group of parameters for PushGP, a
                 stack-based genetic programming system, for several
                 software synthesis problems. Applying SMAC to one
                 particular problem leads to marked improvements in the
                 success rate and the speed with which a solution was
                 found for that problem. Applying these {"}tuned{"}
                 parameters to four additional problems, however, only
                 improved performance on one, and substantially reduced
                 performance on another. This suggests that SMAC is
                 overfitting, tuning the parameters in ways that are
                 highly problem specific, and raises doubts about the
                 value of using these {"}tuned{"} parameters on
                 previously unsolved problems. Efforts to use SMAC to
                 optimize PushGP parameters on other problems have been
                 less successful due to a combination of long PushGP run
                 times and low success rates, which make it hard for
                 SMAC to acquire enough information in a reasonable
                 amount of time.",
  notes =        "Also known as \cite{McPhee:2017:UAC:3067695.3076097}
                 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 Nicholas Freitag McPhee Thomas Helmuth Lee Spector

Citations