A Search for Improved Performance in Regular Expressions

Created by W.Langdon from gp-bibliography.bib Revision:1.4128

@InProceedings{Cody-Kenny:2017:GECCOa,
  author =       "Brendan Cody-Kenny and Michael Fenton and 
                 Adrian Ronayne and Eoghan Considine and Thomas McGuire and 
                 Michael O'Neill",
  title =        "A Search for Improved Performance in Regular
                 Expressions",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "1280--1287",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3071178.3071196",
  DOI =          "doi:10.1145/3071178.3071196",
  acmid =        "3071196",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, performance,
                 regular expressions",
  month =        "15-19 " # jul,
  abstract =     "The primary aim of automated performance improvement
                 is to reduce the running time of programs while
                 maintaining (or improving on) functionality. In this
                 paper, Genetic Programming is used to find performance
                 improvements in regular expressions for an array of
                 target programs, representing the first application of
                 automated software improvement for run-time performance
                 in the Regular Expression language. This particular
                 problem is interesting as there may be many possible
                 alternative regular expressions which perform the same
                 task while exhibiting subtle differences in
                 performance. A benchmark suite of candidate regular
                 expressions is proposed for improvement. We show that
                 the application of Genetic Programming techniques can
                 result in performance improvements in all cases.

                 As we start evolution from a known good regular
                 expression, diversity is critical in escaping the local
                 optima of the seed expression. In order to understand
                 diversity during evolution we compare an initial
                 population consisting of only seed programs with a
                 population initialised using a combination of a single
                 seed individual with individuals generated using PI
                 Grow and Ramped-half-and-half initialisation
                 mechanisms.",
  notes =        "Also known as
                 \cite{Cody-Kenny:2017:SIP:3071178.3071196} 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 Brendan Cody-Kenny Michael Fenton Adrian Ronayne Eoghan Considine Thomas McGuire Michael O'Neill

Citations