- 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)",
