Using Genetic Improvement \& Code Transplants to Specialise a C++ Program to a Problem Class

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

@Misc{Petke:2014:humie,
  author =       "Justyna Petke and Mark Harman and 
                 William B. Langdon and Westley Weimer",
  title =        "Using Genetic Improvement \& Code Transplants to
                 Specialise a C++ Program to a Problem Class",
  howpublished = "11th Annual Humies Awards 2014",
  year =         "2014",
  month =        "14 " # jul,
  note =         "Winner Silver",
  keywords =     "genetic algorithms, genetic programming, Genetic
                 Improvement",
  URL =          "http://www.genetic-programming.org/hc2014/Petke-Text.txt",
  size =         "3 page",
  abstract =     "Genetic Improvement (GI) is a form of Genetic
                 Programming that improves an existing program. We use
                 GI to evolve a faster version of a C++ program, a
                 Boolean satisfiability (SAT) solver called MiniSAT,
                 specialising it for a particular problem class, namely
                 Combinatorial Interaction Testing (CIT), using
                 automated code transplantation. Our GI-evolved solver
                 achieves overall 17percent improvement, making it
                 comparable with average expert human performance.
                 Additionally, this automatically evolved solver is
                 faster than any of the human-improved solvers for the
                 CIT problem.",
  notes =        "See
                 \cite{Petke:2014:EuroGP}.

                 http://www.genetic-programming.org/combined.php
                 HUMIES",
}

Genetic Programming entries for Justyna Petke Mark Harman William B Langdon Westley Weimer

Citations