A General-Purpose Framework for Genetic Improvement

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Marino:2016:PPSN,
  author =       "Francesco Marino and Giovanni Squillero and 
                 Alberto Tonda",
  title =        "A General-Purpose Framework for Genetic Improvement",
  booktitle =    "14th International Conference on Parallel Problem
                 Solving from Nature",
  year =         "2016",
  editor =       "Julia Handl and Emma Hart and Peter R. Lewis and 
                 Manuel Lopez-Ibanez and Gabriela Ochoa and 
                 Ben Paechter",
  volume =       "9921",
  series =       "LNCS",
  pages =        "345--352",
  address =      "Edinburgh",
  month =        "17-21 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Genetic
                 Improvement, SBSE, Linear genetic programming Software
                 engineering",
  isbn13 =       "978-3-319-45823-6",
  DOI =          "doi:10.1007/978-3-319-45823-6_32",
  size =         "8 pages",
  abstract =     "Genetic Improvement is an evolutionary-based
                 technique. Despite its relatively recent introduction,
                 several successful applications have been already
                 reported in the scientific literature: it has been
                 demonstrated able to modify the code complex programs
                 without modifying their intended behaviour; to increase
                 performance with regards to speed, energy consumption
                 or memory use. Some results suggest that it could be
                 also used to correct bugs, restoring the software's
                 intended functionalities. Given the novelty of the
                 technique, however, instances of Genetic Improvement so
                 far rely upon ad-hoc, language-specific
                 implementations. In this paper, we propose a general
                 framework based on the software engineering's idea of
                 mutation testing coupled with Genetic Programming, that
                 can be easily adapted to different programming
                 languages and objective. In a preliminary evaluation,
                 the framework efficiently optimizes the code of the md5
                 hash function in C, Java, and Python.",
  notes =        "XML, mutation testing, MD5 microGP
                 http://ugp3.sourceforge.net/

                 PPSN2016 http://ppsn2016.org

                 ",
}

Genetic Programming entries for Francesco Marino Giovanni Squillero Alberto Tonda

Citations