Multi-Objective Improvement of Software using Co-evolution and Smart Seeding

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Andrea Arcuri and David Robert White and 
                 John Clark and Xin Yao",
  title =        "Multi-Objective Improvement of Software using
                 Co-evolution and Smart Seeding",
  booktitle =    "Proceedings of the 7th International Conference on
                 Simulated Evolution And Learning (SEAL '08)",
  year =         "2008",
  editor =       "Xiaodong Li and Michael Kirley and Mengjie Zhang and 
                 David G. Green and Victor Ciesielski and 
                 Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and 
                 Kalyanmoy Deb and Kay Chen Tan and 
                 J{\"u}rgen Branke and Yuhui Shi",
  volume =       "5361",
  series =       "Lecture Notes in Computer Science",
  pages =        "61--70",
  address =      "Melbourne, Australia",
  month =        dec # " 7-10",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, SBSE",
  bibsource =    "",
  isbn13 =       "978-3-540-89693-7",
  DOI =          "doi:10.1007/978-3-540-89694-4_7",
  size =         "10 pages",
  abstract =     "Optimising non-functional properties of software is an
                 important part of the implementation process. One such
                 property is execution time, and compilers target a
                 reduction in execution time using a variety of
                 optimisation techniques. Compiler optimisation is not
                 always able to produce semantically equivalent
                 alternatives that improve execution times, even if such
                 alternatives are known to exist. Often, this is due to
                 the local nature of such optimisations. In this paper
                 we present a novel framework for optimising existing
                 software using a hybrid of evolutionary optimisation
                 techniques. Given as input the implementation of a
                 program or function, we use Genetic Programming to
                 evolve a new semantically equivalent version, optimised
                 to reduce execution time subject to a given probability
                 distribution of inputs. We employ a co-evolved
                 population of test cases to encourage the preservation
                 of the program's semantics, and exploit the original
                 program through seeding of the population in order to
                 focus the search. We carry out experiments to identify
                 the important factors in maximising efficiency gains.
                 Although in this work we have optimised execution time,
                 other non-functional criteria could be optimised in a
                 similar manner.",
  notes =        "Also known as \cite{DBLP:conf/seal/ArcuriWCY08}",
  bibsource =    "DBLP,",

Genetic Programming entries for Andrea Arcuri David Robert White John A Clark Xin Yao