Evolutionary Meta Compilation: Evolving Programs Using Real World Engineering Tools

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Jamie Cullen",
  title =        "Evolutionary Meta Compilation: Evolving Programs Using
                 Real World Engineering Tools",
  booktitle =    "Proceedings of the 8th International Conference
                 Evolvable Systems: From Biology to Hardware, ICES
  year =         "2008",
  editor =       "Gregory Hornby and Luk{\'a}s Sekanina and 
                 Pauline C. Haddow",
  series =       "Lecture Notes in Computer Science",
  volume =       "5216",
  pages =        "414--419",
  address =      "Prague, Czech Republic",
  month =        sep # " 21-24",
  publisher =    "Springer",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  keywords =     "genetic algorithms, genetic programming, grammatical
  isbn13 =       "978-3-540-85856-0",
  DOI =          "doi:10.1007/978-3-540-85857-7_38",
  size =         "6 pages",
  abstract =     "A general purpose system and technique is presented
                 for the separation of target program compilation and
                 fitness evaluation from the primary evolutionary
                 computation system. Preliminary results are presented
                 for two broadly different domains: (1) Software
                 generated in the C programming language, (2) Hardware
                 designs in Verilog, suitable for synthesis. The
                 presented approach frees the developer from
                 implementing and debugging a complex interpreter, and
                 potentially enables the rapid integration of previously
                 unsupported languages, as well as complex methods of
                 fitness evaluation, by leveraging the availability of
                 external tools. It also enables engineers (especially
                 those in industry) to use preferred/approved tools for
                 which source code may not be readily available, or
                 which may be cost or time prohibitive to reimplement.
                 Efficiency gains are also expected, particularly for
                 complex domains where the fitness evaluation is
                 computationally intensive.",
  notes =        "Artificial Intelligence Laboratory, University of New
                 South Wales, Sydney, NSW.

                 Santa Fe ant. Taxi problem (loops). gcc. tiny c (tcc),
                 full adder circuit",

Genetic Programming entries for Jamie Cullen