Evolutionary meta programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Jamie Cullen",
  title =        "Evolutionary meta programming",
  booktitle =    "GEC '09: Proceedings of the first ACM/SIGEVO Summit on
                 Genetic and Evolutionary Computation",
  year =         "2009",
  editor =       "Lihong Xu and Erik D. Goodman and Guoliang Chen and 
                 Darrell Whitley and Yongsheng Ding",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  pages =        "81--88",
  address =      "Shanghai, China",
  organisation = "SigEvo",
  DOI =          "doi:10.1145/1543834.1543847",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        jun # " 12-14",
  isbn13 =       "978-1-60558-326-6",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "The Evolutionary Meta Programming (EMP) approach
                 towards the evolution of computer programs is
                 presented. An EMP system is divided into two
                 interacting parts: The Host Environment, and the Target
                 Environment. Programs are evolved in an arbitrary
                 target language by the Host Environment and are
                 injected into the Target Environment, where they are
                 evaluated for fitness in their `natural surroundings'.
                 Early results from three significantly different
                 domains are discussed: (1) Compiling C programs using a
                 well-known compiler (GNU C compiler) (2) Circuit
                 synthesis of digital hardware in an industry standard
                 Hardware Description Language (Verilog), and (3)
                 Functional Programming in an external Common LISP
                 interpreter. The presented approach has now been used
                 to evolve solutions to some well-known problems in the
                 field of Evolutionary Computation, as well as enabling
                 the initial examination of some novel problem domains
                 that are typically not amenable to exploration by
                 common techniques. Possible strengths of this approach,
                 when compared to techniques such as Genetic
                 Programming, include more rapid and natural problem
                 specification and testbench development for some types
                 of problems, reduced software development time, and the
                 potential to more readily examine problems that require
                 complex methods of fitness evaluation.",
  notes =        "Also known as \cite{DBLP:conf/gecco/Cullen09a} part of

Genetic Programming entries for Jamie Cullen