Evolving More Representative Programs with Genetic Programming

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

@Article{DBLP:journals/ijseke/McGaughranZ09,
  author =       "Daniel McGaughran and Mengjie Zhang",
  title =        "Evolving More Representative Programs with Genetic
                 Programming",
  journal =      "International Journal of Software Engineering and
                 Knowledge Engineering (IJSEKE)",
  year =         "2009",
  volume =       "19",
  number =       "1",
  pages =        "1--22",
  publisher =    "Imperial College Press",
  keywords =     "genetic algorithms, genetic programming, Computer
                 programs; artificial intelligence and knowledge
                 engineering; automatic learning of programs; C++ code",
  ISSN =         "0218-1940",
  DOI =          "doi:10.1142/S021819400900409X",
  abstract =     "This paper describes a new representation of
                 tree-based genetic programs in Genetic Programming, an
                 approach of artificial intelligence and knowledge
                 engineering, in order to adopt a form more conducive to
                 imperative functions as developed by human programmers.
                 This representation incorporates the Abstract Syntax
                 Tree form into a larger tree structure based on a
                 Control Flow Graph, thereby causing statements to be
                 chained together sequentially and allowing genetic
                 programs to be output as (non-object-oriented) C++ code
                 fragments. Maintaining or improving the evolutionary
                 performance has been a key priority in this
                 development. These prompt additional genetic operators
                 to be defined to better preserve chains of statements
                 than the traditional Mutation and Crossover operators,
                 thereby encouraging a more efficient evolution of
                 genetic programs. Experimental results suggest that
                 adopting a chained approach can make a significant
                 improvement in evolutionary performance over using
                 ProgN functions that evaluate their children
                 sequentially. The introduction of additional operators
                 can improve the evolutionary performance even
                 further.

                 This approach can automatically generate computer
                 programs for a particular problem using artificial
                 intelligence and knowledge engineering approaches. In
                 particular, the newly developed operators in the
                 chained approach have great potential for generating
                 human competitive programs in commonly used imperative
                 programming languages such as C++.",
  notes =        "Cited by \ref{Castle:2010:EuroGP}

                 School of Engineering and Computing Sciences, Victoria
                 University of Wellington, P.O. Box 600, Wellington, New
                 Zealand",
}

Genetic Programming entries for Daniel McGaughran Mengjie Zhang

Citations