Experiments in Automatic Programming for General Purposes

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

  author =       "Marek Reformat and Xinwei Chai and James Miller",
  title =        "Experiments in Automatic Programming for General
  booktitle =    "Proceedings of the 15th IEEE International Conference
                 on Tools with Artificial Intelligence (ICTAI '03)",
  publisher =    "IEEE Computer Society",
  year =         "2003",
  pages =        "366--373",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming, automatic
                 programming, fault tolerant computing, genetic
                 algorithms, software prototyping, software reusability
                 algorithm, automatic cone generation, automatic
                 production, automatic programming, clone crossover,
                 clone generation, clone mutation, clone selection,
                 evolutionary-based genetic programming, fault
                 tolerance, genetic programming, problem solving,
                 software clone, software development, software
                 engineering, software fault tolerance",
  DOI =          "doi:10.1109/TAI.2003.1250213",
  abstract =     "Although the generation and application of software
                 clones is relatively unexplored, it is believed that
                 this is a fundamental technology that can have many
                 different applications within a software engineering
                 environment. For example, software clones could be used
                 in software fault tolerance. Clearly, for these clones
                 to be usable, their production needs to be automated.
                 An interesting approach to this automatic production or
                 generation problem is the application of
                 evolutionary-based genetic programming (GP). Using the
                 paradigms of best fit, selection, crossover and
                 mutation a number of clones, satisfying specific
                 requirements, can be automatically generated. In
                 general, GP is a flexible and powerful algorithm
                 suitable for solving variety of different problems. The
                 paper presents the results of studies that have been
                 conducted in order to answer questions related to
                 feasibility of using GP for clone generation: what
                 features of GP are important? What works and what does
                 not? How GP can be {"}tuned{"} for the problem? The
                 results have been used to draw a set of suggestions and
                 conclusions that indicate possible usability of
                 GP-based approach to automatic generation of clones.",
  bibsource =    "http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/repository.html",
  notes =        "Also known as \cite{1250213}",

Genetic Programming entries for Marek Reformat Xinwei Chai James Miller