Genetic Algorithms and Programming-An Evolutionary Methodology

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

@Misc{Venkat:2010:IJHIT,
  title =        "Genetic Algorithms and Programming-An Evolutionary
                 Methodology",
  author =       "T. Venkat Narayana Rao and Srikanth Madiraju",
  journal =      "International Journal of Hybrid Information
                 Technology",
  year =         "2010",
  volume =       "3",
  number =       "4",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, subtree,
                 chromosomes, mutation",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.303.8499",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.303.8499",
  URL =          "http://www.sersc.org/journals/IJHIT/vol3_no4_2010/1.pdf",
  abstract =     "Genetic programming (GP) is an automated method for
                 creating a working computer program from a high-level
                 problem statement of a problem. Genetic programming
                 starts from a high-level statement of what needs to be
                 done and automatically creates a computer program to
                 solve the problem. In artificial intelligence, genetic
                 programming (GP) is an evolutionary algorithm-based
                 methodology inspired by biological evolution to find
                 computer programs that perform a user defined task. It
                 is a specialisation of genetic algorithms (GA) where
                 each individual is a computer program. It is a machine
                 learning technique used to optimise a population of
                 computer programs according to a fitness span
                 determined by a program{'}s ability to perform a given
                 computational task. This paper presents a idea of the
                 various principles of genetic programming which
                 includes, relative effectiveness of mutation,
                 crossover, breeding computer programs and fitness test
                 in genetic programming. The literature of traditional
                 genetic algorithms contains related studies, but
                 through GP, it saves time by freeing the human from
                 having to design complex algorithms. Not only designing
                 the algorithms but creating ones that give optimal
                 solutions than traditional counterparts in noteworthy
                 ways.",
}

Genetic Programming entries for T Venkat Narayana Rao Srikanth Madiraju

Citations