A Genetic Programming Approach to Geometrical Digital Content Modeling in Web Oriented Applications

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

@Article{Palaghita:2011:IE,
  title =        "A Genetic Programming Approach to Geometrical Digital
                 Content Modeling in Web Oriented Applications",
  author =       "Dragos Palaghita",
  journal =      "Informatica Economica",
  publisher =    "Inforec Association",
  year =         "2011",
  volume =       "15",
  issue =        "1",
  pages =        "31--47",
  keywords =     "genetic algorithms, genetic programming, fitness,
                 geometrical structured entities, analysis",
  ISSN =         "14531305",
  bibsource =    "OAI-PMH server at www.doaj.org",
  language =     "eng",
  oai =          "oai:doaj-articles:4b677e38eb2209cd781614826b99a749",
  source =       "Informatica Economica Journal",
  URL =          "http://revistaie.ase.ro/content/57/03%20-%20Palaghita.pdf",
  URL =          "http://revistaie.ase.ro/57.html",
  broken =       "http://www.doaj.org/doaj?func=openurl&genre=article&issn=14531305&date=2011&volume=15&issue=1&spage=31",
  size =         "17 pages",
  abstract =     "The paper presents the advantages of using genetic
                 techniques in web oriented problems. The specific area
                 of genetic programming applications that paper
                 approaches is content modeling. The analysed digital
                 content is formed through the accumulation of targeted
                 geometrical structured entities that have specific
                 characteristics and behaviour. The accumulated digital
                 content is analysed and specific features are extracted
                 in order to develop an analysis system through the use
                 of genetic programming. An experiment is presented
                 which evolves a model based on specific features of
                 each geometrical structured entity in the digital
                 content base. The results show promising expectations
                 with a low error rate which provides fair
                 approximations related to analyzed geometrical
                 structured entities.",
}

Genetic Programming entries for Dragos Palaghita

Citations