Automated Aesthetic Selection of Evolutionary Art by Distance Based Classification of Genomes and Phenomes using the Universal Similarity Metric

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

@InProceedings{svangard:evows04,
  author =       "Nils Svangard and Peter Nordin",
  title =        "Automated Aesthetic Selection of Evolutionary Art by
                 Distance Based Classification of Genomes and Phenomes
                 using the Universal Similarity Metric",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoWorkshops2004: {EvoBIO}, {EvoCOMNET}, {EvoHOT},
                 {EvoIASP}, {EvoMUSART}, {EvoSTOC}",
  year =         "2004",
  month =        "5-7 " # apr,
  editor =       "Guenther R. Raidl and Stefano Cagnoni and 
                 Jurgen Branke and David W. Corne and Rolf Drechsler and 
                 Yaochu Jin and Colin R. Johnson and Penousal Machado and 
                 Elena Marchiori and Franz Rothlauf and George D. Smith and 
                 Giovanni Squillero",
  series =       "LNCS",
  volume =       "3005",
  address =      "Coimbra, Portugal",
  publisher =    "Springer Verlag",
  publisher_address = "Berlin",
  pages =        "447--456",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation",
  ISBN =         "3-540-21378-3",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3005&spage=447",
  DOI =          "doi:10.1007/978-3-540-24653-4_46",
  abstract =     "In this paper we present a new technique for
                 automatically approximating the aesthetic fitness of
                 evolutionary art. Instead of assigning fitness values
                 to images interactively, we use the Universal
                 Similarity Metric to predict how interesting new images
                 are to the observer based on a library of aesthetic
                 images. In order to approximate the Information
                 Distance, and find the images most similar to the
                 training set, we use a combination of zip-compression,
                 for genomes, and jpeg-compression of the final images.
                 We evaluated the prediction accuracy of our system by
                 letting the user label a new set of images and then
                 compare that to what our system classifies as the most
                 aesthetically pleasing images. Our experiments indicate
                 that the Universal Similarity Metric can successfully
                 be used to classify what images and genomes are
                 aesthetically pleasing, and that it can clearly
                 distinguish between 'ugly' and 'pretty' images with an
                 accuracy better than the random baseline.",
  notes =        "EvoWorkshops2004",
}

Genetic Programming entries for Nils Svangard Peter Nordin

Citations