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:1.2031

@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",
  abstract =     "We present a 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