Linear Genetic Programming for Multi-class Object Classification

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

@InProceedings{conf/ausai/FogelbergZ05,
  title =        "Linear Genetic Programming for Multi-class Object
                 Classification",
  author =       "Christopher Fogelberg and Mengjie Zhang",
  year =         "2005",
  bibdate =      "2005-11-29",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/ausai/ausai2005.html#FogelbergZ05",
  pages =        "369--379",
  booktitle =    "AI 2005: Advances in Artificial Intelligence, 18th
                 Australian Joint Conference on Artificial Intelligence,
                 Proceedings",
  editor =       "Shichao Zhang and Ray Jarvis",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3809",
  address =      "Sydney, Australia",
  month =        dec # " 5-9",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-30462-2",
  DOI =          "doi:10.1007/11589990_39",
  size =         "11 pages",
  abstract =     "Multi-class object classification is an important
                 field of research in computer vision. In this paper
                 basic linear genetic programming is modified to be more
                 suitable for multi-class classification and its
                 performance is then compared to tree-based genetic
                 programming. The directed acyclic graph nature of
                 linear genetic programming is exploited. The existing
                 fitness function is modified to more accurately
                 approximate the true feature space. The results show
                 that the new linear genetic programming approach
                 outperforms the basic tree-based genetic programming
                 approach on all the tasks investigated here and that
                 the new fitness function leads to better and more
                 consistent results. The genetic programs evolved by the
                 new linear genetic programming system are also more
                 comprehensible than those evolved by the tree-based
                 system.",
}

Genetic Programming entries for Christopher Fogelberg Mengjie Zhang

Citations