The Effectiveness of Cost Based Subtree Caching Mechanisms in Typed Genetic Programming for Image Segmentation

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

@InProceedings{Roberts:evowks03,
  author =       "Mark E. Roberts",
  title =        "The Effectiveness of Cost Based Subtree Caching
                 Mechanisms in Typed Genetic Programming for Image
                 Segmentation",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoWorkshops2003: Evo{BIO}, Evo{COP}, Evo{IASP},
                 Evo{MUSART}, Evo{ROB}, Evo{STIM}",
  year =         "2003",
  editor =       "G{\"u}nther R. Raidl and Stefano Cagnoni and 
                 Juan Jes\'us Romero Cardalda and David W. Corne and 
                 Jens Gottlieb and Agn\`es Guillot and Emma Hart and 
                 Colin G. Johnson and Elena Marchiori and Jean-Arcady Meyer and 
                 Martin Middendorf",
  volume =       "2611",
  series =       "LNCS",
  pages =        "444--454",
  address =      "University of Essex, UK",
  publisher_address = "Berlin",
  month =        "14-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation, applications",
  isbn13 =       "978-3-540-00976-4",
  URL =          "http://www.cs.bham.ac.uk/~mer/papers/evoiasp-2003.pdf",
  DOI =          "doi:10.1007/3-540-36605-9_41",
  abstract =     "Genetic programming (GP) has long been known as a
                 computationally expensive optimisation technique. When
                 evolving imaging operations, the processing time
                 increases dramatically. This work describes a system
                 using a caching mechanism which reduces the number of
                 evaluations needed by up to 66 percent, counteracting
                 the effects of increasing tree size. This results in a
                 decrease in elapsed time of up to 52 percent. A cost
                 threshold is introduced which can guarantee a speed
                 increase. This caching technique allows GP to be
                 feasibly applied to problems in computer vision and
                 image processing. The trade-offs involved in caching
                 are analysed, and the use of the technique on a
                 previously time consuming medical segmentation problem
                 is shown.",
  notes =        "EvoWorkshops2003",
}

Genetic Programming entries for Mark E Roberts

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