Multitask Visual Learning Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Wojciech Jaskowski and Krzysztof Krawiec and 
                 Bartosz Wieloch",
  title =        "Multitask Visual Learning Using Genetic Programming",
  journal =      "Evolutionary Computation",
  year =         "2008",
  volume =       "16",
  number =       "4",
  pages =        "439--459",
  month =        "Winter",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1063-6560",
  DOI =          "doi:10.1162/evco.2008.16.4.439",
  abstract =     "We propose a multi-task learning method of visual
                 concepts within the genetic programming (GP) framework.
                 Each GP individual is composed of several trees that
                 process visual primitives derived from input images.
                 Two trees solve two different visual tasks and are
                 allowed to share knowledge with each other by commonly
                 calling the remaining GP trees (sub functions) included
                 in the same individual. The performance of a particular
                 tree is measured by its ability to reproduce the shapes
                 contained in the training images. We apply this method
                 to visual learning tasks of recognizing simple shapes
                 and compare it to a reference method. The experimental
                 verification demonstrates that such multitask learning
                 often leads to performance improvements in one or both
                 solved tasks, without extra computational effort.",
  notes =        "Part of special issue on Evolutionary Computer Vision

Genetic Programming entries for Wojciech Jaskowski Krzysztof Krawiec Bartosz Wieloch