Generative Learning of Visual Concepts using Multiobjective Genetic Programming

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@Article{Krawiec07PRL,
  author =       "Krzysztof Krawiec",
  title =        "Generative Learning of Visual Concepts using
                 Multiobjective Genetic Programming",
  journal =      "Pattern Recognition Letters",
  year =         "2007",
  volume =       "28",
  number =       "16",
  pages =        "2385--2400",
  month =        "1 " # dec,
  email =        "krawiec@cs.put.poznan.pl",
  keywords =     "genetic algorithms, genetic programming, Visual
                 learning, Generative pattern recognition, Evolutionary
                 synthesis of pattern recognition systems",
  DOI =          "doi:10.1016/j.patrec.2007.08.001",
  abstract =     "This paper introduces a novel method of visual
                 learning based on Genetic Programming, which evolves a
                 population of individuals (image analysis programs)
                 that process attributed visual primitives derived from
                 raw raster images. The goal is to evolve an image
                 analysis program that correctly recognises the training
                 concept (shape). The approach uses generative
                 evaluation scheme: individuals are rewarded for
                 re-producing the shape of the object being recognised
                 using graphical primitives and elementary background
                 knowledge encoded in predefined operators. Evolutionary
                 run is driven by a multiobjective fitness function to
                 prevent premature convergence and enable effective
                 exploration of the space of solutions. We present the
                 method in detail and verify it experimentally on the
                 task of learning two visual concepts from examples.",
}

Genetic Programming entries for Krzysztof Krawiec

Citations