Extending Genetic Programming to Evolve Perceptron-Like Learning Programs

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  title =        "Extending Genetic Programming to Evolve
                 Perceptron-Like Learning Programs",
  author =       "Marcin Suchorzewski",
  bibdate =      "2010-06-22",
  bibsource =    "DBLP,
  booktitle =    "Artifical Intelligence and Soft Computing, 10th
                 International Conference, {ICAISC} 2010, Zakopane,
                 Poland, June 13-17, 2010, Part {II}",
  publisher =    "Springer",
  year =         "2010",
  volume =       "6114",
  editor =       "Leszek Rutkowski and Rafal Scherer and 
                 Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek M. Zurada",
  isbn13 =       "978-3-642-13231-5",
  pages =        "221--228",
  series =       "Lecture Notes in Computer Science",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://dx.doi.org/10.1007/978-3-642-13232-2",
  DOI =          "doi:10.1007/978-3-642-13232-2_27",
  abstract =     "We extend genetic programming (GP) with a local memory
                 and vectorisation to evolve simple, perceptron-like
                 programs capable of learning by error correction. The
                 local memory allows for a scalar value or vector to be
                 stored and manipulated within a local scope of GP tree.
                 Vectorization consists in grouping input variables and
                 processing them as vectors. We demonstrate these
                 extensions, along with an island model, allow to evolve
                 general perceptron-like programs, i.e. working for any
                 number of inputs. This is unlike in standard GP, where
                 inputs are represented explicitly as scalars, so that
                 scaling up the problem would require to evolve a new
                 solution. Moreover, we find vectorisation allows to
                 represent programs more compactly and facilitates the
                 evolutionary search.",

Genetic Programming entries for Marcin Suchorzewski