Neural Programming and an Internal Reinforcement Policy

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{teller:1996:npirpSV,
  author =       "Astro Teller and Manuela Veloso",
  title =        "Neural Programming and an Internal Reinforcement
                 Policy",
  booktitle =    "International Conference Simulated Evolution and
                 Learning",
  year =         "1996",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, ANN",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/AS.ps",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/mosaic/astroseal/astro/seal.html",
  size =         "8 pages",
  abstract =     "An important reason for the continued popularity of
                 Artificial Neural Networks (ANNs) in the machine
                 learning community is that the gradient-descent
                 backpropagation procedure gives ANNs a locally optimal
                 change procedure and, in addition, a framework for
                 understanding the ANN learning performance. Genetic
                 programming (GP) is also a successful evolutionary
                 learning technique that provides powerful parameterized
                 primitive constructs. Unlike ANNs, though, GP does not
                 have such a principled procedure for changing parts of
                 the learned system based on its current performance.
                 This paper introduces Neural Programming, a
                 connectionist representation for evolving programs that
                 maintains the benefits of GP. The connectionist model
                 of Neural Programming allows for a regression
                 credit-blame procedure in an evolutionary learning
                 system. We describe a general method for an informed
                 feedback mechanism for Neural Programming, Internal
                 Reinforcement. We introduce an Internal Reinforcement
                 procedure and demonstrate its use through an
                 illustrative experiment.",
  notes =        "html version available from
                 http://www.cs.cmu.edu/~astro/ SEAL, PADO bucket-brigade
                 IRNP reach given level of performace in 30% of
                 generations taken by NP",
}

Genetic Programming entries for Astro Teller Manuela Veloso

Citations