Learning by adapting representations in genetic programming

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

  author =       "J. P. Rosca and D. H. Ballard",
  title =        "Learning by adapting representations in genetic
  year =         "1994",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  volume =       "1",
  pages =        "407--412",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, evolution
                 trace, external environment interaction, genetic search
                 traces, internal problem-solving trace analysis,
                 knowledge acquisition, knowledge representation
                 adaptation, machine learning, search space
                 restructuring, adaptive systems, knowledge acquisition,
                 knowledge representation, learning (artificial
                 intelligence), problem solving, programming, search
  URL =          "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/94.ieee.adaptive_repr.ps.gz",
  DOI =          "doi:10.1109/ICEC.1994.349916",
  size =         "6 pages",
  abstract =     "Machine learning aims towards the acquisition of
                 knowledge based on either experience from the
                 interaction with the external environment or by
                 analysing the internal problem-solving traces. Genetic
                 Programming (GP) has been effective in learning via
                 interaction but so far there have not been any
                 significant tests to show that GP can take advantage of
                 its own search traces. This paper demonstrates how an
                 analysis of the evolution trace enables the genetic
                 search to discover useful genetic material and to use
                 it in order to accelerate the search process. The key
                 idea is that of genetic material discovery which
                 enables a restructuring of the search space so that
                 solutions can be much more easily found.",
  notes =        "See \cite{Rosca94} for more details",

Genetic Programming entries for Justinian Rosca Dana H Ballard