Beyond ``Genetic Programming'': Incremental Self-Improvement

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

@InProceedings{schmidhuber:1995:inc,
  author =       "Jurgen Schmidhuber",
  title =        "Beyond {``}Genetic Programming{''}: Incremental
                 Self-Improvement",
  booktitle =    "Proceedings of the Workshop on Genetic Programming:
                 From Theory to Real-World Applications",
  year =         "1995",
  editor =       "Justinian P. Rosca",
  pages =        "42--49",
  address =      "Tahoe City, California, USA",
  month =        "9 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.idsia.ch/%7Ejuergen/",
  URL =          "ftp://ftp.idsia.ch/pub/juergen/gpself.ps.gz",
  URL =          "http://citeseer.ist.psu.edu/227184.html",
  size =         "8 pages",
  abstract =     "Back in 1986, Dickmanns, Winklhofer, and the author
                 used a genetic algorithm to evolve variable-length
                 computer programs [4]. Today, our approach would be
                 classified as {"}Genetic Programming{"} (GP). We
                 applied it to simple tasks, including the {"}lawnmower
                 problem{"} (later also studied by Koza, 1994). In
                 subsequent work (1987 --- 1994), we found GP
                 unsatisfactory for many reasons: (1) GP's way of
                 constructing new code from old code does not improve
                 itself: it always remains limited to the initial
                 crossover and mutation mechanisms. (2) Like almost all
                 other learning paradigms, GP requires concepts that are
                 unrealistic in real world applications, such as
                 {"}resettable environments and exactly repeatable
                 trials{"}. In general, however, realistic environments
                 cannot be reset -- time is one-way, and there is only
                 one single lifelong training sequence. (3) Like almost
                 all other learning paradigms, GP's objective function
                 does not take into account the computation time
                 required for learning itself. To...",
  notes =        "Presents method aiming to encourage reinforcement
                 learning to improve the way it learns

                 part of \cite{rosca:1995:ml}",
}

Genetic Programming entries for Jurgen Schmidhuber

Citations