Discovery of Subroutines in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InCollection{rosca:1996:aigp2,
  author =       "Justinian P. Rosca and Dana H. Ballard",
  title =        "Discovery of Subroutines in Genetic Programming",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "177--202",
  chapter =      "9",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  URL =          "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/96.aigp2.dsgp.ps.gz",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.138.8609",
  URL =          "http://www.cs.uml.edu/~giam/91.510/Papers/RoscaBallard1996.pdf",
  URL =          "http://cisnet.mit.edu/Advances-in-Genetic-Programming/194",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerXPSU:10.1.1.138.8609",
  abstract =     "A fundamental problem in learning from observation and
                 interaction with an environment is defining a good
                 representation, that is a representation which captures
                 the underlying structure and functionality of the
                 domain. This chapter discusses an extension of the
                 genetic programming (GP) paradigm based on the idea
                 that subroutines obtained from blocks of good
                 representations act as building blocks and may enable a
                 faster evolution of even better representations. This
                 GP extension algorithm is called adaptive
                 representation through learning (ARL). It has built-in
                 mechanisms for (1) creation of new subroutines through
                 discovery and generalization of blocks of code; (2)
                 deletion of subroutines. The set of evolved subroutines
                 extracts common knowledge emerging during the
                 evolutionary process and acquires the necessary
                 structure for solving the problem. ARL was successfully
                 tested on the problem of controlling an agent in a
                 dynamic and non-deterministic environment. Results with
                 the automatic discovery of subroutines show the
                 potential to better scale up the GP technique to
                 complex problems.",
  notes =        "

                 The solutions obtained to a complex typical RL problem
                 using a new GP algorithm, Adaptive Representation
                 through Learning, have increased generality and
                 quality.

                 Population entropy used as decision criterion by ARL.",
}

Genetic Programming entries for Justinian Rosca Dana H Ballard