Automatic Algorithm Development Using New Reinforcement Programming Techniques

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Spencer K. White and Tony R. Martinez and 
                 George L. Rudolph",
  title =        "Automatic Algorithm Development Using New
                 Reinforcement Programming Techniques",
  journal =      "Computational Intelligence",
  year =         "2012",
  volume =       "28",
  number =       "2",
  pages =        "176--208",
  keywords =     "genetic algorithms, genetic programming",
  timestamp =    "Wed, 06 Jun 2012 17:36:13 +0200",
  biburl =       "",
  bibsource =    "dblp computer science bibliography,",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1111/j.1467-8640.2012.00413.x",
  abstract =     "Reinforcement Programming (RP) is a new approach to
                 automatically generating algorithms that uses
                 reinforcement learning techniques. This paper
                 introduces the RP approach and demonstrates its use to
                 generate a generalised, in-place, iterative sort
                 algorithm. The RP approach improves on earlier results
                 that use genetic programming (GP). The resulting
                 algorithm is a novel algorithm that is more efficient
                 than comparable sorting routines. RP learns the sort in
                 fewer iterations than GP and with fewer resources.
                 Experiments establish interesting empirical bounds on
                 learning the sort algorithm: A list of size 4 is
                 sufficient to learn the generalised sort algorithm. The
                 training set only requires one element and learning
                 took less than 200,000 iterations. Additionally RP was
                 used to generate three binary addition algorithms: a
                 full adder, a binary incrementer, and a binary adder.",

Genetic Programming entries for Spencer K White Tony R Martinez George Rudolph