Generating a novel sort algorithm using Reinforcement Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Spencer K. White and Tony Martinez and 
                 George Rudolph",
  title =        "Generating a novel sort algorithm using Reinforcement
  booktitle =    "IEEE Congress on Evolutionary Computation (CEC 2010)",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-6910-9",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1109/CEC.2010.5586457",
  abstract =     "Reinforcement Programming (RP) is a new approach to
                 automatically generating algorithms, that uses
                 reinforcement learning techniques. This paper describes
                 the RP approach and gives results of experiments using
                 RP to generate a generalised, in-place, iterative sort
                 algorithm. The RP approach improves on earlier results
                 that 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.
                 Results establish interesting empirical bounds on
                 learning the sort algorithm: A list of size 4 is
                 sufficient to learn the generalized sort algorithm. The
                 training set only requires one element and learning
                 took less than 200,000 iterations. RP has also been
                 used to generate three binary addition algorithms: a
                 full adder, a binary incrementer, and a binary adder.",
  notes =        "WCCI 2010. Also known as \cite{5586457}",

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