Incrementally learning the rules for supervised tasks: the Monk's problems

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

  author =       "Ibrahim Kuscu",
  title =        "Incrementally learning the rules for supervised tasks:
                 the Monk's problems",
  institution =  "School of Cognitive and Computing Sciences, University
                 of Sussex",
  year =         "1995",
  type =         "Cognitive Science Research Paper",
  number =       "396",
  address =      "Falmer, Brighton, Sussex, UK",
  month =        "7 " # dec,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  abstract =     "In previous experiments [4][5] evolution of variable
                 length mathematical expressions containing input
                 variables was found to be useful in finding learning
                 rules for simple and hard supervised tasks. However,
                 hard learning problems required special attention in
                 terms of their need for larger size codings of the
                 potential solutions and their ability of generalisation
                 over the testing set. This paper describes new
                 experiments aiming to find better solutions to these
                 issues. Rather than evolution a hill climbing strategy
                 with an incremental coding of potential solutions is
                 used in discovering learning rules for the three Monks'
                 problems. It is found that with this strategy larger
                 solutions can easily be coded for. Although a better
                 performance is achieved in training for the hard
                 learning problems, the ability of the generalisation
                 over the testing cases is observed to be poor.",
  size =         "14 pages",

Genetic Programming entries for Ibrahim Kuscu