A Genetic Programming System for the Induction of Iterative Solution

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

  author =       "Nelishia Pillay",
  title =        "A Genetic Programming System for the Induction of
                 Iterative Solution",
  booktitle =    "Research for a Changing World: Proceedings of SAICSIT
  year =         "2005",
  editor =       "Judith Bishop and Derrick Kourie",
  pages =        "111--113",
  address =      "White River, Mpumalanga, South Africa",
  month =        "20-22 " # sep,
  organisation = "South African Institute of Computer Scientists and
                 Information Technologist",
  publisher =    "SAICSIT",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-59593-258-5",
  URL =          "http://www.cs.ukzn.ac.za/publications/nelishia_c37.pdf",
  abstract =     "We evaluate genetic programming (GP) as a means of
                 evolving solution algorithms to novice iterative
                 programming problems. This research forms part of a
                 study aimed at reducing the costs associated with
                 developing intelligent programming tutors by inducing
                 solutions to the programming problems presented to
                 students, instead of requiring the lecturer to provide
                 these solutions. The paper proposes a GP system for the
                 induction of algorithms using iteration and nested
                 iteration. The proposed system was tested on 15
                 randomly selected novice procedural programming
                 problems requiring the use of iterative and
                 nested-iterative constructs. The system was able to
                 evolve solutions to eight of these problems. Premature
                 convergence of the GP algorithm as a result of fitness
                 function biases was identified as the cause of the
                 failure of the system to induce solutions to the
                 remaining seven problems. The iterative structure-based
                 algorithm (ISBA) was developed and successfully
                 implemented to escape local optima caused by fitness
                 function biases and evolve solutions to these
  notes =        "http://saicsit.cs.up.ac.za/",

Genetic Programming entries for Nelishia Pillay