An Investigation into the Use of Genetic Programming for the Induction of Novice Procedural Programming Solution Algorithms in Intelligent Programming Tutors

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@PhdThesis{pillay:thesis,
  author =       "Nelishia Pillay",
  title =        "An Investigation into the Use of Genetic Programming
                 for the Induction of Novice Procedural Programming
                 Solution Algorithms in Intelligent Programming Tutors",
  school =       "School of Geological and Computer Sciences, University
                 of KwaZulu-Natal",
  year =         "2004",
  address =      "Durban South Africa",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, STGP,
                 memory",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/pillay_thesis.pdf",
  size =         "429 pages",
  abstract =     "Intelligent programming tutors have proven to be an
                 economically viable and effective means of assisting
                 novice programmers overcome learning difficulties.
                 However, the large-scale use of intelligent programming
                 tutors has been impeded by the high developmental costs
                 associated with building intelligent programming
                 tutors. The research presented in this thesis forms
                 part of a larger initiative aimed at reducing these
                 costs by building a generic architecture for the
                 development of intelligent programming tutors. One of
                 the facilities that must be provided by the generic
                 architecture is the automatic generation of solutions
                 to programming problems. The study presented in the
                 thesis examines the use of genetic programming as means
                 of inducing solution algorithms to novice programming
                 problems. The scope of the thesis is limited to novice
                 procedural programming paradigm problems requiring the
                 use of arithmetic, string manipulation, conditional,
                 iterative and recursive programming structures.

                 The methodology employed in the study is
                 proof-by-demonstration. A genetic programming system
                 for the induction of novice procedural solution
                 algorithms was implemented and tested on randomly
                 chosen novice procedural programming problems. The
                 study has identified the standard and advanced genetic
                 programming features needed for the successful
                 generation of novice procedural solution algorithms.
                 The outcomes of this study include the derivation of an
                 internal representation language for representing
                 procedural solution algorithms and a high-level
                 programming problem specification format for describing
                 procedural problems, in the generic architecture. One
                 of the limitations of genetic programming is its
                 susceptibility to converge prematurely to local optima
                 and not find a solution in some cases. The study has
                 identified fitness function biases against certain
                 structural components that are needed to find a
                 solution, as an additional cause of premature
                 convergence in this domain. It presents an iterative
                 structure-based algorithm as a solution to this
                 problem.

                 This thesis has contributed to both the fields of
                 genetic programming and intelligent programming tutors.
                 While genetic programming has been successfully
                 implemented in various domains, it is usually applied
                 to a single problem within that domain. In this study
                 the genetic programming system must be capable of
                 solving a number of different programming problems in
                 different application domains. In addition to this, the
                 study has also identified a means of overcoming
                 premature convergence caused by fitness function biases
                 in a genetic programming system for the induction of
                 novice procedural programming algorithms. Furthermore,
                 although a number of studies have addressed the student
                 modelling and pedagogical aspects of intelligent
                 programming tutors, none have examined the automatic
                 generation of problem solutions as a means of reducing
                 developmental costs. Finally, this study has
                 contributed to the ongoing research being conducted by
                 the artificial intelligence in education community, to
                 test the effectiveness of using machine learning
                 techniques in the development of different aspects of
                 intelligent tutoring systems.",
  notes =        "I/O, named memory, indexed memory, recursion, ISBA,
                 ASCII graphics problem.

                 All work for this thesis was completed at the former
                 University of Natal

                 Supervisor:Professor Alan Sartori-Angus",
}

Genetic Programming entries for Nelishia Pillay

Citations