A comparison of genetic algorithms and genetic programming in solving the school timetabling problem

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Raghavjee:2012:NaBIC,
  author =       "Rushil Raghavjee and Nelishia Pillay",
  booktitle =    "Fourth World Congress on Nature and Biologically
                 Inspired Computing (NaBIC 2012)",
  title =        "A comparison of genetic algorithms and genetic
                 programming in solving the school timetabling problem",
  year =         "2012",
  address =      "Mexico City",
  month =        "5-9 " # nov,
  pages =        "98--103",
  keywords =     "genetic algorithms, genetic programming, educational
                 institutions, mathematical operators, Abramson set, GA,
                 GP, optimal program, optimal timetable, school
                 timetabling problem, solution space, timetable
                 construction, Educational institutions, Sociology,
                 Space exploration, Statistics, school timetabling
                 problem",
  DOI =          "doi:10.1109/NaBIC.2012.6402246",
  size =         "6 pages",
  abstract =     "In this paper we compare the performance of genetic
                 algorithms and genetic programming in solving a set of
                 hard school timetabling problems. Genetic algorithms
                 search a solution space whereas genetic programming
                 explores a program space. While previous work has
                 examined the use of genetic algorithms in solving the
                 school timetabling problem, there has not been any
                 research on the use of genetic programming for this
                 domain. The GA explores a space of timetables to find
                 an optimal timetable. GP on the other hand searches for
                 an optimal program which when executed will produce a
                 solution. Each program is comprised of operators for
                 timetable construction. The GA and GP were tested on
                 the Abramson set of school timetabling problems.
                 Genetic programming proved to be more effective than
                 genetic algorithms in solving this set of problems.
                 Furthermore, the results produced by both the GA and GP
                 were found to be comparative to methods applied to the
                 same set of problems.",
  notes =        "Also known as \cite{6402246}",
}

Genetic Programming entries for Rushil Raghavjee Nelishia Pillay

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