A comparison of genetic algorithms and genetic programming in solving the school timetabling problem
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- @InProceedings{Raghavjee:2012:NaBIC,
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author = "Rushil Raghavjee and Nelishia Pillay",
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booktitle = "Fourth World Congress on Nature and Biologically
Inspired Computing (NaBIC 2012)",
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title = "A comparison of genetic algorithms and genetic
programming in solving the school timetabling problem",
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year = "2012",
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address = "Mexico City",
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month = "5-9 " # nov,
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pages = "98--103",
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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",
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DOI = "doi:10.1109/NaBIC.2012.6402246",
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size = "6 pages",
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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.",
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notes = "Also known as \cite{6402246}",
- }
Genetic Programming entries for
Rushil Raghavjee
Nelishia Pillay
Citations