Evolving Construction Heuristics for the Curriculum Based University Course Timetabling Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Pillay:2016:CEC,
  author =       "Nelishia Pillay",
  title =        "Evolving Construction Heuristics for the Curriculum
                 Based University Course Timetabling Problem",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "4437--4443",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, automatic
                 heuristic derivation, university course timetabling,
                 hyper-heuristics",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7744354",
  abstract =     "In solving combinatorial optimization problems
                 construction heuristics are generally used to create an
                 initial solution which is improved using optimization
                 techniques like genetic algorithms. These construction
                 heuristics are usually derived by humans and this is
                 usually quite a time consuming task. Furthermore,
                 according to the no free lunch theorem different
                 heuristics are effective for different problem
                 instances. Ideally we would like to derive construction
                 heuristics for different problem instances or classes
                 of problems. However, due to the time it takes to
                 manually derive construction heuristics it is generally
                 not feasible to induce problem instance specific
                 heuristics. The research presented in the paper forms
                 part of the initiative aimed at automating the
                 derivation of construction heuristics. Genetic
                 programming is used to evolve construction heuristics
                 for the curriculum based university course timetabling
                 (CB-CTT) problem. Each heuristic is a hierarchical
                 combination of problem characteristics and a period
                 selection heuristic. The paper firstly presents and
                 analyses the performance of known construction
                 heuristics for CB-CTT. The analysis has shown that
                 different heuristics are effective for different
                 problem instances. The paper then presents the genetic
                 programming approach for the automated induction of
                 construction heuristics for the CB-CTT problem and
                 evaluates the approach on the ITC 2007 problem
                 instances for the second international timetabling
                 competition. The evolved heuristics performed better
                 than the known construction heuristics, producing
                 timetables with lower soft constraint costs.",
  notes =        "WCCI2016",
}

Genetic Programming entries for Nelishia Pillay

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