A Genetic Programming Approach to the Generation of Hyper-Heuristics for the Uncapacitated Examination Timetabling Problem

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@InProceedings{conf/epia/PillayB07,
  author =       "Nelishia Pillay and Wolfgang Banzhaf",
  title =        "A Genetic Programming Approach to the Generation of
                 Hyper-Heuristics for the Uncapacitated Examination
                 Timetabling Problem",
  booktitle =    "13th Portuguese Conference on Aritficial Intelligence,
                 EPIA 2007",
  year =         "2007",
  editor =       "Jos{\'e} Neves and Manuel Filipe Santos and 
                 Jos{\'e} Machado",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  pages =        "223--234",
  address =      "Guimar{\~a}es, Portugal",
  month =        dec # " 3-7",
  keywords =     "genetic algorithms, genetic programming,
                 hyper-heuristics, examination timetabling",
  isbn13 =       "978-3-540-77000-8",
  URL =          "http://www.cs.mun.ca/~banzhaf/papers/epia.pdf",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.2636",
  DOI =          "doi:10.1007/978-3-540-77002-2_19",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  abstract =     "Research in the field of examination timetabling has
                 developed in two directions. The first looks at
                 applying various methodologies to induce examination
                 timetables. The second takes an indirect approach to
                 the problem and examines the generation of heuristics
                 or combinations of heuristics, i.e. hyper-heuristics,
                 to be used in the construction of examination
                 timetables. The study presented in this paper focuses
                 on the latter area. This paper presents a first attempt
                 at using genetic programming for the evolution of
                 hyper-heuristics for the uncapacitated examination
                 timetabling problem. The system has been tested on 9
                 benchmark examination timetabling problems. Clash-free
                 timetables were found for all 9 nine problems.
                 Furthermore, the performance of the genetic programming
                 system is comparable to, and in a number of cases has
                 produced better quality timetables, than other search
                 algorithms used to evolve hyper-heuristics for this set
                 of problems.{"}, bibsource = {"}OAI-PMH server at
                 citeseerx.ist.psu.edu",
  notes =        "Workshops: GAIW, AIASTS, ALEA, AMITA, BAOSW, BI,
                 CMBSB, IROBOT, MASTA, STCS, and TEMA",
}

Genetic Programming entries for Nelishia Pillay Wolfgang Banzhaf

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