• DocumentCode
    2849660
  • Title

    Assignment of Students to Preferred Laboratory Groups Using a Hybrid Grouping Genetic Algorithm

  • Author

    Agustin-Blas, L.E. ; Salcedo-Sanz, Sancho ; Ortiz-Garcia, E. ; Perez-Bellido, A. ; Portilla-Figueras, A.

  • Author_Institution
    Dept. of Signal Theor. & Commun., Univ. de Alcala, Madrid
  • fYear
    2008
  • fDate
    10-12 Sept. 2008
  • Firstpage
    48
  • Lastpage
    52
  • Abstract
    In this paper we present an application of the grouping genetic algorithm to the problem of assigning students to laboratory groups in university courses. This problem includes an important constraint of capacity, due to laboratories usually have a maximum number of equips or computers available, so the number of total students in a group is constrained to be equal or less than the capacity of the laboratory. In addition, our approach considers the case in which the students provide a sorted list of preferred laboratory groups, so the objective of the assignment must take this point into account. Another case in which lecturers´ preferences are considered is also treated. The performance of the approach is shown in several test instances of the problem and compared with the results of an existing heuristic algorithm.
  • Keywords
    computer aided instruction; genetic algorithms; problem solving; student experiments; heuristic algorithm; hybrid grouping genetic algorithm; preferred laboratory groups; students assignment; university courses; Algorithm design and analysis; Application software; Educational institutions; Genetic algorithms; Heuristic algorithms; Hybrid intelligent systems; Laboratories; Performance evaluation; Testing; grouping genetic algorithm; hybrid algorithms; laboratory groups; preferences; students assignment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Hybrid Intelligent Systems, 2008. HIS '08. Eighth International Conference on
  • Conference_Location
    Barcelona
  • Print_ISBN
    978-0-7695-3326-1
  • Electronic_ISBN
    978-0-7695-3326-1
  • Type

    conf

  • DOI
    10.1109/HIS.2008.37
  • Filename
    4626604