• DocumentCode
    2224355
  • Title

    Method of Inequality-Based Multi-Objective Genetic Algorithm for Course Scheduling Model

  • Author

    Liu Chu-ling ; Peng Ping ; Xie Zan-fu ; Chen Chao-tian

  • Author_Institution
    Coll. of Comput. Sci., Guangdong Polytech. Normal Univ., Guangzhou, China
  • fYear
    2009
  • fDate
    26-28 Dec. 2009
  • Firstpage
    270
  • Lastpage
    273
  • Abstract
    The constraints, goals and difficulties in the course scheduling problem are discussed in this paper, and the course scheduling model based on method of inequality-based multi-objective genetic algorithm (MMGA) is proposed. The auxiliary performance index vector is introduced into the original multi-objective optimization problem, and a new method that guarantees the search in the "region of interest" through inequality transformation. The new method which makes up for the deficiencies in course scheduling with traditional genetic algorithm is a more practical form of algorithm, which describes the course scheduling problem much closer to the reality.
  • Keywords
    educational courses; genetic algorithms; scheduling; auxiliary performance index vector; course scheduling model; course scheduling problem; inequality transformation; inequality-based multiobjective genetic algorithm; Computer science; Education; Educational institutions; Genetic algorithms; Genetic engineering; Information science; Optimization methods; Performance analysis; Processor scheduling; Scheduling algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Science and Engineering (ICISE), 2009 1st International Conference on
  • Conference_Location
    Nanjing
  • Print_ISBN
    978-1-4244-4909-5
  • Type

    conf

  • DOI
    10.1109/ICISE.2009.738
  • Filename
    5455188