An Informed Genetic Algorithm for University Course and Student Timetabling Problems

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6114)


This paper describes an Informed Genetic Algorithm (IGA), a genetic algorithm using greedy initialization and directed mutation, to solve a practical university course and student timetabling problem. A greedy method creates some feasible solutions, where all specified hard constraints are not broken, as initial population. A directed mutation scheme is used to reduce violations regarding all given soft constraints and to keep the solutions feasible. Here, IGA creates a timetable in two stages. Firstly, IGA evolves a course timetable using any constraints regarding lecturer, class and room. This stage produce best-so-far timetable. Secondly, using some certain rules IGA evolves the best-so-far timetable using all constraints. The batch student sectioning is done by allowing the first stage timetable to change. Computer simulation to a highly constrained timetabling problem shows that the informed GA is capable of producing a reliable timetable.


Time Slot Soft Constraint Hard Constraint Timetabling Problem Directed Mutation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Suyanto
    • 1
  1. 1.Faculty of Informatics - IT TelkomWest JavaIndonesia

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