AI SearchA genetic approach to class scheduling
Call Number: AIT Thesis no. ISE-96-19 Material type:
TextSeries: Asian Institute of Technology. Thesis ; no. ISE-96-19Publication details: Bangkok : Asian Institute of Technology, 1996Description: 109 pSubject(s): Online resources: Dissertation note: Thesis (M. Eng.) - Asian Institute of Technology, 1996 Summary: A genetic approach was applied for the solution of the model. The courses were grouped into blocks, where each block represents a characteristic schedule of timeslot. The genetic algorithm created a specific number of solutions and use the best features of these solutions to form the offsprings. The developed genetic algorithm was used for solving the AIT class scheduling problem. An implementation procedure is developed and described for using the genetic algorithm software, and a detailed users manual is prepared for the software. The algorithm is tested for its performance with respect to the population size, the number of iterations, and the convergency. The developed algorithm is found to be very powerful and fast in searching an optimal solution.
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A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Advanced Technologies
Thesis (M. Eng.) - Asian Institute of Technology, 1996
A genetic approach was applied for the solution of the model. The courses were grouped into blocks, where each block represents a characteristic schedule of timeslot. The genetic algorithm created a specific number of solutions and use the best features of these solutions to form the offsprings. The developed genetic algorithm was used for solving the AIT class scheduling problem. An implementation procedure is developed and described for using the genetic algorithm software, and a detailed users manual is prepared for the software. The algorithm is tested for its performance with respect to the population size, the number of iterations, and the convergency. The developed algorithm is found to be very powerful and fast in searching an optimal solution.
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