AI SearchA genetic approach to class scheduling
Le Minh Tuan
A genetic approach to class scheduling - Bangkok : Asian Institute of Technology, 1996 - 109 p. - Thesis ; no. ISE-96-19 . - Asian Institute of Technology. Thesis ; no. ISE-96-19 .
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.
Genetic algorithms
Scheduling (Management)
A genetic approach to class scheduling - Bangkok : Asian Institute of Technology, 1996 - 109 p. - Thesis ; no. ISE-96-19 . - Asian Institute of Technology. Thesis ; no. ISE-96-19 .
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.
Genetic algorithms
Scheduling (Management)