Journal of Applied Sciences1812-56541812-5662Asian Network for Scientific Information10.3923/jas.2013.2947.2952YunfengDong1220131315The scheduling problem is a typical time table problem in
educational administration. For such a NP complete problems, when the genetic
algorithm solves this problem, it has precociousness phenomenon and quickly
converges not to the global optimal solution but to the local optimal solution.
Therefore, we use the advantage of simulated annealing algorithm to transform
the fitness function and chaotic sequence to control the crossover and mutation
genetic operations and then overcome the weakness of genetic algorithm in the
Time Table Problem. We do a lot of experiments and evaluate the performance
of the improved genetic algorithm. The experiment results show that improved
genetic algorithm is a more superior algorithm to apply to the TTP problem.]]>De Almeida, E.S., A. Alvaro, V.C. Garcia, J.C.C.P. Mascena and V.A. de Arruda Buregio,2007Dimopoulou, M. and P. Miliotis,2004Charlga, S.C. and Y.C. Chung,2005Sorin, D.J.,2009Van Heiningen, W., T. Brecht and S. MacDonald,2006