a multi-objective bisexual reproduction genetic algorithm for ...

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TABLE 4-2 (CONTINUED)
Course Section Lecturer Room Group
MAT223 002 00061 MATLECRM
MAT322
MAT322
001
002
00063
00063
MATLECRM
MATLECRM
CHE103
CHE103
005
006
00071
00071
CHELECRM
CHELECRM
CHE104 005 00072 CHEFTCLB
CHE104 006 00073 CHEFTCLB
Similarly, constraints about classroom size and lecturer’s time are also prepared.
4.2 The Experiments and Discussions
4.2.1 Experimental Designs
The aims of the experiments are to evaluate the influence of setting the GA
parameters and the influence of the grid computing environment.
The proposed GA that is presented in chapter 3 is applied to both the centralized
course scheduling program and decentralized course scheduling program. In addition,
the same values of the GA parameters will be applied to these programs. Thus, to
evaluate the efficiency of the GA, we only need to test one of the above course
scheduling programs. Here, we test the centralized course scheduling program. To
evaluate the influence of the grid computing environment, we use the grid system as
shown in section 3.7.
We will do four separate experiments. The first experiment tests the influence of
weighting for hard and soft constraints in the fitness function. The second and third
experiments test the influence of the mutation rate and the population size on the
speed of evolution respectively. Finally, the forth experiment tests the influence of
using the grid computing environment.
The course scheduling is a NP hard problem, and the GA itself is a metaheuristic
algorithm. Therefore, we would obtain a good enough solution if not the best
one. Each experiment will run models until the GA detects the best solution or until
the GA cannot improve the fitness value in 300 consecutive generations. The model
giving a faster fitness value via many runs would be a better one.