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

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The outline of the basic genetic algorithm is presented in section 1.1.2.
At present, a large number of researches have used the GAs for course
scheduling. The difference of the proposed GAs depends on representing
chromosomes and populations, setting up GAs parameters (population size, crossover
rate, and mutation rate), designing strategies in selection, crossover, and mutation, and
evaluating the fitness function.
The chromosome represents a timetable that is a solution. It can be represented
directly or indirectly. In the former, the timetable is usually a long bit string of
encoding, that stands for when and where each course takes place [33]. Thus, pairs of
selected timetables may be “crossed over” by cutting and splicing the bit strings to
create a new timetable. On the other hand, in the later, the timetable can be
represented by using a data structure such as a multi-dimension array or a linked list.
The indirect representation brings the advantage of processing time and simple GA
operations. However, it needs complex processing to exchange and maintain
constraints between the bit string and real timetable. In contrast, the direct
representation needs more processing time for GA operations, but it is easy to
maintain a large number of constraints for a real timetable. More details of the GAs
will be presented in section 2.3.
2.2.4.4 Hybrid Approaches
The above approaches have been proved that they can create good solutions for
course scheduling problems. However, as above mentioned, they usually need a long
computational time. In order to overcome this problem, many researchers have used
hybrid approaches.
Tuan et al. have successfully combined constraint programming and simulated
annealing for the problem of exam scheduling with real data sets [34]. The proposed
algorithm consists of two phases. A constraint programming phase is to provide an
initial solution. This solution is improved by the simulated annealing phase. Tuan et
al. have applied Kempe chain as neighborhood structure, a special technique for
determining starting temperature T 0 and a mechanism that allows the user to define a
certain period of time in which the algorithm should run. The mentioned mechanism
not only helps to increase the efficiency of the SA algorithm but also makes simulated
annealing experiments easier.