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

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should revise the chromosome representation, presented in section 3.6.1. Each
chromosome represents directly a timetable or a solution, so it stores a large amount
of data. It also has a large amount of related data from the database. As a result, the
larger population needs more memory and more processing time for GA operations.
This experiment also shows that with the smallest population size (five) we
have the fastest GA.
The GAs with a large population do not give a faster speed of evolution.
However, in order to have diversity of solutions, it may be safe to keep the population
size larger than an optimum size although it is a little slower to execute. We will use
the population of 10 for our GA.
4.2.4 Experiment 3: Mutation Rate Test
The aim of this experiment is to analyze the behavior of the GA as mutation rate
is modified.
To perform this experiment, the centralized course scheduling program will be
run on one Pentium IV 1.7 GHz machine with the following GA settings:
- Population size : 10
- Crossover rate : 0.5
- Selection method : Steady state
- Hard constraint weight W 1 : 0.75
- Soft constraint weight W 2 : 0.25
- Mutation rate : Varied
This experiment is performed for 4 different mutation rates: 0.00, 0.02, 0.20 and
0.40. Each rate is tested 5 times. The chart of the average fitness value f(x) after 500
generations versus different mutation rates is given in Figure 4-4.
The best mutation rate is found to be 0.02. The mutation rates that are lower or
higher than this rate give slower evolution. This is shown definitely. If there is no
mutation (0.00), offspring are generated immediately after crossover without any
change. Therefore, the GA would fall into local optimum. On the other hand, the high
mutation rates usually cause the exploration of search space. The GA now can fall
into a random search space instead of searching from offspring of good parents.