Chapter 9 Results and discussion
9.3 Fine-tuning of DGA control parameter values
9.3.3 The Beligiannis Greek school timetabling problem
Table 9.88: Processes and parameter values to test best number of generations (Valouxis problem)
Constant Methods and Operators Phase 1
Heuristic Saturation Degree
Selection Variant
Mutation 1VH
Phase 2
Selection Variant
Mutation 2 Violation
Constant Parameter Values
SCM Size 50
Population Size 1000
Tournament Size 10
Swaps per mutation 100
The success rates listed in Table 9.89 indicate that feasible timetables are produced for all three tested generation values. In terms of quality, generation values of 50 and 75 produce the same quality timetables for all runs. This means that before this point, the algorithm has converged between generations 20 and 50. For the Valouxis problem, a generation parameter value of 50 is used. The timetables do not improve in quality after 50 generations.
Table 9.89: Results for different number of generations per phase Success Rates
20 50 75
100% 100% 100%
Average Quality
20 50 100
43.43 41.97 41.97
population. Table 9.90 lists the processes and parameter values used to test the DGA using the different SCM values.
Table 9.90: Processes and parameter values to test SCM size (Beligiannis problem) Constant Methods and Operators
Phase 1
Heuristic Saturation Degree (Largest degree for HS5)
Selection Variant
Mutation 1VH (1VNH for HS5)
Phase 2
Selection Standard
Mutation 1 Violation (Random swap for HS5) Constant Parameter Values Population Size 750
Tournament Size 10
Swaps per mutation 20 Number of generations 50
The DGA is tested using four SCM values and the success rates are listed below.
Table 9.91: Success rates for various SCM parameter values Success Rates
SCM = 1 SCM = 10 SCM = 25 SCM = 50
HS1 100.00% 100.00% 100.00% 100.00%
HS2 100.00% 100.00% 100.00% 100.00%
HS3 100.00% 100.00% 100.00% 100.00%
HS4 100.00% 100.00% 100.00% 100.00%
HS5 100.00% 100.00% 100.00% 100.00%
HS7 100.00% 100.00% 100.00% 100.00%
An SCM parameter value of 1 indicates that no SCM is used. Table 9.91 shows that for all runs, all SCM values produce feasible timetables. In order to determine the best SCM value, the average quality of the timetables produced must be compared. The average timetable quality found for each data set by applying the DGA with different SCM parameter values is shown in the Table 9.92.
Table 9.92: Average quality found per data set using different SCM parameter values Average Quality (and standard deviation)
SCM = 1 SCM = 10 SCM = 25 SCM = 50
HS1 117.67 (8.1) 115.10 (7.02) 114.03 (5.83) 115.77 (8)
HS2 120.27 (7.24) 122.87 (8.34) 123.20 (7.03) 122.53 (7.32)
HS3 49.27 (4.83) 48.00 (3.99) 48.20 (5.23) 48.20 (4.06)
HS4 75.83 (4.59) 74.27 (4.28) 73.63 (4.30) 74.03 (5.40)
HS5 57.40 (7.83) 55.53 (7.97) 57.47 (8.88) 54.80 (8.56)
HS7 139.80 (6.12) 138.13 (7.44) 137.33 (5.65) 137.53 (6.13)
For the data sets HS1, HS4 and HS7, an SCM value of 25 produces the best quality timetables. An SCM value of 1 and 10 produce the best quality timetables for data sets HS2 and HS3 respectively. When applied to data set HS5, the DGA produces the best quality timetables when using an SCM value of 50. When using this SCM value, the number of average soft constraint violations is reduced by at least one.
The success rates and average quality vary between data sets. Any of the tested SCM values could be used and feasible timetables are produced. In terms of quality, the ideal SCM parameter value varies between data sets. An SCM size of 25 is used when conducting the remaining tests for this problem.
9.3.3.2 Fine-tuning the population size
Three population sizes of 200, 500 and 750 are tested and 100% success rates are achieved when applying the DGA to all of the data sets, meaning that the DGA using any of the three population sizes tested manages to produce feasible timetables for every run (see Table 9.95). Trial runs using smaller population sizes were also attempted. While feasible timetables were found, timetable quality was poor when compared to larger population sizes (see Table 9.93).
Table 9.93: Trial runs for smaller populations sizes (Beligiannis problem) Populations size = 100
HS1 HS2 HS3 HS4 HS5 HS7
Average SC cost 125.78 136.78 53.56 79.11 75.13 145.44
Best SC cost 117 128 44 73 62 133
Population size = 50
HS1 HS2 HS3 HS4 HS5 HS7
Average SC cost 128 140.78 54.89 83.89 73.38 150.67
Best SC cost 117 131 52 79 63 145
Table 9.94 below shows the processes and parameter values that are kept constant when testing each population size.
Table 9.94: Processes and parameter values to test population size (Beligiannis problem)
Constant Methods and Operators Phase 1
Heuristic Saturation Degree (Largest degree for HS5)
Selection Variant
Mutation 1VH (1VNH for HS5)
Phase 2
Selection Standard
Mutation 1 Violation (Random swap for HS5) Constant Parameter Values
SCM size 25 (50 for HS5)
Tournament Size 10
Swaps per mutation 20 Number of generations 50
Table 9.95: Success rates for different population sizes Success Rates
Pop Size = 200 Pop Size = 500 Pop Size = 750
HS1 100.00% 100.00% 100.00%
HS2 100.00% 100.00% 100.00%
HS3 100.00% 100.00% 100.00%
HS4 100.00% 100.00% 100.00%
HS5 100.00% 100.00% 100.00%
HS7 100.00% 100.00% 100.00%
In order to determine the best population size, timetable quality must also be compared.
The average timetable quality found when the DGA approach is applied to each data set using different population sizes is listed in Table 9.96.
Table 9.96: Average quality produced for different population sizes Average Soft Constraint Violations (and standard deviations)
Pop Size = 200 Pop Size = 500 Pop Size = 750
HS1 120.03 (7.91) 117.40 (6.09) 114.03 (5.83)
HS2 129.40 (8.27) 125.83 (8.79) 123.20 (7.03)
HS3 51.57 (5.67) 49.80 (3.97) 48.20 (5.23)
HS4 76.60 (5.12) 74.07 (4.76) 73.63 (4.30)
HS5 61.70 (10.82) 59.30 (10.55) 54.80 (8.56)
HS7 143.07 (8.41) 138.00 (6.51) 137.33 (5.65)
The table shows that for all data sets, a population size of 750 produces the best quality timetable on average. The column chart in Figure 9.27 illustrates the results listed in Table 9.96. Figure 9.27 shows that the best quality timetables are produced when using the largest population size of 750.
Figure 9.27: Column chart showing quality of different population sizes
The population size to be used is 750 as the DGA approach found better quality timetables when using this parameter value.
9.3.3.3 Fine-tuning the tournament size
The DGA is run using different tournament sizes of 5, 10 and 15. The processes and parameter values used to test the tournament sizes are listed in the table below (Table 9.97). The results in terms of success rate when using different tournament size values are shown in the Table 9.98.
40.00 60.00 80.00 100.00 120.00 140.00
HS1 HS2 HS3 HS4 HS5 HS7
Average quality for changing population sizes
Pop Size = 200 Pop Size = 500 Pop Size = 750
Table 9.97: Processes and parameter values to test best tournament size (Beligiannis problem)
Constant Methods and Operators Phase 1
Heuristic Saturation Degree (Largest degree for HS5)
Selection Variant
Mutation 1VH (1VNH for HS5)
Phase 2
Selection Standard
Mutation 1 Violation (Random swap for HS5) Constant Parameter Values
SCM size 25 (50 for HS5)
Population size 750 Swaps per mutation 20 Number of generations 50
Table 9.98: Success rates produced for various tournament sizes Success Rates
Tourn Size = 5 Tourn Size = 10 Tourn Size = 15
HS1 100.00% 100.00% 100.00%
HS2 100.00% 100.00% 100.00%
HS3 100.00% 100.00% 100.00%
HS4 100.00% 100.00% 100.00%
HS5 100.00% 100.00% 100.00%
HS7 100.00% 100.00% 100.00%
In order to determine the tournament size, the average quality of timetables produced also needs to be analyzed. The quality of timetables produced using the DGA approach with different tournament sizes are listed in Table 9.99.
Table 9.99: Average quality produced using different tournament sizes Average SC Cost (and standard deviations)
Tourn Size = 5 Tourn Size = 10 Tourn Size = 15
HS1 115.37 (5.13) 114.03 (5.83) 114.30 (7.19)
HS2 122.13 (6.64) 123.20 (7.03) 121.87 (8.02)
HS3 46.20 (4.53) 48.20 (5.23) 47.43 (4.64)
HS4 71.70 (3.94) 73.63 (4.3) 72.07 (4.56)
HS5 52.63 (7.76) 54.80 (8.56) 57.90 (7.34)
HS7 137.60 (6.31) 137.33 (5.65) 136.90 (6.75)
Table 9.99 shows that for each data set, the DGA approach could use different tournament sizes in order to produce better quality timetables. For the larger data sets with more classes and teachers (HS1, HS2 and HS7), a tournament size of between 10 and 15 performs best (higher selection pressure) while for the smaller size data sets (HS3, HS4 and HS5), a tournament size of 5 produces the best quality timetables (lower selection pressure).
With the exception of data set HS5, the tournament size is set to 15. For data set HS5, a tournament size of 5 will be used as the average quality of timetables produced using this tournament size are found to be better than when using other tournament sizes.
9.3.3.4 Fine-tuning the number of swaps
This fine-tuning test determines the number of swaps that the mutation operator must perform when applied to each individual. Swap values of 20, 50, 100 and 200 were tested with the following processes and parameter values (Table 9.100):
Table 9.100: Processes and parameter values to test best number of swaps (Beligiannis problem)
Constant Methods and Operators Phase 1
Heuristic Saturation Degree (Largest degree for HS5)
Selection Variant
Mutation 1VH (1VNH for HS5)
Phase 2
Selection Standard
Mutation 1 Violation (Random swap for HS5) Constant Parameter Values
SCM size 25 (50 for HS5)
Population size 750
Tournament size 15 (5 for HS5) Number of generations 50
The success rate and average quality found for the DGA approach using four different swap parameter values is shown in Tables 9.101 and 9.102.
Table 9.101: Success rates produced using different swap parameter values Success Rates
Swaps = 20 Swaps = 50 Swaps = 100 Swaps = 200
HS1 100.00% 100.00% 100.00% 100.00%
HS2 100.00% 100.00% 100.00% 100.00%
HS3 100.00% 100.00% 100.00% 100.00%
HS4 100.00% 100.00% 100.00% 100.00%
HS5 100.00% 56.67% 13.33% 0.00%
HS7 100.00% 100.00% 100.00% 100.00%
Table 9.102: Average quality produced using different swap parameter values Average SC Cost (and standard deviations)
Swaps = 20 Swaps = 50 Swaps = 100 Swaps = 200
HS1 114.30 (7.19) 112.60 (6.63) 109.33 (6.72) 107.20 (6.21) HS2 121.87 (8.02) 118.20 (7.90) 117.77 (5.82) 113.23 (7.83)
HS3 47.43 (4.64) 45.13 (3.14) 45.00 (3.95) 42.47 (3.69)
HS4 72.07 (4.56) 71.77 (4.87) 69.70 (4.15) 68.80 (4.25)
HS5 52.63 (7.76) 51.94 (7.39) 48.50 (9.88) NA
HS7 136.90 (6.75) 135.70 (5.64) 135.17 (5.81) 130.40 (6.08)
Tables 9.101 and 9.102 show that, with the exception of data set HS5, 100% success rates are found when using the DGA approach with any swap parameter value. The average quality must be used to determine the best swap parameter value. With the exception of data set HS5, the DGA produces the best quality timetables when a swap parameter value of 200 is used. This conclusion is also be made when observing the column chart below (Figure 9.28).
Figure 9.28: Average quality found for different swap parameter values 0.00
20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.00
HS1 HS2 HS3 HS4 HS5 HS7
No of SC Violations
Data set
Average quality
Swaps = 20 Swaps = 50 Swaps = 100 Swaps = 200
The column chart also shows the effect on quality of timetables produced when increasing the swap parameter value. The table also indicates an inverse relationship between the number of swaps and the resultant number of soft constraint violations i.e. as the number of swaps increase, the number of soft constraint violations decrease. This trend is observed when the DGA is applied to any of the data sets.
Therefore, the best value for the number of swaps parameter is 200. For the HS5 data set, the best number of swaps is set to 20 as this swap parameter value produces the most number of feasible timetables.
9.3.3.5 Maximum number of generations
Generation parameter values of 20, 50 and 75 are used and the performance of the DGA is evaluated for each generation parameter value.
Table 9.103: Processes and parameter values to test best number of generations (Beligiannis problem)
Constant Methods and Operators Phase 1
Heuristic Saturation Degree (Largest degree for HS5)
Selection Variant
Mutation 1VH (1VNH for HS5)
Phase 2
Selection Standard
Mutation 1 Violation (Random swap for HS5) Constant Parameter Values
SCM size 25 (50 for HS5)
Population size 750
Tournament size 15 (5 for HS5) Number of swaps 200 (20 for HS5)
The results are displayed below and show the success rates and average quality of the timetables obtained.
Table 9.104: Success rates for varying number of generations Success Rates
Gens = 20 Gens = 50 Gens = 75
HS1 100% 100% 100%
HS2 100% 100% 100%
HS3 100% 100% 100%
HS4 100% 100% 100%
HS5 0% 100% 100%
HS7 100% 100% 100%
Table 9.105: Average quality produced for different generation parameter values Average SC Cost
Gens = 20 Gens = 50 Gens = 75
HS1 107.73 107.20 107.17
HS2 113.50 113.23 113.23
HS3 42.47 42.47 42.47
HS4 68.80 68.80 68.80
HS5 NA 52.63 52.33
HS7 130.80 130.40 130.40
100% success rates are found for all data sets except when using 20 generations for data set HS5. This indicates that for this data set, the DGA has not yet converged. In terms of timetable quality, the DGA converges before generation 20 for data sets HS3 and HS4 (the smaller data sets in terms of requirements, teachers and classes). For the data sets HS1 and HS5, timetable quality stops improving after generation 50 while for data sets HS2 and HS7, timetables stop improving after generation 20. The best number of generations (from the values tested) for all data sets is 75 as the algorithm would have converged at this point.