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Routing and Scheduling in Multistage Networks using Genetic Algorithms. Advisor: Dr. Yi Pan Chunyan Ji 3/26/01. Presentation Outline. Background and Motivation of this research Genetic Algorithm Analysis of Testing Results Simulation Package in Java Applet Conclusion and Future work Demo. - PowerPoint PPT Presentation
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Routing and Scheduling in Multistage Networks using Genetic AlgorithmsAdvisor: Dr. Yi PanChunyan Ji3/26/01
Presentation OutlineBackground and Motivation of this researchGenetic AlgorithmAnalysis of Testing ResultsSimulation Package in Java AppletConclusion and Future workDemo
Background and Motivation of this researchMultistage Interconnection NetworkNetwork size N=2n (n is the number of stages) N/2 switching elements in each stage
Crosstalk in OMINTwo ways to produce undesired coupling in a Switching Element
Approaches to avoid crosstalk2N*2N regular OMIN to provide N*N connectionRouting traffic through an N*N OMIN to avoid coupling two signals within each Switching Element
Legal path in SW at a timePaths without crosstalk in SE:
Omega NetworkEach connection between stages is shuffle-exchanged000->000001->010010->100 111->111
Routing in Omega Network
Routing same ex. in 2 passes
Routing same ex. in 2 passes
The Window Method
Conflict Graph
Routing AlgorithmWhile (not end of messages list) 1. Select one of the left messages;2. Schedule the message in a time slot with no conflict with other messages that have been already scheduled.
Four Routing AlgorithmsSequential Algorithm: Choose a message in increasing order of the message source address.Seq-Down Algorithm: Choose a message in decreasing order of the message source address.Degree-ascending Algo: Choose a message in the order of the increasing degrees in conflict graph.Degree-descending Algo: Choose a message in the order of the decreasing degrees in conflict graph
Genetic Algorithm
ChromosomesBinary: 01011010Permutation encoding:21314231Index represents the node in the graph and the integer value represents the color of its corresponding node
Operators of GACrossoverMutationSelection
CrossoverSingle Crossover:Parent 1: 2311242212341Parent 2: 1232422311243After crossover,Offspring 1: 2311242311243Offspring 2: 1232422212341
Operators of GA(cont.)Double Crossover
Parent 1: 2311242212341Parent 2: 1232422311243After double crossover,Offspring 1: 2312422312341Offspring 2: 1231242211243
MutationOffspring from the crossover:Offspring 1: 2311242311243Offspring 2: 1232422212341Offspring after mutation:Offspring 1: 2312242311243Offspring 2: 1232322212311
SelectionFitness Function:number of colorsvalid solutionsBetting fitting offspring (less number of colors) gets to be the parent of next generation
Parameters of GACrossover ProbabilityMutation ProbabilityPopulation SizeNumber of Generations
Example
Sequential Algo. Coloring
Degree-descending Coloring
GA Coloring(MP=0.1,Gen=100)
Analysis of testing results
Sheet1
nodesRoundsGensMu ProbCrsOverPopuSeqAlgoDescendsmallestGeneticTimediff(S-G)diff(D-G)
8100100.5Single22.722.582.562.5600.02
10010000.5Single22.732.592.572.5700.02
820010000.5Single22.6652.512.4852.48!!0.0050.03
100100.001Single42.782.682.672.671 sec00.01
1001000.001Single42.772.652.632.638 sec00.02
10010000.001Single42.782.612.582.5874 sec00.03
20010000.001Single42.72.562.5252.525147 sec00.035
16100100.5Single23.753.493.423.4200.07
1001000.5Single23.663.523.463.45!!0.010.07
1001000.5While23.693.543.453.42!!0.030.12
00
(#4)100100.01While43.653.453.363.350.010.1
(#8)100100.001While43.73.533.433.410.020.12
(#9)100200.01While43.823.523.453.440.010.08
(#9)100200.001While43.73.593.513.50.010.09
(#4)100300.01While43.773.543.413.40.010.14
(#4)100300.001While43.733.533.453.440.010.09
(#7)100500.01While43.63.523.373.360.010.16
(#20)100500.001While43.633.533.383.370.010.16
(#9)100800.01While43.693.513.393.380.010.13
(#4)100800.001While43.783.553.53.490.010.06
(#5)1001000.01While43.673.473.383.370.010.1
(#2)1001000.001While43.723.523.433.420.010.1
(#4)1001000.001Double43.743.493.383.3722 sec0.010.12
(#1)100010000.001Double43.6983.5433.4323.4339m24s0.0020.113
(#1)100010000.001Single43.6943.5183.4163.41259m28s0.0040.106
321001000.5While24.524.354.274.2700.08
10010000.5While254.44.44.3>13hours0.10.1
20200.001While4No improvement whithin 100 times
(#38)20500.001While44.654.44.34.2519 sec0.050.15
(#11)201000.001While44.654.44.254.236 sec0.050.2
(#24)50100.001While44.464.264.124.110 sec0.020.16
(#23)50200.001While44.544.284.264.2420 sec0.020.04
(#12)50500.001While44.64.384.344.3247 sec0.020.06
(#15)501000.001While44.584.34.264.2495 sec0.020.06
(#5)10050.001While44.54.244.24.1910 sec0.010.05
(#2)100100.001While44.544.284.214.1919 sec0.020.09
(#1)100200.001While44.54.244.194.1837 sec0.010.06
(#6)100500.001While44.534.274.174.1691 sec0.010.11
(#3)1001000.001While44.474.264.194.18197 sec0.010.08
641001000.5While2more than 24 hours
(#4)100100.001While45.325.055.025.011m29s0.010.04
(#5)100200.001While45.295.044.994.982m57s0.010.06
(#10)100500.001While45.354.984.964.957m39s0.010.03
(#1)1001000.001While45.285.065.035.0214m27s0.010.04
1281001000.001While46.195.875.855.8461m38s0.010.03
nodesRoundsGensMu ProbCrsOverPopuSeqAlgoDescendsmallestGeneticTime
8100100.5Single22.722.582.562.56
810010000.5Single22.732.592.572.57
820010000.5Single22.6652.512.4852.488 hours
16100100.5Single23.753.493.423.42
161001000.5Single23.663.523.463.454 hours
321001000.5Single24.524.354.274.27
3210010000.5Single254.44.44.313hours
641001000.5Single2more than 24 hours, and less valid offspring
nodesRoundsGensMu ProbCrsOverPopuSeqAlgoDescendsmallestGeneticTime
8100100.001Single42.782.682.672.671 sec
81001000.001Single42.772.652.632.638 sec
810010000.001Single42.782.612.582.5874 sec
820010000.001Single42.72.562.5252.525147 sec
161001000.001Single43.723.523.433.4238sec
16100010000.001Single43.6943.5183.4163.41259m28s
32501000.001Single44.584.34.264.2495 sec
321001000.001Single44.474.264.194.18197 sec
64100500.001Single45.354.984.964.957m39s
641001000.001Single45.285.065.035.0214m27s
1281001000.001Single46.195.875.855.8461m38s
Sheet2
Sheet3
Color-exchanging Mutation results
Sheet1
nodesRoundsGensMu ProbCrsOverPopuSeqAlgoDescendsmallestGeneticTimediff(S-G)diff(D-G)
8100100.5Single22.722.582.562.5600.02
10010000.5Single22.732.592.572.5700.02
820010000.5Single22.6652.512.4852.48!!0.0050.03
100100.001Single42.782.682.672.671 sec00.01
1001000.001Single42.772.652.632.638 sec00.02
10010000.001Single42.782.612.582.5874 sec00.03
20010000.001Single42.72.562.5252.525147 sec00.035
16100100.5Single23.753.493.423.4200.07
1001000.5Single23.663.523.463.45!!0.010.07
1001000.5While23.693.543.453.42!!0.030.12
00
(#4)100100.01While43.653.453.363.350.010.1
(#8)100100.001While43.73.533.433.410.020.12
(#9)100200.01While43.823.523.453.440.010.08
(#9)100200.001While43.73.593.513.50.010.09
(#4)100300.01While43.773.543.413.40.010.14
(#4)100300.001While43.733.533.453.440.010.09
(#7)100500.01While43.63.523.373.360.010.16
(#20)100500.001While43.633.533.383.370.010.16
(#9)100800.01While43.693.513.393.380.010.13
(#4)100800.001While43.783.553.53.490.010.06
(#5)1001000.01While43.673.473.383.370.010.1
(#2)1001000.001While43.723.523.433.420.010.1
(#4)1001000.001Double43.743.493.383.3722 sec0.010.12
(#1)100010000.001Double43.6983.5433.4323.4339m24s0.0020.113
(#1)100010000.001Single43.6943.5183.4163.41259m28s0.0040.106
321001000.5While24.524.354.274.2700.08
10010000.5While254.44.44.3>13hours0.10.1
20200.001While4No improvement whithin 100 times
(#38)20500.001While44.654.44.34.2519 sec0.050.15
(#11)201000.001While44.654.44.254.236 sec0.050.2
(#24)50100.001While44.464.264.124.110 sec0.020.16
(#23)50200.001While44.544.284.264.2420 sec0.020.04
(#12)50500.001While44.64.384.344.3247 sec0.020.06
(#15)501000.001While44.584.34.264.2495 sec0.020.06
(#5)10050.001While44.54.244.24.1910 sec0.010.05
(#2)100100.001While44.544.284.214.1919 sec0.020.09
(#1)100200.001While44.54.244.194.1837 sec0.010.06
(#6)100500.001While44.534.274.174.1691 sec0.010.11
(#3)1001000.001While44.474.264.194.18197 sec0.010.08
641001000.5While2more than 24 hours
(#4)100100.001While45.325.055.025.011m29s0.010.04
(#5)100200.001While45.295.044.994.982m57s0.010.06
(#10)100500.001While45.354.984.964.957m39s0.010.03
(#1)1001000.001While45.285.065.035.0214m27s0.010.04
1281001000.001While46.195.875.855.8461m38s0.010.03
nodesRoundsGensMu ProbCrsOverPopuSeqAlgoDescendsmallestGeneticTime
8100100.5Single22.722.582.562.56
810010000.5Single22.732.592.572.57
820010000.5Single22.6652.512.4852.488 hours
16100100.5Single23.753.493.423.42
161001000.5Single23.663.523.463.454 hours
321001000.5Single24.524.354.274.27
3210010000.5Single254.44.44.313hours
641001000.5Single2more than 24 hours, and less valid offspring
nodesRoundsGensMu ProbCrsOverPopuSeqAlgoDescendsmallestGeneticTime
8100100.001Single42.782.682.672.671 sec
81001000.001Single42.772.652.632.638 sec
810010000.001Single42.782.612.582.5874 sec
820010000.001Single42.72.562.5252.525147 sec
161001000.001Single43.723.523.433.4238sec
16100010000.001Single43.6943.5183.4163.41259m28s
32501000.001Single44.584.34.264.2495 sec
321001000.001Single44.474.264.194.18197 sec
64100500.001Single45.354.984.964.957m39s
641001000.001Single45.285.065.035.0214m27s
1281001000.001Single46.195.875.855.8461m38s
Sheet2
Sheet3
Generations affects GA
Chart4
3.743.72
3.493.52
3.383.43
3.373.42
Double Crossover
Single Crossover
Seq->Des->Sma->Genetic
Average Passes
100Rounds,100Gens(16*16),MP=0.001
Chart5
2.772.652.632.63
3.723.523.433.415
4.474.264.194.175
5.285.065.035.01
Sequential Algorithm
Degree Descending Algorithm
Smallest of Four Algorithm
Genetic Algorithm
Number of Nodes(8,16,32,64...)
Average Passes
100 Rnds, 100 Gens, MP=0.001
Chart7
3.6953.684
3.5283.528
3.4053.42
3.4023.416
Double Crossover
Single Crossover
Seq->Des->Sma->Genetic
Average Passes
1000Rnds,1000Gens(16*16),MP=0.001
Sheet1
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.013.653.453.363.35
100200.013.823.523.453.44
100300.013.773.543.413.4
100500.013.63.523.373.36
100800.013.693.613.393.38
1001000.013.673.473.383.37
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.0013.73.533.433.41
100200.0013.73.593.513.5
100300.0013.733.533.453.44
100500.0013.633.533.383.37
100800.0013.783.553.53.49
1001000.0013.723.523.433.42
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.13.663.453.383.36
100200.13.763.613.543.52
100300.13.813.573.53.48
100500.13.743.663.543.51
100800.13.723.533.463.43
1001000.13.823.583.463.43
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGeneticdiff:Sm-G
100100.13.733.53.393.360.03
100200.13.693.543.483.470.01
100300.13.73.53.413.350.06
100500.13.753.553.463.370.09
100800.13.783,63.523.470.05
1001000.13.733.563.493.460.03
3.6953.5283.4053.402
3.6843.5283.423.416
3.743.493.383.37
3.723.523.433.42
2.773.724.475.28
2.653.524.265.06
2.633.434.195.03
2.633.4154.1755.01
mp=0.282.842.752.72.652.84
163.773.613.493.453.77
324.514.194.164.144.51
64(0.1)5.295.075.055.045.29
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.013.7033.553.4433.44150
10000.013.7033.553.4433.436100
10000.013.7033.553.4433.43150
10000.013.7033.553.4433.428200
10000.013.7033.553.4433.428250
10000.013.7033.553.4433.425300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.13.7183.5413.443.41450
10000.13.7183.5413.443.402100
10000.13.7183.5413.443.391150
10000.13.7183.5413.443.389200
10000.13.7183.5413.443.387250
10000.13.7183.5413.443.387300
Sheet2
Sheet2
3.663.763.813.743.723.82
3.453.613.573.663.533.58
3.383.543.53.543.463.46
3.363.523.483.513.433.43
10 generations
20 generations
30 generations
50 generations
80 generations
100 genreations
Sequential->Descending->Smallest->Genetic Algorithm
Average passes
100Rounds(16*16), MP=0.1
Sheet3
3.73.73.733.633.783.72
3.533.593.533.533.553.52
3.433.513.453.383.53.43
3.413.53.443.373.493.42
10 generations
20 generations
30 generations
50 generations
80 generations
100 generations
Sequential->Descending->Smallest->Genetic Algorithm
Average Passes
100 Rounds(16*16), MP=0.001
3.7033.553.4433.441
3.7033.553.4433.436
3.7033.553.4433.43
3.7033.553.4433.428
3.7033.553.4433.428
3.7033.553.4433.425
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of Various Generations(MP=0.1,Rnds=1000)
3.7183.5413.443.414
3.7183.5413.443.402
3.7183.5413.443.391
3.7183.5413.443.389
3.7183.5413.443.387
3.7183.5413.443.387
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.1,Rnds=1000)
Generations(MP=0.1)
Chart2
3.7183.5413.443.414
3.7183.5413.443.402
3.7183.5413.443.391
3.7183.5413.443.389
3.7183.5413.443.387
3.7183.5413.443.387
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.1,Rnds=1000)
Chart4
3.743.72
3.493.52
3.383.43
3.373.42
Double Crossover
Single Crossover
Seq->Des->Sma->Genetic
Average Passes
100Rounds,100Gens(16*16),MP=0.001
Chart5
2.772.652.632.63
3.723.523.433.415
4.474.264.194.175
5.285.065.035.01
Sequential Algorithm
Degree Descending Algorithm
Smallest of Four Algorithm
Genetic Algorithm
Number of Nodes(8,16,32,64...)
Average Passes
100 Rnds, 100 Gens, MP=0.001
Chart7
3.6953.684
3.5283.528
3.4053.42
3.4023.416
Double Crossover
Single Crossover
Seq->Des->Sma->Genetic
Average Passes
1000Rnds,1000Gens(16*16),MP=0.001
Sheet1
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.013.653.453.363.35
100200.013.823.523.453.44
100300.013.773.543.413.4
100500.013.63.523.373.36
100800.013.693.613.393.38
1001000.013.673.473.383.37
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.0013.73.533.433.41
100200.0013.73.593.513.5
100300.0013.733.533.453.44
100500.0013.633.533.383.37
100800.0013.783.553.53.49
1001000.0013.723.523.433.42
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.13.663.453.383.36
100200.13.763.613.543.52
100300.13.813.573.53.48
100500.13.743.663.543.51
100800.13.723.533.463.43
1001000.13.823.583.463.43
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGeneticdiff:Sm-G
100100.13.733.53.393.360.03
100200.13.693.543.483.470.01
100300.13.73.53.413.350.06
100500.13.753.553.463.370.09
100800.13.783,63.523.470.05
1001000.13.733.563.493.460.03
3.6953.5283.4053.402
3.6843.5283.423.416
3.743.493.383.37
3.723.523.433.42
2.773.724.475.28
2.653.524.265.06
2.633.434.195.03
2.633.4154.1755.01
mp=0.282.842.752.72.652.84
163.773.613.493.453.77
324.514.194.164.144.51
64(0.1)5.295.075.055.045.29
seqdessmallGenetic
3.7033.553.4433.441
3.7033.553.4433.436
3.7033.553.4433.43
3.7033.553.4433.428
3.7033.553.4433.428
3.7033.553.4433.425
seqdessmallGenetic
3.7183.5413.443.414
3.7183.5413.443.402
3.7183.5413.443.391
3.7183.5413.443.389
3.7183.5413.443.387
3.7183.5413.443.387
Sheet2
Sheet2
3.663.763.813.743.723.82
3.453.613.573.663.533.58
3.383.543.53.543.463.46
3.363.523.483.513.433.43
10 generations
20 generations
30 generations
50 generations
80 generations
100 genreations
Sequential->Descending->Smallest->Genetic Algorithm
Average passes
100Rounds(16*16), MP=0.1
Sheet3
3.73.73.733.633.783.72
3.533.593.533.533.553.52
3.433.513.453.383.53.43
3.413.53.443.373.493.42
10 generations
20 generations
30 generations
50 generations
80 generations
100 generations
Sequential->Descending->Smallest->Genetic Algorithm
Average Passes
100 Rounds(16*16), MP=0.001
3.7033.553.4433.441
3.7033.553.4433.436
3.7033.553.4433.43
3.7033.553.4433.428
3.7033.553.4433.428
3.7033.553.4433.425
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of Various Generations(MP=0.1,Rnds=1000)
3.7183.5413.443.414
3.7183.5413.443.402
3.7183.5413.443.391
3.7183.5413.443.389
3.7183.5413.443.387
3.7183.5413.443.387
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.1,Rnds=1000)
Generations(MP=0.01)
Chart1
3.7033.553.4433.441
3.7033.553.4433.436
3.7033.553.4433.43
3.7033.553.4433.428
3.7033.553.4433.428
3.7033.553.4433.425
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of Various Generations(MP=0.01,Rnds=1000)
Chart4
3.743.72
3.493.52
3.383.43
3.373.42
Double Crossover
Single Crossover
Seq->Des->Sma->Genetic
Average Passes
100Rounds,100Gens(16*16),MP=0.001
Chart5
2.772.652.632.63
3.723.523.433.415
4.474.264.194.175
5.285.065.035.01
Sequential Algorithm
Degree Descending Algorithm
Smallest of Four Algorithm
Genetic Algorithm
Number of Nodes(8,16,32,64...)
Average Passes
100 Rnds, 100 Gens, MP=0.001
Chart7
3.6953.684
3.5283.528
3.4053.42
3.4023.416
Double Crossover
Single Crossover
Seq->Des->Sma->Genetic
Average Passes
1000Rnds,1000Gens(16*16),MP=0.001
Sheet1
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.013.653.453.363.35
100200.013.823.523.453.44
100300.013.773.543.413.4
100500.013.63.523.373.36
100800.013.693.613.393.38
1001000.013.673.473.383.37
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.0013.73.533.433.41
100200.0013.73.593.513.5
100300.0013.733.533.453.44
100500.0013.633.533.383.37
100800.0013.783.553.53.49
1001000.0013.723.523.433.42
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.13.663.453.383.36
100200.13.763.613.543.52
100300.13.813.573.53.48
100500.13.743.663.543.51
100800.13.723.533.463.43
1001000.13.823.583.463.43
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGeneticdiff:Sm-G
100100.13.733.53.393.360.03
100200.13.693.543.483.470.01
100300.13.73.53.413.350.06
100500.13.753.553.463.370.09
100800.13.783,63.523.470.05
1001000.13.733.563.493.460.03
3.6953.5283.4053.402
3.6843.5283.423.416
3.743.493.383.37
3.723.523.433.42
2.773.724.475.28
2.653.524.265.06
2.633.434.195.03
2.633.4154.1755.01
mp=0.282.842.752.72.652.84
163.773.613.493.453.77
324.514.194.164.144.51
64(0.1)5.295.075.055.045.29
seqdessmallGenetic
3.7033.553.4433.441
3.7033.553.4433.436
3.7033.553.4433.43
3.7033.553.4433.428
3.7033.553.4433.428
3.7033.553.4433.425
Sheet2
Sheet2
3.663.763.813.743.723.82
3.453.613.573.663.533.58
3.383.543.53.543.463.46
3.363.523.483.513.433.43
10 generations
20 generations
30 generations
50 generations
80 generations
100 genreations
Sequential->Descending->Smallest->Genetic Algorithm
Average passes
100Rounds(16*16), MP=0.1
Sheet3
3.73.73.733.633.783.72
3.533.593.533.533.553.52
3.433.513.453.383.53.43
3.413.53.443.373.493.42
10 generations
20 generations
30 generations
50 generations
80 generations
100 generations
Sequential->Descending->Smallest->Genetic Algorithm
Average Passes
100 Rounds(16*16), MP=0.001
3.7033.553.4433.441
3.7033.553.4433.436
3.7033.553.4433.43
3.7033.553.4433.428
3.7033.553.4433.428
3.7033.553.4433.425
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of Various Generations(MP=0.1,Rnds=1000)
Generations(MP=0.3)
Chart8
3.6943.5513.4333.403
3.6943.5513.4333.389
3.6943.5513.4333.384
3.6943.5513.4333.382
3.6943.5513.4333.38
3.6943.5513.4333.38
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.3,Rnds=1000)
Chart4
3.743.72
3.493.52
3.383.43
3.373.42
Double Crossover
Single Crossover
Seq->Des->Sma->Genetic
Average Passes
100Rounds,100Gens(16*16),MP=0.001
Chart5
2.772.652.632.63
3.723.523.433.415
4.474.264.194.175
5.285.065.035.01
Sequential Algorithm
Degree Descending Algorithm
Smallest of Four Algorithm
Genetic Algorithm
Number of Nodes(8,16,32,64...)
Average Passes
100 Rnds, 100 Gens, MP=0.001
Chart7
3.6953.684
3.5283.528
3.4053.42
3.4023.416
Double Crossover
Single Crossover
Seq->Des->Sma->Genetic
Average Passes
1000Rnds,1000Gens(16*16),MP=0.001
Sheet1
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.013.653.453.363.35
100200.013.823.523.453.44
100300.013.773.543.413.4
100500.013.63.523.373.36
100800.013.693.613.393.38
1001000.013.673.473.383.37
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.0013.73.533.433.41
100200.0013.73.593.513.5
100300.0013.733.533.453.44
100500.0013.633.533.383.37
100800.0013.783.553.53.49
1001000.0013.723.523.433.42
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.13.663.453.383.36
100200.13.763.613.543.52
100300.13.813.573.53.48
100500.13.743.663.543.51
100800.13.723.533.463.43
1001000.13.823.583.463.43
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGeneticdiff:Sm-G
100100.13.733.53.393.360.03
100200.13.693.543.483.470.01
100300.13.73.53.413.350.06
100500.13.753.553.463.370.09
100800.13.783,63.523.470.05
1001000.13.733.563.493.460.03
3.6953.5283.4053.402
3.6843.5283.423.416
3.743.493.383.37
3.723.523.433.42
2.773.724.475.28
2.653.524.265.06
2.633.434.195.03
2.633.4154.1755.01
mp=0.282.842.752.72.652.84
163.773.613.493.453.77
324.514.194.164.144.51
64(0.1)5.295.075.055.045.29
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.013.7033.553.4433.44150
10000.013.7033.553.4433.436100
10000.013.7033.553.4433.43150
10000.013.7033.553.4433.428200
10000.013.7033.553.4433.428250
10000.013.7033.553.4433.425300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.13.7183.5413.443.41450
10000.13.7183.5413.443.402100
10000.13.7183.5413.443.391150
10000.13.7183.5413.443.389200
10000.13.7183.5413.443.387250
10000.13.7183.5413.443.387300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.0013.7093.5373.4443.444
10000.0013.7093.5373.4443.444
10000.0013.7093.5373.4443.443
10000.0013.7093.5373.4443.443
10000.0013.7093.5373.4443.443
10000.0013.7093.5373.4443.442
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
0.13.7123.5423.4443.406
3.7123.5423.4443.396
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.23.7343.5693.4553.415
10000.23.7343.5693.4553.401
10000.23.7343.5693.4553.395
10000.23.7343.5693.4553.394
10000.23.7343.5693.4553.394
10000.23.7343.5693.4553.394
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.33.6943.5513.4333.403
10000.33.6943.5513.4333.389
10000.33.6943.5513.4333.384
10000.33.6943.5513.4333.382
10000.33.6943.5513.4333.38
10000.33.6943.5513.4333.38
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.43.7163.5573.4453.445
10000.43.7163.5573.4453.444
10000.43.7163.5573.4453.444
10000.43.7163.5573.4453.444
10000.43.7163.5573.4453.444
10000.43.7163.5573.4453.444
Sheet2
Sheet2
3.663.763.813.743.723.82
3.453.613.573.663.533.58
3.383.543.53.543.463.46
3.363.523.483.513.433.43
10 generations
20 generations
30 generations
50 generations
80 generations
100 genreations
Sequential->Descending->Smallest->Genetic Algorithm
Average passes
100Rounds(16*16), MP=0.1
Sheet3
3.73.73.733.633.783.72
3.533.593.533.533.553.52
3.433.513.453.383.53.43
3.413.53.443.373.493.42
10 generations
20 generations
30 generations
50 generations
80 generations
100 generations
Sequential->Descending->Smallest->Genetic Algorithm
Average Passes
100 Rounds(16*16), MP=0.001
3.7033.553.4433.441
3.7033.553.4433.436
3.7033.553.4433.43
3.7033.553.4433.428
3.7033.553.4433.428
3.7033.553.4433.425
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of Various Generations(MP=0.1,Rnds=1000)
3.7093.5373.4443.444
3.7093.5373.4443.444
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.442
3.7183.5413.443.414
3.7183.5413.443.402
3.7183.5413.443.391
3.7183.5413.443.389
3.7183.5413.443.387
3.7183.5413.443.387
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.1,Rnds=1000)
3.7123.5423.4443.406
3.7123.5423.4443.396
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.2,Rnds=1000)
3.7093.5373.4443.444
3.7093.5373.4443.444
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.442
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comprisons of various Generations(MP=0.001,Rnds=1000)
3.6943.5513.4333.403
3.6943.5513.4333.389
3.6943.5513.4333.384
3.6943.5513.4333.382
3.6943.5513.4333.38
3.6943.5513.4333.38
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.3,Rnds=1000)
Generations(MP=0.4)
Chart9
3.7163.5573.4453.445
3.7163.5573.4453.444
3.7163.5573.4453.444
3.7163.5573.4453.444
3.7163.5573.4453.444
3.7163.5573.4453.444
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.4,Rnds=1000)
Chart4
3.743.72
3.493.52
3.383.43
3.373.42
Double Crossover
Single Crossover
Seq->Des->Sma->Genetic
Average Passes
100Rounds,100Gens(16*16),MP=0.001
Chart5
2.772.652.632.63
3.723.523.433.415
4.474.264.194.175
5.285.065.035.01
Sequential Algorithm
Degree Descending Algorithm
Smallest of Four Algorithm
Genetic Algorithm
Number of Nodes(8,16,32,64...)
Average Passes
100 Rnds, 100 Gens, MP=0.001
Chart7
3.6953.684
3.5283.528
3.4053.42
3.4023.416
Double Crossover
Single Crossover
Seq->Des->Sma->Genetic
Average Passes
1000Rnds,1000Gens(16*16),MP=0.001
Sheet1
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.013.653.453.363.35
100200.013.823.523.453.44
100300.013.773.543.413.4
100500.013.63.523.373.36
100800.013.693.613.393.38
1001000.013.673.473.383.37
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.0013.73.533.433.41
100200.0013.73.593.513.5
100300.0013.733.533.453.44
100500.0013.633.533.383.37
100800.0013.783.553.53.49
1001000.0013.723.523.433.42
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.13.663.453.383.36
100200.13.763.613.543.52
100300.13.813.573.53.48
100500.13.743.663.543.51
100800.13.723.533.463.43
1001000.13.823.583.463.43
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGeneticdiff:Sm-G
100100.13.733.53.393.360.03
100200.13.693.543.483.470.01
100300.13.73.53.413.350.06
100500.13.753.553.463.370.09
100800.13.783,63.523.470.05
1001000.13.733.563.493.460.03
3.6953.5283.4053.402
3.6843.5283.423.416
3.743.493.383.37
3.723.523.433.42
2.773.724.475.28
2.653.524.265.06
2.633.434.195.03
2.633.4154.1755.01
mp=0.282.842.752.72.652.84
163.773.613.493.453.77
324.514.194.164.144.51
64(0.1)5.295.075.055.045.29
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.013.7033.553.4433.44150
10000.013.7033.553.4433.436100
10000.013.7033.553.4433.43150
10000.013.7033.553.4433.428200
10000.013.7033.553.4433.428250
10000.013.7033.553.4433.425300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.13.7183.5413.443.41450
10000.13.7183.5413.443.402100
10000.13.7183.5413.443.391150
10000.13.7183.5413.443.389200
10000.13.7183.5413.443.387250
10000.13.7183.5413.443.387300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.0013.7093.5373.4443.444
10000.0013.7093.5373.4443.444
10000.0013.7093.5373.4443.443
10000.0013.7093.5373.4443.443
10000.0013.7093.5373.4443.443
10000.0013.7093.5373.4443.442
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
0.13.7123.5423.4443.406
3.7123.5423.4443.396
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.23.7343.5693.4553.415
10000.23.7343.5693.4553.401
10000.23.7343.5693.4553.395
10000.23.7343.5693.4553.394
10000.23.7343.5693.4553.394
10000.23.7343.5693.4553.394
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.33.6943.5513.4333.40350
10000.33.6943.5513.4333.389100
10000.33.6943.5513.4333.384150
10000.33.6943.5513.4333.382200
10000.33.6943.5513.4333.38250
10000.33.6943.5513.4333.38300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.43.7163.5573.4453.44550
10000.43.7163.5573.4453.444100
10000.43.7163.5573.4453.444150
10000.43.7163.5573.4453.444200
10000.43.7163.5573.4453.444250
10000.43.7163.5573.4453.444300
Sheet2
Sheet2
3.663.763.813.743.723.82
3.453.613.573.663.533.58
3.383.543.53.543.463.46
3.363.523.483.513.433.43
10 generations
20 generations
30 generations
50 generations
80 generations
100 genreations
Sequential->Descending->Smallest->Genetic Algorithm
Average passes
100Rounds(16*16), MP=0.1
Sheet3
3.73.73.733.633.783.72
3.533.593.533.533.553.52
3.433.513.453.383.53.43
3.413.53.443.373.493.42
10 generations
20 generations
30 generations
50 generations
80 generations
100 generations
Sequential->Descending->Smallest->Genetic Algorithm
Average Passes
100 Rounds(16*16), MP=0.001
3.7033.553.4433.441
3.7033.553.4433.436
3.7033.553.4433.43
3.7033.553.4433.428
3.7033.553.4433.428
3.7033.553.4433.425
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of Various Generations(MP=0.1,Rnds=1000)
3.7093.5373.4443.444
3.7093.5373.4443.444
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.442
3.7183.5413.443.414
3.7183.5413.443.402
3.7183.5413.443.391
3.7183.5413.443.389
3.7183.5413.443.387
3.7183.5413.443.387
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.1,Rnds=1000)
3.7123.5423.4443.406
3.7123.5423.4443.396
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.2,Rnds=1000)
3.7093.5373.4443.444
3.7093.5373.4443.444
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.442
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comprisons of various Generations(MP=0.001,Rnds=1000)
3.6943.5513.4333.403
3.6943.5513.4333.389
3.6943.5513.4333.384
3.6943.5513.4333.382
3.6943.5513.4333.38
3.6943.5513.4333.38
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.3,Rnds=1000)
3.7163.5573.4453.445
3.7163.5573.4453.444
3.7163.5573.4453.444
3.7163.5573.4453.444
3.7163.5573.4453.444
3.7163.5573.4453.444
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.4,Rnds=1000)
Generations(MP=0.001)
Chart6
3.7093.5373.4443.444
3.7093.5373.4443.444
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.442
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comprisons of various Generations(MP=0.001,Rnds=1000)
Chart4
3.743.72
3.493.52
3.383.43
3.373.42
Double Crossover
Single Crossover
Seq->Des->Sma->Genetic
Average Passes
100Rounds,100Gens(16*16),MP=0.001
Chart5
2.772.652.632.63
3.723.523.433.415
4.474.264.194.175
5.285.065.035.01
Sequential Algorithm
Degree Descending Algorithm
Smallest of Four Algorithm
Genetic Algorithm
Number of Nodes(8,16,32,64...)
Average Passes
100 Rnds, 100 Gens, MP=0.001
Chart7
3.6953.684
3.5283.528
3.4053.42
3.4023.416
Double Crossover
Single Crossover
Seq->Des->Sma->Genetic
Average Passes
1000Rnds,1000Gens(16*16),MP=0.001
Sheet1
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.013.653.453.363.35
100200.013.823.523.453.44
100300.013.773.543.413.4
100500.013.63.523.373.36
100800.013.693.613.393.38
1001000.013.673.473.383.37
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.0013.73.533.433.41
100200.0013.73.593.513.5
100300.0013.733.533.453.44
100500.0013.633.533.383.37
100800.0013.783.553.53.49
1001000.0013.723.523.433.42
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.13.663.453.383.36
100200.13.763.613.543.52
100300.13.813.573.53.48
100500.13.743.663.543.51
100800.13.723.533.463.43
1001000.13.823.583.463.43
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGeneticdiff:Sm-G
100100.13.733.53.393.360.03
100200.13.693.543.483.470.01
100300.13.73.53.413.350.06
100500.13.753.553.463.370.09
100800.13.783,63.523.470.05
1001000.13.733.563.493.460.03
3.6953.5283.4053.402
3.6843.5283.423.416
3.743.493.383.37
3.723.523.433.42
2.773.724.475.28
2.653.524.265.06
2.633.434.195.03
2.633.4154.1755.01
mp=0.282.842.752.72.652.84
163.773.613.493.453.77
324.514.194.164.144.51
64(0.1)5.295.075.055.045.29
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.013.7033.553.4433.44150
10000.013.7033.553.4433.436100
10000.013.7033.553.4433.43150
10000.013.7033.553.4433.428200
10000.013.7033.553.4433.428250
10000.013.7033.553.4433.425300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.13.7183.5413.443.41450
10000.13.7183.5413.443.402100
10000.13.7183.5413.443.391150
10000.13.7183.5413.443.389200
10000.13.7183.5413.443.387250
10000.13.7183.5413.443.387300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.0013.7093.5373.4443.444
10000.0013.7093.5373.4443.444
10000.0013.7093.5373.4443.443
10000.0013.7093.5373.4443.443
10000.0013.7093.5373.4443.443
10000.0013.7093.5373.4443.442
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
0.13.7123.5423.4443.406
3.7123.5423.4443.396
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.23.7343.5693.4553.415
10000.23.7343.5693.4553.401
10000.23.7343.5693.4553.395
10000.23.7343.5693.4553.394
10000.23.7343.5693.4553.394
10000.23.7343.5693.4553.394
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.33.6943.5513.4333.403
10000.33.6943.5513.4333.389
10000.33.6943.5513.4333.384
10000.33.6943.5513.4333.382
10000.33.6943.5513.4333.38
10000.33.6943.5513.4333.38
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.43.7163.5573.4453.445
10000.43.7163.5573.4453.444
10000.43.7163.5573.4453.444
10000.43.7163.5573.4453.444
10000.43.7163.5573.4453.444
10000.43.7163.5573.4453.444
Sheet2
Sheet2
3.663.763.813.743.723.82
3.453.613.573.663.533.58
3.383.543.53.543.463.46
3.363.523.483.513.433.43
10 generations
20 generations
30 generations
50 generations
80 generations
100 genreations
Sequential->Descending->Smallest->Genetic Algorithm
Average passes
100Rounds(16*16), MP=0.1
Sheet3
3.73.73.733.633.783.72
3.533.593.533.533.553.52
3.433.513.453.383.53.43
3.413.53.443.373.493.42
10 generations
20 generations
30 generations
50 generations
80 generations
100 generations
Sequential->Descending->Smallest->Genetic Algorithm
Average Passes
100 Rounds(16*16), MP=0.001
3.7033.553.4433.441
3.7033.553.4433.436
3.7033.553.4433.43
3.7033.553.4433.428
3.7033.553.4433.428
3.7033.553.4433.425
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of Various Generations(MP=0.1,Rnds=1000)
3.7093.5373.4443.444
3.7093.5373.4443.444
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.442
3.7183.5413.443.414
3.7183.5413.443.402
3.7183.5413.443.391
3.7183.5413.443.389
3.7183.5413.443.387
3.7183.5413.443.387
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.1,Rnds=1000)
3.7123.5423.4443.406
3.7123.5423.4443.396
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.2,Rnds=1000)
3.7093.5373.4443.444
3.7093.5373.4443.444
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.442
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comprisons of various Generations(MP=0.001,Rnds=1000)
AnalysisBest Mutation Probability: 0.1---0.3 Generations:100---300Population size:4--8Crossover Probability used: 100%In this research, maximum colors reduced by GA: 2
Maximum passes reduced by GA in this research
Sheet1
nodesRoundsGensMu ProbCrsOverPopuSeqAlgoDescendsmallestGeneticTimediff(S-G)diff(D-G)
8100100.5Single22.722.582.562.5600.020.16
10010000.5Single22.732.592.572.5700.020.16
820010000.5Single22.6652.512.4852.48!!0.0050.030.185
0
100100.001Single42.782.682.672.671 sec00.010.11
1001000.001Single42.772.652.632.638 sec00.020.14
10010000.001Single42.782.612.582.5874 sec00.030.2
20010000.001Single42.72.562.5252.525147 sec00.0350.175
0
0
16100100.5Single23.753.493.423.4200.070.33
1001000.5Single23.663.523.463.45!!0.010.070.21
1001000.5While23.693.543.453.42!!0.030.120.27
000
(#4)100100.01While43.653.453.363.350.010.10.3
(#8)100100.001While43.73.533.433.410.020.120.29
(#9)100200.01While43.823.523.453.440.010.080.38
(#9)100200.001While43.73.593.513.50.010.090.2
(#4)100300.01While43.773.543.413.40.010.140.37
(#4)100300.001While43.733.533.453.440.010.090.29
(#7)100500.01While43.63.523.373.360.010.160.24
(#20)100500.001While43.633.533.383.370.010.160.26
(#9)100800.01While43.693.513.393.380.010.130.31
(#4)100800.001While43.783.553.53.490.010.060.29
(#5)1001000.01While43.673.473.383.370.010.10.3
(#2)1001000.001While43.723.523.433.420.010.10.3
(#4)1001000.001Double43.743.493.383.3722 sec0.010.120.37
(#1)100010000.001Double43.6983.5433.4323.4339m24s0.0020.1130.268
(#1)100010000.001Single43.6943.5183.4163.41259m28s0.0040.1060.282
0
321001000.5While24.524.354.274.2700.080.25
10010000.5While254.44.44.3>13hours0.10.10.7
0
20200.001While4No improvement whithin 100 times0
(#38)20500.001While44.654.44.34.2519 sec0.050.150.4
(#11)201000.001While44.654.44.254.236 sec0.050.20.45
(#24)50100.001While44.464.264.124.110 sec0.020.160.36
(#23)50200.001While44.544.284.264.2420 sec0.020.040.3
(#12)50500.001While44.64.384.344.3247 sec0.020.060.28
(#15)501000.001While44.584.34.264.2495 sec0.020.060.34
(#5)10050.001While44.54.244.24.1910 sec0.010.050.31
(#2)100100.001While44.544.284.214.1919 sec0.020.090.35
(#1)100200.001While44.54.244.194.1837 sec0.010.060.32
(#6)100500.001While44.534.274.174.1691 sec0.010.110.37
(#3)1001000.001While44.474.264.194.18197 sec0.010.080.29
0
641001000.5While2more than 24 hours0
0
(#4)100100.001While45.325.055.025.011m29s0.010.040.31
(#5)100200.001While45.295.044.994.982m57s0.010.060.31
(#10)100500.001While45.354.984.964.957m39s0.010.030.4
(#1)1001000.001While45.285.065.035.0214m27s0.010.040.26
0
1281001000.001While46.195.875.855.8461m38s0.010.030.35
nodesRoundsGensMu ProbCrsOverPopuSeqAlgoDescendsmallestGeneticTime
8100100.5Single22.722.582.562.56
810010000.5Single22.732.592.572.57
820010000.5Single22.6652.512.4852.488 hours
16100100.5Single23.753.493.423.42
161001000.5Single23.663.523.463.454 hours
321001000.5Single24.524.354.274.27
3210010000.5Single254.44.44.313hours
641001000.5Single2more than 24 hours, and less valid offspring
nodesRoundsGensMu ProbCrsOverPopuSeqAlgoDescendsmallestGeneticTime
8100100.001Single42.782.682.672.671 sec
81001000.001Single42.772.652.632.638 sec
810010000.001Single42.782.612.582.5874 sec
820010000.001Single42.72.562.5252.525147 sec
161001000.001Single43.723.523.433.4238sec
16100010000.001Single43.6943.5183.4163.41259m28s
32501000.001Single44.584.34.264.2495 sec
321001000.001Single44.474.264.194.18197 sec
64100500.001Single45.354.984.964.957m39s
641001000.001Single45.285.065.035.0214m27s
1281001000.001Single46.195.875.855.8461m38s
nodesSequential - GeneticDegree descending - GeneticSmallest - Genetic
8222
16222
32221
64211
128211
256211
Sheet2
Sheet3
Single vs. Double Crossover
Chart3
3.6953.684
3.5283.528
3.4053.42
3.4023.416
Double Crossover
Single Crossover
Seq->Des->Smallest->Genetic
Average Passes
1000 Rounds,1000 Gens, MP=0.001(16*16 Network)
Chart4
3.743.72
3.493.52
3.383.43
3.373.42
Double Crossover
Single Crossover
Seq->Des->Sma->Genetic
Average Passes
100Rounds,100Gens(16*16),MP=0.001
Chart5
2.772.652.632.63
3.723.523.433.415
4.474.264.194.175
5.285.065.035.01
Sequential Algorithm
Degree Descending Algorithm
Smallest of Four Algorithm
Genetic Algorithm
Number of Nodes(8,16,32,64...)
Average Passes
100 Rnds, 100 Gens, MP=0.001
Chart7
3.6953.684
3.5283.528
3.4053.42
3.4023.416
Double Crossover
Single Crossover
Seq->Des->Sma->Genetic
Average Passes
1000Rnds,1000Gens(16*16),MP=0.001
Sheet1
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.013.653.453.363.35
100200.013.823.523.453.44
100300.013.773.543.413.4
100500.013.63.523.373.36
100800.013.693.613.393.38
1001000.013.673.473.383.37
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.0013.73.533.433.41
100200.0013.73.593.513.5
100300.0013.733.533.453.44
100500.0013.633.533.383.37
100800.0013.783.553.53.49
1001000.0013.723.523.433.42
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.13.663.453.383.36
100200.13.763.613.543.52
100300.13.813.573.53.48
100500.13.743.663.543.51
100800.13.723.533.463.43
1001000.13.823.583.463.43
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGeneticdiff:Sm-G
100100.13.733.53.393.360.03
100200.13.693.543.483.470.01
100300.13.73.53.413.350.06
100500.13.753.553.463.370.09
100800.13.783,63.523.470.05
1001000.13.733.563.493.460.03
3.6953.5283.4053.402
3.6843.5283.423.416
3.743.493.383.37
3.723.523.433.42
2.773.724.475.28
2.653.524.265.06
2.633.434.195.03
2.633.4154.1755.01
mp=0.282.842.752.72.652.84
163.773.613.493.453.77
324.514.194.164.144.51
64(0.1)5.295.075.055.045.29
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.013.7033.553.4433.44150
10000.013.7033.553.4433.436100
10000.013.7033.553.4433.43150
10000.013.7033.553.4433.428200
10000.013.7033.553.4433.428250
10000.013.7033.553.4433.425300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.13.7183.5413.443.41450
10000.13.7183.5413.443.402100
10000.13.7183.5413.443.391150
10000.13.7183.5413.443.389200
10000.13.7183.5413.443.387250
10000.13.7183.5413.443.387300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.0013.7093.5373.4443.44450
10000.0013.7093.5373.4443.444100
10000.0013.7093.5373.4443.443150
10000.0013.7093.5373.4443.443200
10000.0013.7093.5373.4443.443250
10000.0013.7093.5373.4443.442300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
0.13.7123.5423.4443.406
3.7123.5423.4443.396
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.23.7343.5693.4553.41550
10000.23.7343.5693.4553.401100
10000.23.7343.5693.4553.395150
10000.23.7343.5693.4553.394200
10000.23.7343.5693.4553.394250
10000.23.7343.5693.4553.394300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.33.6943.5513.4333.40350
10000.33.6943.5513.4333.389100
10000.33.6943.5513.4333.384150
10000.33.6943.5513.4333.382200
10000.33.6943.5513.4333.38250
10000.33.6943.5513.4333.38300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.43.7163.5573.4453.44550
10000.43.7163.5573.4453.444100
10000.43.7163.5573.4453.444150
10000.43.7163.5573.4453.444200
10000.43.7163.5573.4453.444250
10000.43.7163.5573.4453.444300
RoundsMPGensNetwork sizeSeqDescendSmallestGeneticGens
10000.110082.7342.5932.5732.562
163.7013.5483.4363.394
324.4934.2534.24.179
645.3435.0575.0335.023
Sheet1
Double Crossover
Single Crossover
Seq->Des-Smallest->Genetic
Average Passes
100 Rounds, 100 Generations, MP=0.001(16*16 Network)
Sheet2
Double Crossover
Single Crossover
Seq->Des->Smallest->Genetic
Average Passes
1000 Rounds,1000 Gens, MP=0.001(16*16 Network)
Sheet3
Sheet3
3.7033.553.4433.441
3.7033.553.4433.436
3.7033.553.4433.43
3.7033.553.4433.428
3.7033.553.4433.428
3.7033.553.4433.425
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of Various Generations(MP=0.1,Rnds=1000)
3.7093.5373.4443.444
3.7093.5373.4443.444
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.442
3.7183.5413.443.414
3.7183.5413.443.402
3.7183.5413.443.391
3.7183.5413.443.389
3.7183.5413.443.387
3.7183.5413.443.387
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.1,Rnds=1000)
3.7123.5423.4443.406
3.7123.5423.4443.396
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.2,Rnds=1000)
3.7093.5373.4443.444
3.7093.5373.4443.444
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.442
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comprisons of various Generations(MP=0.001,Rnds=1000)
3.6943.5513.4333.403
3.6943.5513.4333.389
3.6943.5513.4333.384
3.6943.5513.4333.382
3.6943.5513.4333.38
3.6943.5513.4333.38
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.3,Rnds=1000)
3.7163.5573.4453.445
3.7163.5573.4453.444
3.7163.5573.4453.444
3.7163.5573.4453.444
3.7163.5573.4453.444
3.7163.5573.4453.444
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.4,Rnds=1000)
Comparisons of 5 algorithms
Chart3
2.712.822.932.652.58
3.743.713.833.553.44
4.464.544.644.224.15
5.375.395.7955.05
6.176.126.715.875.74
Sequential Algorithm
Sequetial down Algorithm
Degree-ascending Algorithm
Degree-descending Algorithm
Genetic Algorithm
Number of stages of OMIN
Average Passes
Performances of 5 algorithms(MP=0.1,Gens=100)
Sheet1
2.712.822.932.652.58
3.743.713.833.553.44
4.464.544.644.224.15
5.375.395.7955.05
6.176.126.715.875.74
Sheet1
Sequential Algorithm
Sequetial down Algorithm
Degree-ascending Algorithm
Degree-descending Algorithm
Genetic Algorithm
Number of stages of OMIN
Average Passes
Performances of 5 algorithms(MP=0.1,Gens=100)
Sheet2
Sheet3
MBD001D29B4.xls
Chart3
3.6953.684
3.5283.528
3.4053.42
3.4023.416
Double Crossover
Single Crossover
Seq->Des->Smallest->Genetic
Average Passes
1000 Rounds,1000 Gens, MP=0.001(16*16 Network)
Chart4
3.743.72
3.493.52
3.383.43
3.373.42
Double Crossover
Single Crossover
Seq->Des->Sma->Genetic
Average Passes
100Rounds,100Gens(16*16),MP=0.001
Chart5
2.772.652.632.63
3.723.523.433.415
4.474.264.194.175
5.285.065.035.01
Sequential Algorithm
Degree Descending Algorithm
Smallest of Four Algorithm
Genetic Algorithm
Number of Nodes(8,16,32,64...)
Average Passes
100 Rnds, 100 Gens, MP=0.001
Chart7
3.6953.684
3.5283.528
3.4053.42
3.4023.416
Double Crossover
Single Crossover
Seq->Des->Sma->Genetic
Average Passes
1000Rnds,1000Gens(16*16),MP=0.001
Sheet1
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.013.653.453.363.35
100200.013.823.523.453.44
100300.013.773.543.413.4
100500.013.63.523.373.36
100800.013.693.613.393.38
1001000.013.673.473.383.37
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.0013.73.533.433.41
100200.0013.73.593.513.5
100300.0013.733.533.453.44
100500.0013.633.533.383.37
100800.0013.783.553.53.49
1001000.0013.723.523.433.42
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.13.663.453.383.36
100200.13.763.613.543.52
100300.13.813.573.53.48
100500.13.743.663.543.51
100800.13.723.533.463.43
1001000.13.823.583.463.43
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGeneticdiff:Sm-G
100100.13.733.53.393.360.03
100200.13.693.543.483.470.01
100300.13.73.53.413.350.06
100500.13.753.553.463.370.09
100800.13.783,63.523.470.05
1001000.13.733.563.493.460.03
3.6953.5283.4053.402
3.6843.5283.423.416
3.743.493.383.37
3.723.523.433.42
2.773.724.475.28
2.653.524.265.06
2.633.434.195.03
2.633.4154.1755.01
mp=0.282.842.752.72.652.84
163.773.613.493.453.77
324.514.194.164.144.51
64(0.1)5.295.075.055.045.29
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.013.7033.553.4433.44150
10000.013.7033.553.4433.436100
10000.013.7033.553.4433.43150
10000.013.7033.553.4433.428200
10000.013.7033.553.4433.428250
10000.013.7033.553.4433.425300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.13.7183.5413.443.41450
10000.13.7183.5413.443.402100
10000.13.7183.5413.443.391150
10000.13.7183.5413.443.389200
10000.13.7183.5413.443.387250
10000.13.7183.5413.443.387300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.0013.7093.5373.4443.44450
10000.0013.7093.5373.4443.444100
10000.0013.7093.5373.4443.443150
10000.0013.7093.5373.4443.443200
10000.0013.7093.5373.4443.443250
10000.0013.7093.5373.4443.442300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
0.13.7123.5423.4443.406
3.7123.5423.4443.396
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.23.7343.5693.4553.41550
10000.23.7343.5693.4553.401100
10000.23.7343.5693.4553.395150
10000.23.7343.5693.4553.394200
10000.23.7343.5693.4553.394250
10000.23.7343.5693.4553.394300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.33.6943.5513.4333.40350
10000.33.6943.5513.4333.389100
10000.33.6943.5513.4333.384150
10000.33.6943.5513.4333.382200
10000.33.6943.5513.4333.38250
10000.33.6943.5513.4333.38300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.43.7163.5573.4453.44550
10000.43.7163.5573.4453.444100
10000.43.7163.5573.4453.444150
10000.43.7163.5573.4453.444200
10000.43.7163.5573.4453.444250
10000.43.7163.5573.4453.444300
RoundsMPGensNetwork sizeSeqDescendSmallestGeneticGens
10000.110082.7342.5932.5732.562
163.7013.5483.4363.394
324.4934.2534.24.179
645.3435.0575.0335.023
Sheet1
Double Crossover
Single Crossover
Seq->Des-Smallest->Genetic
Average Passes
100 Rounds, 100 Generations, MP=0.001(16*16 Network)
Sheet2
Double Crossover
Single Crossover
Seq->Des->Smallest->Genetic
Average Passes
1000 Rounds,1000 Gens, MP=0.001(16*16 Network)
Sheet3
Sheet3
3.7033.553.4433.441
3.7033.553.4433.436
3.7033.553.4433.43
3.7033.553.4433.428
3.7033.553.4433.428
3.7033.553.4433.425
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of Various Generations(MP=0.1,Rnds=1000)
3.7093.5373.4443.444
3.7093.5373.4443.444
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.442
3.7183.5413.443.414
3.7183.5413.443.402
3.7183.5413.443.391
3.7183.5413.443.389
3.7183.5413.443.387
3.7183.5413.443.387
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.1,Rnds=1000)
3.7123.5423.4443.406
3.7123.5423.4443.396
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.2,Rnds=1000)
3.7093.5373.4443.444
3.7093.5373.4443.444
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.442
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comprisons of various Generations(MP=0.001,Rnds=1000)
3.6943.5513.4333.403
3.6943.5513.4333.389
3.6943.5513.4333.384
3.6943.5513.4333.382
3.6943.5513.4333.38
3.6943.5513.4333.38
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.3,Rnds=1000)
3.7163.5573.4453.445
3.7163.5573.4453.444
3.7163.5573.4453.444
3.7163.5573.4453.444
3.7163.5573.4453.444
3.7163.5573.4453.444
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.4,Rnds=1000)
MBD001D304B.xls
Chart1
3.743.72
3.493.52
3.383.43
3.373.42
Double Crossover
Single Crossover
Seq->Des-Smallest->Genetic
Average Passes
100 Rounds, 100 Generations, MP=0.001(16*16 Network)
Chart4
3.743.72
3.493.52
3.383.43
3.373.42
Double Crossover
Single Crossover
Seq->Des->Sma->Genetic
Average Passes
100Rounds,100Gens(16*16),MP=0.001
Chart5
2.772.652.632.63
3.723.523.433.415
4.474.264.194.175
5.285.065.035.01
Sequential Algorithm
Degree Descending Algorithm
Smallest of Four Algorithm
Genetic Algorithm
Number of Nodes(8,16,32,64...)
Average Passes
100 Rnds, 100 Gens, MP=0.001
Chart7
3.6953.684
3.5283.528
3.4053.42
3.4023.416
Double Crossover
Single Crossover
Seq->Des->Sma->Genetic
Average Passes
1000Rnds,1000Gens(16*16),MP=0.001
Sheet1
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.013.653.453.363.35
100200.013.823.523.453.44
100300.013.773.543.413.4
100500.013.63.523.373.36
100800.013.693.613.393.38
1001000.013.673.473.383.37
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.0013.73.533.433.41
100200.0013.73.593.513.5
100300.0013.733.533.453.44
100500.0013.633.533.383.37
100800.0013.783.553.53.49
1001000.0013.723.523.433.42
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGenetic
100100.13.663.453.383.36
100200.13.763.613.543.52
100300.13.813.573.53.48
100500.13.743.663.543.51
100800.13.723.533.463.43
1001000.13.823.583.463.43
roundsgenerationsMut PorbSeq AlgoDeg-DescendSmallestGeneticdiff:Sm-G
100100.13.733.53.393.360.03
100200.13.693.543.483.470.01
100300.13.73.53.413.350.06
100500.13.753.553.463.370.09
100800.13.783,63.523.470.05
1001000.13.733.563.493.460.03
3.6953.5283.4053.402
3.6843.5283.423.416
3.743.493.383.37
3.723.523.433.42
2.773.724.475.28
2.653.524.265.06
2.633.434.195.03
2.633.4154.1755.01
mp=0.282.842.752.72.652.84
163.773.613.493.453.77
324.514.194.164.144.51
64(0.1)5.295.075.055.045.29
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.013.7033.553.4433.44150
10000.013.7033.553.4433.436100
10000.013.7033.553.4433.43150
10000.013.7033.553.4433.428200
10000.013.7033.553.4433.428250
10000.013.7033.553.4433.425300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.13.7183.5413.443.41450
10000.13.7183.5413.443.402100
10000.13.7183.5413.443.391150
10000.13.7183.5413.443.389200
10000.13.7183.5413.443.387250
10000.13.7183.5413.443.387300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.0013.7093.5373.4443.44450
10000.0013.7093.5373.4443.444100
10000.0013.7093.5373.4443.443150
10000.0013.7093.5373.4443.443200
10000.0013.7093.5373.4443.443250
10000.0013.7093.5373.4443.442300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
0.13.7123.5423.4443.406
3.7123.5423.4443.396
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.23.7343.5693.4553.41550
10000.23.7343.5693.4553.401100
10000.23.7343.5693.4553.395150
10000.23.7343.5693.4553.394200
10000.23.7343.5693.4553.394250
10000.23.7343.5693.4553.394300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.33.6943.5513.4333.40350
10000.33.6943.5513.4333.389100
10000.33.6943.5513.4333.384150
10000.33.6943.5513.4333.382200
10000.33.6943.5513.4333.38250
10000.33.6943.5513.4333.38300
RoundsMut ProbSeq AlgoDeg-DescendSmallest AlgoGeneticGenerations
10000.43.7163.5573.4453.44550
10000.43.7163.5573.4453.444100
10000.43.7163.5573.4453.444150
10000.43.7163.5573.4453.444200
10000.43.7163.5573.4453.444250
10000.43.7163.5573.4453.444300
RoundsMPGensNetwork sizeSeqDescendSmallestGeneticGens
10000.110082.7342.5932.5732.562
163.7013.5483.4363.394
324.4934.2534.24.179
645.3435.0575.0335.023
Sheet1
Double Crossover
Single Crossover
Seq->Des-Smallest->Genetic
Average Passes
100 Rounds, 100 Generations, MP=0.001(16*16 Network)
Sheet2
Sheet2
3.7033.553.4433.441
3.7033.553.4433.436
3.7033.553.4433.43
3.7033.553.4433.428
3.7033.553.4433.428
3.7033.553.4433.425
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of Various Generations(MP=0.1,Rnds=1000)
Sheet3
3.7093.5373.4443.444
3.7093.5373.4443.444
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.442
3.7183.5413.443.414
3.7183.5413.443.402
3.7183.5413.443.391
3.7183.5413.443.389
3.7183.5413.443.387
3.7183.5413.443.387
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.1,Rnds=1000)
3.7123.5423.4443.406
3.7123.5423.4443.396
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
3.7123.5423.4443.392
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.2,Rnds=1000)
3.7093.5373.4443.444
3.7093.5373.4443.444
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.443
3.7093.5373.4443.442
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comprisons of various Generations(MP=0.001,Rnds=1000)
3.6943.5513.4333.403
3.6943.5513.4333.389
3.6943.5513.4333.384
3.6943.5513.4333.382
3.6943.5513.4333.38
3.6943.5513.4333.38
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.3,Rnds=1000)
3.7163.5573.4453.445
3.7163.5573.4453.444
3.7163.5573.4453.444
3.7163.5573.4453.444
3.7163.5573.4453.444
3.7163.5573.4453.444
Sequential Algorithm
Degree-descending Algorithm
Smallest Algorithm
Genetic Algorithm
N*50 Generations
Average Passes
Comparisons of various Generations(MP=0.4,Rnds=1000)
Java Applet
Sequential Algo.(128*128)
Sequential Down Algo.
Degree-ascending Algo.
Degree-descending Algo.
Genetic Algorithm
Comparisons of 5 algorithms
Conclusion and Future workGenetic Algorithm can be used as a optimizing toolDisadvantage:time consumingPerform GA in parallelOther complicated GA techniques to improve the results