The Evolutionary Pre-Processor (version 3.0) Batch Run Report |
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Batch Details
Batch run name | vehicle_1 |
Description | Vehicle 1 on Nyquist |
This report file | final/vehicle_1/vehicle_1_report.html |
data file | /home/cssip/jsherrah/EPrep3/data/vehicle_1.dat |
Time of completion | Mon Jun 1 01:39:37 1998 |
Duration of Batch | 11 hours, 20 minutes, 3 seconds |
Random Seed | 18446744072244177578 (from clock) |
Average generations per run | 26.90 |
Average failed feature creations per run | 2711.60 |
Average fitness evalutions per run | 62820.30 |
Test Set Improvement | -0.94 % |
Data Partition
Class | Training | Validation | Test | Total |
0 | 107 | 53 | 52 | 212 |
1 | 108 | 55 | 54 | 217 |
2 | 109 | 54 | 55 | 218 |
3 | 99 | 49 | 51 | 199 |
Total | 423 | 211 | 212 | 846 |
Summary of Results
Original Classification Errors (%)
Classifier | Training | Validation | Test |
Parallelepiped(PPD) | 56.74 | 63.03 | 62.26 |
Maximum Likelihood(ML) | 6.15 | 16.11 | 12.74 |
Min. Distance to Means(MDTM) | 60.76 | 59.24 | 60.38 |
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1 | 7.09 | 15.17 | 12.74 | 0.264 | 16 | 17 | 96 | ML |
2 | 9.93 | 15.17 | 13.21 | 0.258 | 13 | 12 | 16 | ML |
3 | 6.62 | 13.27 | 15.09 | 0.055 | 15 | 17 | 66 | ML |
4 | 7.57 | 15.17 | 13.21 | 0.184 | 15 | 15 | 15 | ML |
5 | 9.22 | 13.27 | 13.68 | 0.174 | 13 | 13 | 13 | ML |
6 | 6.38 | 14.69 | 13.21 | 0.184 | 14 | 14 | 14 | ML |
7 | 6.15 | 13.74 | 14.15 | 0.084 | 17 | 18 | 146 | ML |
8 | 7.80 | 13.74 | 13.21 | 0.221 | 13 | 13 | 13 | ML |
9 | 9.22 | 13.27 | 13.68 | 0.174 | 13 | 13 | 15 | ML |
10 | 7.09 | 15.17 | 15.57 | 0.064 | 14 | 14 | 14 | ML |
Ave. | 7.71 (1.25 ) | 14.27 (0.83 ) | 13.77 (0.86 ) | 0.17 (0.07 ) | 14.30 (1.35 ) | 14.60 (1.96 ) | 40.80 (44.36 ) | ML |
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Total | ||||
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Ground Truth |
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39 | 12 | 0 | 1 | 52 |
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12 | 41 | 0 | 1 | 54 | |
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0 | 0 | 53 | 2 | 55 | |
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0 | 0 | 1 | 50 | 51 | |
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51 | 53 | 54 | 54 | 212 |
Average Operator Probabilities
Operator | Average Probability |
Delete-Feature Mutation | 0.095 |
Add-Feature Mutation | 0.104 |
Hoist Mutation | 0.107 |
Truncate Mutation | 0.101 |
Swap Mutation | 0.100 |
One-Symbol Mutation | 0.093 |
All-Nodes Mutation | 0.104 |
One-Node Mutation | 0.107 |
Grow Mutation | 0.096 |
High-Level Crossover | 0.092 |
Number of Run Terminations attributed to each Criterion
Termination Criterion | Number of Terminations |
TP Criterion | 0 |
GL Criterion | 9 |
Max. Generations | 1 |
Client Abort | 0 |
Zero Validation Error | 0 |
Total | 10 |
Related Data Files
Description | Filename |
vehicle_1_gen.m | Matlab plot generation function |
vehicle_1_bogf_ave.{eps,gif} | Best-of-generation Fitness, averaged over 10 runs |
vehicle_1_bogv_ave.{eps,gif} | Best-of-generation Validation Set Error, averaged over 10 runs |
vehicle_1_avef_ave.{eps,gif} | Average fitness, averaged over 10 runs |
vehicle_1_stdf_ave.{eps,gif} | Standard deviation of fitness, averaged over 10 runs |
vehicle_1_nftr_ave.{eps,gif} | Average number of features per individual, averaged over 10 runs |
vehicle_1_nnode_ave.{eps,gif} | Average number of nodes per individual, averaged over 10 runs |
vehicle_1_nint_ave.{eps,gif} | Average number of introns per individual, averaged over 10 runs |
vehicle_1_ntrl_ave.{eps,gif} | Average number of RAT trials per individual, averaged over 10 runs |
vehicle_1_optimp_ave.{eps,gif} | Average improvement in fitness due to optimisation, averaged over 10 runs |
vehicle_1_opprob_ave.{eps,gif} | Average probability of each genetic operator, averaged over 10 runs |
vehicle_1_run_x.dat | Binary data file containing results of run x (read by Matlab functions) |
vehicle_1_bor_x.prep x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-run individual for run x |
vehicle_1_run_x.corr x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Feature correlation file for run x |
vehicle_1_tst_bor_x.pred x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Test set predictions for best-of-run individual from run x |
vehicle_1_bogf_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-generation Fitness for run x |
vehicle_1_bogv_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-generation Validation Set Error for run x |
vehicle_1_avef_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average fitness for run x |
vehicle_1_stdf_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Standard deviation of fitness for run x |
vehicle_1_nftr_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average number of features per individual for run x |
vehicle_1_nnode_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average number of nodes per individual for run x |
vehicle_1_nint_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average number of introns per individual for run x |
vehicle_1_ntrl_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average number of RAT trials per individual for run x |
vehicle_1_optimp_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average improvement in fitness due to optimisation for run x |
vehicle_1_opprob_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average probability of each genetic operator for run x |