The Evolutionary Pre-Processor (version 3.0) Batch Run Report |
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Batch Details
Batch run name | vehicle_3 |
Description | Vehicle 3 on Nyquist |
This report file | final/vehicle_3/vehicle_3_report.html |
data file | /home/cssip/jsherrah/EPrep3/data/vehicle_3.dat |
Time of completion | Mon Jun 1 22:05:27 1998 |
Duration of Batch | 12 hours, 53 minutes, 52 seconds |
Random Seed | 18446744072233820835 (from clock) |
Average generations per run | 39.80 |
Average failed feature creations per run | 2725.60 |
Average fitness evalutions per run | 70973.90 |
Test Set Improvement | -2.36 % |
Data Partition
Class | Training | Validation | Test | Total |
0 | 107 | 53 | 52 | 212 |
1 | 108 | 54 | 55 | 217 |
2 | 109 | 54 | 55 | 218 |
3 | 99 | 50 | 50 | 199 |
Total | 423 | 211 | 212 | 846 |
Summary of Results
Original Classification Errors (%)
Classifier | Training | Validation | Test |
Parallelepiped(PPD) | 59.81 | 63.98 | 65.57 |
Maximum Likelihood(ML) | 5.91 | 18.48 | 14.15 |
Min. Distance to Means(MDTM) | 60.76 | 57.82 | 63.21 |
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1 | 10.64 | 14.69 | 17.45 | 0.036 | 14 | 13 | 17 | ML |
2 | 12.29 | 13.74 | 16.51 | 0.090 | 12 | 12 | 12 | ML |
3 | 10.64 | 14.69 | 15.09 | 0.274 | 13 | 13 | 15 | ML |
4 | 10.40 | 15.17 | 15.57 | 0.218 | 13 | 13 | 14 | ML |
5 | 11.82 | 13.74 | 14.15 | 0.248 | 13 | 13 | 13 | ML |
6 | 8.75 | 16.11 | 13.21 | 0.736 | 15 | 15 | 15 | ML |
7 | 12.29 | 13.74 | 16.51 | 0.090 | 12 | 12 | 12 | ML |
8 | 11.35 | 15.17 | 18.87 | 0.039 | 15 | 15 | 34 | ML |
9 | 9.22 | 16.11 | 15.57 | 0.169 | 15 | 18 | 340 | ML |
10 | 6.38 | 15.64 | 14.15 | 0.394 | 18 | 17 | 20 | ML |
Ave. | 10.38 (1.75 ) | 14.88 (0.88 ) | 15.71 (1.61 ) | 0.23 (0.20 ) | 14.00 (1.73 ) | 14.10 (1.97 ) | 49.20 (97.13 ) | ML |
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Total | ||||
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Ground Truth |
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39 | 13 | 0 | 0 | 52 |
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14 | 38 | 0 | 3 | 55 | |
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0 | 0 | 51 | 4 | 55 | |
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1 | 0 | 0 | 49 | 50 | |
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54 | 51 | 51 | 56 | 212 |
Average Operator Probabilities
Operator | Average Probability |
Delete-Feature Mutation | 0.096 |
Add-Feature Mutation | 0.104 |
Hoist Mutation | 0.092 |
Truncate Mutation | 0.102 |
Swap Mutation | 0.102 |
One-Symbol Mutation | 0.092 |
All-Nodes Mutation | 0.095 |
One-Node Mutation | 0.099 |
Grow Mutation | 0.098 |
High-Level Crossover | 0.120 |
Number of Run Terminations attributed to each Criterion
Termination Criterion | Number of Terminations |
TP Criterion | 0 |
GL Criterion | 5 |
Max. Generations | 5 |
Client Abort | 0 |
Zero Validation Error | 0 |
Total | 10 |
Related Data Files
Description | Filename |
vehicle_3_gen.m | Matlab plot generation function |
vehicle_3_bogf_ave.{eps,gif} | Best-of-generation Fitness, averaged over 10 runs |
vehicle_3_bogv_ave.{eps,gif} | Best-of-generation Validation Set Error, averaged over 10 runs |
vehicle_3_avef_ave.{eps,gif} | Average fitness, averaged over 10 runs |
vehicle_3_stdf_ave.{eps,gif} | Standard deviation of fitness, averaged over 10 runs |
vehicle_3_nftr_ave.{eps,gif} | Average number of features per individual, averaged over 10 runs |
vehicle_3_nnode_ave.{eps,gif} | Average number of nodes per individual, averaged over 10 runs |
vehicle_3_nint_ave.{eps,gif} | Average number of introns per individual, averaged over 10 runs |
vehicle_3_ntrl_ave.{eps,gif} | Average number of RAT trials per individual, averaged over 10 runs |
vehicle_3_optimp_ave.{eps,gif} | Average improvement in fitness due to optimisation, averaged over 10 runs |
vehicle_3_opprob_ave.{eps,gif} | Average probability of each genetic operator, averaged over 10 runs |
vehicle_3_run_x.dat | Binary data file containing results of run x (read by Matlab functions) |
vehicle_3_bor_x.prep x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-run individual for run x |
vehicle_3_run_x.corr x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Feature correlation file for run x |
vehicle_3_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_3_bogf_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-generation Fitness for run x |
vehicle_3_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_3_avef_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average fitness for run x |
vehicle_3_stdf_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Standard deviation of fitness for run x |
vehicle_3_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_3_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_3_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_3_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_3_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_3_opprob_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average probability of each genetic operator for run x |