The Evolutionary Pre-Processor (version 3.0) Batch Run Report |
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Batch Details
Batch run name | cmc_2 |
Description | cmc 2 on Nyquist |
This report file | postfinal/cmc_2/cmc_2_report.html |
data file | /home/cssip/jsherrah/EPrep3/data/cmc_2.dat |
Time of completion | Sun Jun 21 14:45:20 1998 |
Duration of Batch | 44 hours, 57 minutes, 29 seconds |
Random Seed | 673744928 (from clock) |
Average generations per run | 201.00 |
Average failed feature creations per run | 3425.40 |
Average fitness evalutions per run | 886055.90 |
Test Set Improvement | 18.97 % |
Data Partition
Class | Training | Validation | Test | Total |
0 | 314 | 158 | 157 | 629 |
1 | 167 | 83 | 83 | 333 |
2 | 255 | 127 | 129 | 511 |
Total | 736 | 368 | 369 | 1473 |
Summary of Results
Original Classification Errors (%)
Classifier | Training | Validation | Test |
Min. Distance to Means(MDTM) | 62.23 | 57.07 | 63.96 |
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1 | 44.57 | 42.66 | 41.73 | 0.999 | 6 | 9 | 159 | MDTM |
2 | 44.16 | 42.12 | 47.15 | 1.000 | 8 | 8 | 59 | MDTM |
3 | 44.02 | 41.30 | 47.97 | 0.956 | 3 | 5 | 26 | MDTM |
4 | 47.96 | 42.12 | 47.70 | 0.984 | 4 | 9 | 80 | MDTM |
5 | 43.89 | 38.32 | 44.99 | 0.999 | 3 | 8 | 182 | MDTM |
6 | 46.47 | 41.58 | 46.34 | 1.000 | 6 | 8 | 85 | MDTM |
7 | 45.38 | 41.58 | 43.90 | 1.000 | 5 | 9 | 80 | MDTM |
8 | 45.11 | 42.12 | 47.15 | 0.997 | 8 | 6 | 71 | MDTM |
9 | 47.15 | 42.66 | 46.34 | 1.000 | 5 | 7 | 52 | MDTM |
10 | 42.80 | 42.12 | 45.26 | 0.970 | 7 | 9 | 114 | MDTM |
Ave. | 45.15 (1.53 ) | 41.66 (1.19 ) | 45.85 (1.83 ) | 0.99 (0.01 ) | 5.50 (1.75 ) | 7.80 (1.33 ) | 90.80 (45.70 ) | MDTM |
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Total | |||
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Ground Truth |
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97 | 30 | 30 | 157 |
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19 | 41 | 23 | 83 | |
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34 | 30 | 65 | 129 | |
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150 | 101 | 118 | 369 |
Average Operator Probabilities
Operator | Average Probability |
Delete-Feature Mutation | 0.083 |
Add-Feature Mutation | 0.129 |
Hoist Mutation | 0.075 |
Truncate Mutation | 0.106 |
Swap Mutation | 0.099 |
One-Symbol Mutation | 0.104 |
All-Nodes Mutation | 0.072 |
One-Node Mutation | 0.089 |
Grow Mutation | 0.072 |
High-Level Crossover | 0.170 |
Number of Run Terminations attributed to each Criterion
Termination Criterion | Number of Terminations |
TP Criterion | 0 |
GL Criterion | 0 |
Max. Generations | 10 |
Client Abort | 0 |
Zero Validation Error | 0 |
Total | 10 |
Related Data Files
Description | Filename |
cmc_2_gen.m | Matlab plot generation function |
cmc_2_bogf_ave.{eps,gif} | Best-of-generation Fitness, averaged over 10 runs |
cmc_2_bogv_ave.{eps,gif} | Best-of-generation Validation Set Error, averaged over 10 runs |
cmc_2_avef_ave.{eps,gif} | Average fitness, averaged over 10 runs |
cmc_2_stdf_ave.{eps,gif} | Standard deviation of fitness, averaged over 10 runs |
cmc_2_nftr_ave.{eps,gif} | Average number of features per individual, averaged over 10 runs |
cmc_2_nnode_ave.{eps,gif} | Average number of nodes per individual, averaged over 10 runs |
cmc_2_nint_ave.{eps,gif} | Average number of introns per individual, averaged over 10 runs |
cmc_2_ntrl_ave.{eps,gif} | Average number of RAT trials per individual, averaged over 10 runs |
cmc_2_optimp_ave.{eps,gif} | Average improvement in fitness due to optimisation, averaged over 10 runs |
cmc_2_opprob_ave.{eps,gif} | Average probability of each genetic operator, averaged over 10 runs |
cmc_2_run_x.dat | Binary data file containing results of run x (read by Matlab functions) |
cmc_2_bor_x.prep x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-run individual for run x |
cmc_2_run_x.corr x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Feature correlation file for run x |
cmc_2_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 |
cmc_2_bogf_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-generation Fitness for run x |
cmc_2_bogv_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-generation Validation Set Error for run x |
cmc_2_avef_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average fitness for run x |
cmc_2_stdf_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Standard deviation of fitness for run x |
cmc_2_nftr_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average number of features per individual for run x |
cmc_2_nnode_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average number of nodes per individual for run x |
cmc_2_nint_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average number of introns per individual for run x |
cmc_2_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 |
cmc_2_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 |
cmc_2_opprob_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average probability of each genetic operator for run x |