The Evolutionary Pre-Processor (version 3.0) Batch Run Report |
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Batch Details
Batch run name | monks_2 |
Description | monks 2 on hilbert |
This report file | results/preliminary/monks_2/monks_2_report.html |
data file | /home/cssip/jsherrah/EPrep3/data/monks_2.dat |
Time of completion | Mon Mar 23 12:00:26 1998 |
Duration of Batch | 1 hours, 39 minutes, 58 seconds |
Random Seed | 170009224 (from clock) |
Average generations per run | 14.10 |
Average failed feature creations per run | 16491.10 |
Average fitness evalutions per run | 75167.80 |
Test Set Improvement | 24.77 % |
Data Partition
Class | Training | Validation | Test | Total |
0 | 70 | 35 | 290 | 395 |
1 | 43 | 21 | 142 | 206 |
Total | 113 | 56 | 432 | 601 |
Summary of Results
Original Classification Errors (%)
Classifier | Training | Validation | Test |
Maximum Likelihood(ML) | 21.24 | 21.43 | 25.69 |
Parallelepiped(PPD) | 38.05 | 37.50 | 32.87 |
Min. Distance to Means(MDTM) | 44.25 | 53.57 | 46.53 |
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1 | 2.65 | 5.36 | 6.48 | 1.000 | 5 | 6 | 56 | ML |
2 | 3.54 | 5.36 | 5.56 | 1.000 | 6 | 6 | 83 | ML |
3 | 0.00 | 1.79 | 0.93 | 1.000 | 6 | 6 | 12 | ML |
4 | 3.54 | 3.57 | 5.09 | 1.000 | 5 | 6 | 59 | ML |
5 | 2.65 | 5.36 | 7.41 | 1.000 | 5 | 6 | 13 | ML |
6 | 1.77 | 1.79 | 32.87 | 0.010 | 5 | 6 | 22 | ML |
7 | 5.31 | 10.71 | 11.11 | 1.000 | 6 | 6 | 106 | ML |
8 | 4.42 | 3.57 | 3.70 | 1.000 | 5 | 6 | 40 | ML |
9 | 5.31 | 5.36 | 5.56 | 1.000 | 5 | 6 | 44 | ML |
10 | 0.00 | 0.00 | 0.93 | 1.000 | 5 | 6 | 45 | ML |
Ave. | 2.92 (1.82 ) | 4.29 (2.79 ) | 7.96 (8.77 ) | 0.90 (0.30 ) | 5.30 (0.46 ) | 6.00 (0.00 ) | 48.00 (28.39 ) | ML |
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Total | ||
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Ground Truth |
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286 | 4 | 290 |
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0 | 142 | 142 | |
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286 | 146 | 432 |
Average Operator Probabilities
Operator | Average Probability |
Delete-Feature Mutation | 0.120 |
Add-Feature Mutation | 0.131 |
Hoist Mutation | 0.123 |
One-Symbol Mutation | 0.131 |
All-Nodes Mutation | 0.124 |
One-Node Mutation | 0.124 |
Grow Mutation | 0.123 |
High-Level Crossover | 0.124 |
Number of Run Terminations attributed to each Criterion
Termination Criterion | Number of Terminations |
TP Criterion | 2 |
GL Criterion | 6 |
Max. Generations | 1 |
Client Abort | 0 |
Zero Validation Error | 1 |
Total | 10 |
Related Data Files
Description | Filename |
monks_2_gen.m | Matlab plot generation function |
monks_2_bogf_ave.{eps,gif} | Best-of-generation Fitness, averaged over 10 runs |
monks_2_bogv_ave.{eps,gif} | Best-of-generation Validation Set Error, averaged over 10 runs |
monks_2_avef_ave.{eps,gif} | Average fitness, averaged over 10 runs |
monks_2_stdf_ave.{eps,gif} | Standard deviation of fitness, averaged over 10 runs |
monks_2_nftr_ave.{eps,gif} | Average number of features per individual, averaged over 10 runs |
monks_2_nnode_ave.{eps,gif} | Average number of nodes per individual, averaged over 10 runs |
monks_2_nint_ave.{eps,gif} | Average number of introns per individual, averaged over 10 runs |
monks_2_ntrl_ave.{eps,gif} | Average number of RAT trials per individual, averaged over 10 runs |
monks_2_optimp_ave.{eps,gif} | Average improvement in fitness due to optimisation, averaged over 10 runs |
monks_2_opprob_ave.{eps,gif} | Average probability of each genetic operator, averaged over 10 runs |
monks_2_run_x.dat | Binary data file containing results of run x (read by Matlab functions) |
monks_2_bor_x.prep x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-run individual for run x |
monks_2_run_x.corr x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Feature correlation file for run x |
monks_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 |
monks_2_bogf_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-generation Fitness for run x |
monks_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 |
monks_2_avef_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average fitness for run x |
monks_2_stdf_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Standard deviation of fitness for run x |
monks_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 |
monks_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 |
monks_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 |
monks_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 |
monks_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 |
monks_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 |