Data Mining Using Grammar Based Genetic Programming and Applications

Cover......Page 1
Series Editor......Page 3
Springer......Page 4
Contents......Page 6
Contributing Authors......Page 8
Preface......Page 14
Foreword......Page 16
1. Theory and Practice: Mind the Gap......Page 18
2. The Accounts of Practitioners......Page 19
3. GP Theory and Analysis......Page 21
4. Achieving Better GP Performance......Page 23
5. How Biology and Computation Can Inform Each Other......Page 26
6. Wrap up: Narrowing the Gap......Page 27
1. Introduction......Page 28
2. Related Work......Page 30
3. Modular GP through Lambda Abstraction......Page 31
4. The PolyGP System......Page 32
5. S&P 500 Index Time Series Data......Page 34
6. Experimental Setup......Page 36
Fitness Function......Page 38
7. Results......Page 39
8. Analysis of GP Trading Rules......Page 40
9. Analysis of Transaction Frequency......Page 42
10. Concluding Remarks......Page 45
References......Page 46
1. INTRODUCTION......Page 48
2. SYNERGY BETWEEN GP AND STATISTICAL MODEL BUILDING......Page 49
2.2 Unique Features of Statistical Model Building Attractive to GP......Page 50
3. METHODOLOGY......Page 51
3.1 Methodology for Designed Data......Page 52
3.2 Methodology for Undesigned Data......Page 56
4. FUTURE RESEARCH......Page 61
5. APPENDIX 1: GLOSSARY OF STATISTICAL TERMS......Page 62
References......Page 64
1. Introduction......Page 66
2. Background......Page 68
3. GA Population Sizing from the Perspective of Competing Building Blocks......Page 69
4. GP Definitions for a Population Sizing Derivation......Page 72
5. GP Population Sizing Model......Page 73
6. Sizing Model Problems......Page 75
Every BB in a tree is expressed......Page 76
Tunable building block expression......Page 77
7. Conclusions......Page 79
Acknowledgments......Page 80
References......Page 81
1. INTRODUCTION......Page 84
2. A HIERARCHICAL VIEW OF STRUCTURE......Page 86
3.1 Theory Concerning Lattice......Page 87
3.2 Practical Implications of Lattice......Page 89
4.1 Theory Concerning Network......Page 90
5. CONTENT......Page 94
5.1 Theory Concerning Content......Page 95
5.2 Practical Implications of Content......Page 97
6. CONCLUSIONS......Page 99
REFERENCES......Page 100
1. INTRODUCTION......Page 104
1.1 The Stock Picking Problems We Faced (a.k.a. Our Growth Market Problem)......Page 105
2. PROJECT DESCRIPTION OVERVIEW......Page 106
2.1 Acceptability Criterion – Does the resulting model agree with our intuition of how the markets work? Does it improve our knowledge?......Page 107
3.1 Factor Models Entering Problem......Page 108
4.1 Fitness Elements as a Proxy for Portfolio Performance......Page 109
4.2 Fitness Function Specification......Page 110
5.1 Program Representation......Page 111
5.2 Time-Series Selection: Avoiding Data Mining/Snooping Concerns......Page 112
5.4 Other Genetic Program Parameters......Page 113
6. GENETIC PROGRAMMING RESULTS 6.1 Simplification and Interpretation of Formulae......Page 115
7. POST GENETIC PROGRAMMING PORTFOLIO SIMULATIONS......Page 116
9. SUMMARY......Page 118
REFERENCES......Page 119
1. Introduction......Page 120
Module Acquisition Strategies......Page 121
3. Run Transferable Libraries......Page 122
4. Mnemosyne......Page 124
5. Initial Results......Page 125
6. Bias in Function Sets......Page 126
Multiplexer......Page 128
7. Subsequent Library Performance......Page 132
8. Debiasing Function Sets......Page 134
9. Conclusions and future work......Page 135
Appendix: The Mnemosyne Algorithm......Page 136
References......Page 137
TOWARD AUTOMATED DESIGN OF INDUSTRIAL-STRENGTH ANALOG CIRCUITS BY MEANS OF GENETIC PROGRAMMING......Page 138
1. INTRODUCTION......Page 139
2. ABILITY OF GENETIC PROGRAMMING TO PROFITABLY EXPLOIT INCREASED COMPUTER POWER......Page 143
3. EXPLOITING GENERAL KNOWLEDGE ABOUT CIRCUITS......Page 146
4. EXPLOITING PROBLEM-SPECIFIC KNOWLEDGE......Page 147
5. IMPROVING TECHNIQUES OF GENETIC PROGRAMMING......Page 149
6. GRAPPLING WITH A MULTI-OBJECTIVE FITNESS MEASURE......Page 151
7. CONCLUSIONS......Page 157
References......Page 158
1. Introduction......Page 160
2. Related Work......Page 161
Bond Graphs......Page 163
Analog Filter Synthesis by Bond Graph and Genetic Programming......Page 164
4. Evolving Robust Analog Filters by QHFC-GP......Page 166
5. Experiments and Results......Page 167
Analog Filters with Different Topologies Have Different Noise Robustness and Fault Tolerance Capability......Page 168
Evolving Robustness to Component Sizing Perturbations......Page 169
Evolving Robustness to Component Failure......Page 171
6. Conclusions and Future Work......Page 172
References......Page 173
1. Introduction......Page 176
2. Background......Page 177
3. Experimental Methods Experiments......Page 179
The GP......Page 180
Even Parity......Page 181
Battleship......Page 182
4. Results......Page 183
5. Discussion and Conclusions......Page 189
References......Page 190
1. Introduction......Page 192
3. The Method......Page 194
Linear GP with Sequence Generators......Page 195
A register machine as an Algorithmic Chemistry......Page 196
Evolution of an Algorithmic Chemistry......Page 197
Measures......Page 198
4. Description of Experiments......Page 199
Classification — Thyroid Problem......Page 200
Fitness......Page 201
Program Length and Connection Entropy......Page 202
Visualization of an Algorithmic Chemistry......Page 203
6. Summary and Outlook......Page 205
References......Page 206
1. Introduction......Page 208
2. CGP Technology......Page 210
ACGP Flowchart and Algorithm......Page 212
Distribution Statistics......Page 213
4. Illustrative Experiments......Page 214
Off–line Experiment......Page 215
Varying Iteration Length and Regrow......Page 218
Varying Population and Sampling Sizes......Page 220
5. Summary......Page 221
References......Page 223
1. Introduction......Page 224
The simulation......Page 225
Evolutionary process......Page 227
Championship rounds......Page 230
3. Genetic programming parameters......Page 231
4. Experimental design......Page 233
5. Hypotheses......Page 234
6. Discussion of results......Page 238
References......Page 240
1. Introduction......Page 242
2. Cartesian Genetic Programming......Page 243
Evolutionary Algorithm......Page 246
3. Docking......Page 247
Implementation......Page 248
Fitness Function......Page 249
Genome Sizes......Page 250
Output Node......Page 251
5. Experiments Comparing NDEA vs. EA......Page 253
Seeded Libraries......Page 254
Best Filter......Page 256
7. Results with Real Data......Page 257
8. Conclusions......Page 258
References......Page 259
1. INTRODUCTION......Page 262
2. BACKGROUND......Page 263
2.1 Post-Processing Analysis......Page 264
3. EXAMPLE STUDIES......Page 266
3.1 Well-behaved Runs......Page 268
3.2 Under-specified Behavior......Page 275
3.3 Unsupervised Results From Supervised Learning......Page 276
4. CONCLUSIONS......Page 278
REFERENCES......Page 279
1. Introduction......Page 280
Manual incident identification......Page 282
Correcting traffic data......Page 284
Traffic data sets......Page 285
Fitness Measure......Page 286
Evolving first-stage detectors......Page 287
Validating first-stage detectors......Page 289
4. Second Detection Stage......Page 290
Evolving second-stage detectors......Page 291
Validating Second-Stage Detectors......Page 292
5. Detection visualization......Page 295
6. Conclusions......Page 297
References......Page 299
1. Introduction......Page 300
Motivations for Symbolic Regression......Page 301
Problems with Symbolic Regression......Page 302
New Variant: Exploit the Pareto Front......Page 303
Model Performance......Page 304
Model Complexity Measures......Page 305
3. Defining Pareto Optimality......Page 306
Pareto Performance Metrics......Page 307
4. Pareto Exploitation: User Selection......Page 308
Algorithm Objectives......Page 309
The ParetoGP Algorithm......Page 310
Practitioner Comments......Page 311
6. ParetoGP Algorithm Performance......Page 312
Major Improvements Over Classical GP......Page 313
Obvious Extensions......Page 314
References......Page 315
1. Introduction......Page 318
2. ST5 Mission Antenna Requirements......Page 319
3. Evolved Antenna Design......Page 320
4. EA Run Setup......Page 325
6. Results Analysis......Page 326
Acknowledgments......Page 328
References......Page 329
Index......Page 334