Riolo / Soule / Worzel Genetic Programming Theory and Practice IV

1. Auflage 2007
ISBN: 978-0-387-49650-4
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 338 Seiten

Reihe: Genetic and Evolutionary Computation

Genetic Programming Theory and Practice IV was developed from the fourth workshop at the University of Michigan's Center for the Study of Complex Systems. The workshop was convened in May 2006 to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application.

Autoren/Hrsg.

Riolo, Rick

Soule, Terence

Worzel, Bill

Inhaltsverzeichnis

1;Contents;6
2;Contributing Authors;9
3;Preface;13
4;Foreword;15
5;Chapter 1 GENETIC PROGRAMMING: THEORY AND PRACTICE An Introduction to Volume IV;17
5.1;1. Theory and Practice: Crossing a Watershed;17
5.2;2. Common Themes;18
5.3;3. Common Applications;19
5.4;4. Common Hurdles;20
5.5;5. Improving GP: convergence of practice and theory;21
5.6;6. Next Steps;26
6;Chapter 2 GENOME- WIDE GENETIC ANALYSIS USING GENETIC PROGRAMMING: THE CRITICAL NEED FOR EXPERT KNOWLEDGE;27
6.1;1. Introduction;28
6.2;2. Genetic Programming Methods;31
6.3;3. Multifactor Dimensionality Reduction ( MDR) for Attribute Construction;34
6.4;4. Expert Knowledge from T\ ined ReliefF;34
6.5;5. Data Simulation and Analysis;35
6.6;6. Experimental Results;36
6.7;7. Discussion and Conclusion;36
6.8;8. Acknowledgment;41
6.9;References;41
7;Chapter 3 LIFTING THE CURSE OF DIMENSIONALITY;45
7.1;1. Introduction;45
7.2;2. Statement of Problem;46
7.3;3. Methods;46
7.4;4. Concentrating the Data;48
7.5;5. Testing for Generality;51
7.6;6. Discussion;52
7.7;7. Summary;54
7.8;References;55
8;Chapter 4 GENETIC PROGRAMMING FOR CLASSIFYING CANCER DATA AND CONTROLLING HUMANOID ROBOTS;57
8.1;1. Introduction;57
8.2;2. Classification of Gene Expression Data GP- based classification;59
8.3;3. Evolutionary Humanoid Robots;64
8.4;4. Conclusion;70
9;Chapter 5 BOOSTING IMPROVES STABILITY AND ACCURACY OF GENETIC PROGRAMMING IN BIOLOGICAL SEQUENCE CLASSIFICATION;76
9.1;1. Introduction;76
9.2;2. Methods Genetic programming with string queries;77
9.3;3. Results Predicting microRNA targets;81
9.4;4. Discussion;90
9.5;References;91
10;Chapter 6 ORTHOGONAL EVOLUTION OF TEAMS: A CLASS OF ALGORITHMS FOR EVOLVING TEAMS WITH INVERSELY CORRELATED ERRORS;94
10.1;1. Introduction;94
10.2;2. Background;96
10.3;3. Orthogonal Evolution of Teams;99
10.4;4. Experiments;100
10.5;5. Conclusions;107
10.6;References;108
11;Chapter 7 MULTIDIMENSIONAL TAGS, COOPERATIVE POPULATIONS, AND GENETIC PROGRAMMING;111
11.1;1. Cooperation and adaptive complexity;111
11.2;2. Tag- mediated cooperation;112
11.3;3. Multidimensional tags;113
11.4;4. Results;115
11.5;5. Discussion;118
11.6;6. Cooperation and genetic programming;121
11.7;7. Conclusions;123
11.8;Acknowledgments;123
11.9;References;123
12;Chapter 8 COEVOLVING FITNESS MODELS FOR ACCELERATING EVOLUTION AND REDUCING EVALUATIONS;127
12.1;1. Introduction;127
12.2;2. Preliminaries Coevolution;128
12.3;3. Coevolved Fitness Models;131
12.4;4. Training Data Sample Fitness Model;134
12.5;5. Experiments in Symbolic Regression;135
12.6;6. Conclusion;140
12.7;References;141
13;Chapter 9 MULTI- DOMAIN OBSERVATIONS CONCERNING THE USE OF GENETIC PROGRAMMING TO AUTOMATICALLY SYNTHESIZE HUMAN-COMPETITIVE DESIGNS FOR ANALOG CIRCUITS, OPTICAL LENS SYSTEMS, CONTROLLERS, ANTENNAS, MECHANICAL SYSTEMS, AND QUANTUM COMPUTING CIRCUITS;145
13.1;1. Introduction;146
13.2;2. Background on genetic programming and developmental genetic programming;146
13.3;3. Cross- domain common features of human- competitive results produced by genetic programming;147
13.4;4. Amenability of a domain to the application of genetic programming to automated design;154
13.5;5. Genetic or evolutionary search domain- specific specializations;156
13.6;6, Techniques issues observed in multiple domains;156
13.7;7. Conclusions;158
13.8;References;158
14;Chapter 10 ROBUST PARETO FRONT GENETIC PROGRAMMING PARAMETER SELECTION BASED ON DESIGN OF EXPERIMENTS AND INDUSTRIAL DATA;162
14.1;1. Introduction;162
14.2;2. Key Parameters of Pareto Front Genetic Programming for Symbolic Regression;164
14.3;3. A Generic Methodology for Optimal GP Parameter Selection Based On Statistical Design of Experiments;166
14.4;4. Results Experimental Setup;168
14.5;5. Robustness;174
14.6;6. Summary;177
14.7;References;177
15;Chapter 11 PURSUING THE PARETO PARADIGM: TOURNAMENTS, ALGORITHM VARIATIONS AND ORDINAL OPTIMIZATION;180
15.1;1. Introduction;180
15.2;2. Pareto- Aware GP - Variations on the Pareto Theme;181
15.3;3. Tournament Selection Intensity - Single and Multiple Winners with One Objective;185
15.4;4. Tunable Pareto- Aware Selection Strategies;189
15.5;5. Ordinal Optimization and Application to Symbolic Regression;193
15.6;6. Conclusions and Summary;197
15.7;References;197
16;Chapter 12 APPLYING GENETIC PROGRAMMING TO RESERVOIR HISTORY MATCHING PROBLEM;199
16.1;1. Introduction;200
16.2;2. Reservoir History Matching Problem;200
16.3;3. A Genetic Programming Solution;203
16.4;4. A Case Study;204
16.5;5. Concluding Remarks;211
16.6;Acknowledgment;212
16.7;References;212
17;Chapter 13 COMPARISON OF ROBUSTNESS OF THREE FILTER DESIGN STRATEGIES USING GENETIC PROGRAMMING AND BOND GRAPHS;214
17.1;1. Introduction;215
17.2;2. Related Work;216
17.3;3. Analog Filter Synthesis Using Bond Graphs and Genetic Programming Bond Graphs;217
17.4;4. Evolving Robust Analog Filters with Components of Preferred Values Using Bond Graphs and Evolutionary Algorithms;222
17.5;5. Experiments and Results;223
17.6;6. Conclusions and Future Work;227
17.7;References;227
18;Chapter 14 DESIGN OF POSYNOMIAL MODELS FOR MOSFETS: SYMBOLIC REGRESSION USING GENETIC ALGORITHMS;229
18.1;1. Introduction;229
18.2;2. Geometric Programming;233
18.3;3. The MOS Posynomial Modeling Problem;236
18.4;4. Our Genetic Algorithm for MOS Modeling;237
18.5;5. Experiments;240
18.6;6. Summary;243
18.7;7. Future Work;244
18.8;Acknowledgements;244
18.9;References;244
19;Chapter 15 PHASE TRANSITIONS IN GENETIC PROGRAMMING SEARCH;247
19.1;1. Introduction;247
19.2;2. Transitions in GP Search Background;248
19.3;3. Methods and Tools;253
19.4;4. Case Study;255
19.5;5. Discussion;260
19.6;6. Conclusions;262
19.7;7. Acknowledgements;262
19.8;References;263
20;Chapter 16 EFFICIENT MARKOV CHAIN MODEL OF MACHINE CODE PROGRAM EXECUTION AND HALTING;267
20.1;1. Introduction;267
20.2;2. The T7 computer;269
20.3;3. Markov chain model: States;270
20.4;4. Markov chain model: transition probabilities;271
20.5;5. Halting probability;277
20.6;6. Efficient formulations of the model;278
20.7;7. Discussion Implications for Genetic Programming Research;283
20.8;8. Conclusions;286
20.9;References;287
21;Chapter 17 A RE- EXAMINATION OF A REAL WORLD BLOOD FLOW MODELING PROBLEM USING CONTEXT- AWARE CROSSOVER;289
21.1;1. Introduction;289
21.2;2. Background;290
21.3;3. Context- aware crossover for GP;292
21.4;4. Blood Flow Modeling problem;299
21.5;5. Conclusion & Future Work;306
21.6;References;307
22;Chapter 18 LARGE- SCALE, TIME- CONSTRAINED SYMBOLIC REGRESSION;309
22.1;1. Introduction;309
22.2;Summary;323
22.3;Acknowledgments;324
22.4;References;324
23;Chapter 19 STOCK SELECTION: AN INNOVATIVE APPLICATION OF GENETIC PROGRAMMING METHODOLOGY;325
23.1;1. Introduction;326
23.2;2. Financial Data;327
23.3;3. Genetic Programming Methodology Overview;328
23.4;4. Stock Selection Models Variables and Factors;332
23.5;5. Results and Discussion Statistical Test;336
23.6;6. Conclusion;341
23.7;7. Acknowledgements;343
23.8;Notes;343
23.9;References;343
24;Index;345