Genetic programming IV : routine human-competitive machine intelligence

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Title:

Series:

Genetic programming series ; GPEM 5

Publication Information:

Norwell, Mass. : Kluwer Academic Publishers, 2003

Physical Description:

1 CD-ROM ; 12 cm

ISBN:

9781402074462

General Note:

Accompanies text with the same title : (QA76.623 G46 2003)

Subject Term:

Genetic programming (Computer science)

Added Author:

Koza, John R.

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010037564	CP 2769	Computer File Accompanies Open Access Book	Compact Disc Accompanies Open Access Book	Searching... Unknown

Genetic Programming IV: Routine Human-Competitive Machine Intelligence presents the application of GP to a wide variety of problems involving automated synthesis of controllers, circuits, antennas, genetic networks, and metabolic pathways. The book describes fifteen instances where GP has created an entity that either infringes or duplicates the functionality of a previously patented 20th-century invention, six instances where it has done the same with respect to post-2000 patented inventions, two instances where GP has created a patentable new invention, and thirteen other human-competitive results. The book additionally establishes:

GP now delivers routine human-competitive machine intelligence

GP is an automated invention machine

GP can create general solutions to problems in the form of parameterized topologies

GP has delivered qualitatively more substantial results in synchrony with the relentless iteration of Moore's Law

Author Notes

John R. Koza received his Ph.D. in Computer Science from the University of Michigan in 1972 under the supervision of John Holland. He was co-founder, Chairman, and CEO of Scientific Games Inc. from 1973 through 1987 where he co-invented the rub-off instant lottery ticket used by state lotteries. He has taught a course on genetic algorithms and genetic programming at Stanford University since 1988. He is currently a consulting professor in the Biomedical Informatics Program in the Department of Medicine at Stanford University and a consulting professor in the Department of Electrical Engineering at Stanford University
Martin A. Keane received a Ph.D. in Mathematics from Northwestern University in 1969. He worked for Applied Devices Corporation until 1972, in the Mathematics Department at General Motors Laboratory until 1976, and was Vice-President for Engineering of Bally Manufacturing Corporation until 1986. He is currently chief scientist of Econometrics Inc. of Chicago and a consultant to various computer-related and gaming-related companies
Matthew J. Streeter received a Masters degree in Computer Science from Worcester Polytechnic Institute in 2001. His Masters thesis applied genetic programming to the automated discovery of numerical approximation formulae for functions and surfaces. His primary research interest is applying genetic programming to problems of real-world scientific or practical importance. He is currently working at Genetic Programming Inc. as a systems programmer and researcher
William Mydlowec is Chief Executive Officer and co-founder of Pharmix Corporation, a venture-funded computational drug discovery company in Silicon Valley. He received his B.S. degree in Computer Science from Stanford University in 1998. He formerly did research at Genetic Programming Inc. with John Koza between 1997 and 2000
Jessen Yu is Director of Engineering of Pharmix Corporation. He received a B.S. degree in Computer Science and Chemistry from Stanford University. He formerly did research at Genetic Programming Inc. with John Koza between 1998 and 2000
Guido Lanza is Vice President of Biology and co-founder of Pharmix Corporation. He received his B.A. degree in 1998 from the University of California at Berkeley from the Department of Molecular and Cell Biology and Department of Integrative Biology. He received an M.Sc. in 1999 in Bioinformatics from the University of Manchester, UK. He formerly did research at Genetic Programming Inc. with John Koza in 2000

1 Introduction	p. 1
1.1 Genetic Programming Now Routinely Delivers High-Return Human-Competitive Machine Intelligence	p. 3
1.1.1 What We Mean by "Human-Competitive"	p. 3
1.1.2 What We Mean by "High-Return"	p. 4
1.1.3 What We Mean by "Routine"	p. 5
1.1.4 What We Mean by "Machine Intelligence"	p. 6
1.1.5 Human-Competitiveness of the Results Produced by Genetic Programming	p. 7
1.1.6 High-Return of the Results Produced by Genetic Programming	p. 10
1.1.7 Routineness of the Results Produced by Genetic Programming	p. 14
1.1.8 Machine Intelligence	p. 15
1.2 Genetic Programming Is an Automated Invention Machine	p. 15
1.2.1 The Illogical Nature of Invention and Evolution	p. 19
1.2.2 Overcoming Established Beliefs	p. 20
1.2.3 Automating the Invention Process	p. 21
1.2.4 Patentable New Inventions Produced by Genetic Programming	p. 22
1.3 Genetic Programming Can Automatically Create Parameterized Topologies	p. 23
1.4 Historical Progression of Qualitatively More Substantial Results Produced by Genetic Programming in Synchrony with Increasing Computer Power	p. 25
2 Background on Genetic Programming	p. 29
2.1 Preparatory Steps of Genetic Programming	p. 29
2.2 Executional Steps of Genetic Programming	p. 31
2.2.1 Example of a Run of Genetic Programming	p. 34
2.3 Advanced Features of Genetic Programming	p. 38
2.3.1 Constrained Syntactic Structures	p. 38
2.3.2 Automatically Defined Functions	p. 39
2.3.3 Automatically Defined Iterations, Automatically Defined Loops, Automatically Defined Recursions, and Automatically Defined Stores	p. 40
2.3.4 Program Architecture and Architecture-Altering Operations	p. 40
2.3.5 Genetic Programming Problem Solver	p. 41
2.3.6 Developmental Genetic Programming	p. 41
2.3.7 Computer Code for Implementing Genetic Programming	p. 42
2.4 Main Points of Four Books on Genetic Programming	p. 42
2.5 Sources of Additional Information about Genetic Programming	p. 45
3 Automatic Synthesis of Controllers	p. 49
3.1 Background on Controllers	p. 50
3.2 Design Considerations for Controllers	p. 52
3.3 Representation of a Controller by a Block Diagram	p. 53
3.4 Possible Techniques for Designing Controllers	p. 58
3.4.1 Search by Hill Climbing	p. 59
3.4.2 Search by Gradient Methods	p. 60
3.4.3 Search by Simulated Annealing	p. 61
3.4.4 Search by Genetic Algorithm and Genetic Programming	p. 61
3.4.5 Previous Work on Controller Synthesis by Means of Genetic and Evolutionary Computation	p. 62
3.4.6 Possible Approaches to Automatic Controller Synthesis Using Genetic Programming	p. 62
3.5 Our Approach to the Automatic Synthesis of the Topology and Tuning of Controllers	p. 64
3.5.1 Repertoire of Functions	p. 65
3.5.2 Repertoire of Terminals	p. 67
3.5.3 Representing the Plant	p. 67
3.5.4 Automatically Defined Functions	p. 68
3.5.5 Three Approaches for Establishing Numerical Parameter Values	p. 69
3.5.6 Constrained Syntactic Structure for Program Trees	p. 73
3.6 Additional Representations of Controllers	p. 73
3.6.1 Representation of a Controller by a Transfer Function	p. 73
3.6.2 Representation of a Controller as a LISP Symbolic Expression	p. 74
3.6.3 Representation of a Controller as a Program Tree	p. 74
3.6.4 Representation of a Controller in Mathematica	p. 75
3.6.5 Representation of a Controller and Plant as a Connection List	p. 75
3.6.6 Representation of a Controller and Plant as a SPICE Netlist	p. 78
3.7 Two-Lag Plant	p. 87
3.7.1 Preparatory Steps for the Two-Lag Plant	p. 88
3.7.2 Results for the Two-Lag Plant	p. 102
3.7.3 Human-Competitiveness of the Result for the Two-Lag Plant Problem	p. 111
3.7.4 AI Ratio for the Two-Lag Plant Problem	p. 112
3.8 Three-Lag Plant	p. 113
3.8.1 Preparatory Steps for the Three-Lag Plant	p. 114
3.8.2 Results for the Three-Lag Plant	p. 115
3.8.3 Routineness for the Three-Lag Plant Problem	p. 119
3.8.4 AI Ratio for the Three-Lag Plant Problem	p. 119
3.9 Three-Lag Plant with a Five-Second Delay	p. 120
3.9.1 Preparatory Steps for the Three-Lag Plant with a Five-Second Delay	p. 120
3.9.2 Results for the Three-Lag Plant with a Five-Second Delay	p. 122
3.9.3 Routineness for the Three-Lag Plant with a Five-Second Delay	p. 123
3.9.4 AI Ratio for the Three-Lag Plant with a Five-Second Delay	p. 123
3.10 Non-Minimal-Phase Plant	p. 125
3.10.1 Preparatory Steps for the Non-Minimal-Phase Plant	p. 125
3.10.2 Results for the Non-Minimal Phase Plant	p. 125
3.10.3 Routineness for the Non-Minimal Phase Plant Problem	p. 127
3.10.4 AI Ratio for the Non-Minimal Phase Plant Problem	p. 127
4 Automatic Synthesis of Circuits	p. 129
4.1 Our Approach to the Automatic Synthesis of the Topology and Sizing of Circuits	p. 131
4.1.1 Evolvable Hardware	p. 134
4.2 Searching for the Impossible	p. 135
4.2.1 Preparatory Steps for the RC Circuit with Gain Greater than Two	p. 138
4.2.2 Results for the RC Circuit with Gain Greater than Two	p. 142
4.2.3 Routineness of the Transition from a Problem of Controller Synthesis to a Problem of Circuit Synthesis	p. 143
4.2.4 AI Ratio for the RC Circuit with Gain Greater than Two	p. 145
4.3 Reinvention of the Philbrick Circuit	p. 147
4.3.1 Preparatory Steps for the Philbrick Circuit	p. 148
4.3.2 Results for the Philbrick Circuit	p. 150
4.3.3 Human-Competitiveness of the Result for the Philbrick Circuit Problem	p. 151
4.3.4 Routineness for the Philbrick Circuit Problem	p. 152
4.3.5 AI Ratio for the Philbrick Circuit Problem	p. 153
4.4 Circuit for the NAND Function	p. 153
4.4.1 Preparatory Steps for the NAND Circuit	p. 154
4.4.2 Results for the NAND Circuit	p. 157
4.4.3 Human-Competitiveness of the Result for the NAND Circuit Problem	p. 158
4.4.4 Routineness for the NAND Circuit Problem	p. 159
4.4.5 AI Ratio for the NAND Circuit Problem	p. 159
4.5 Evolution of a Computer	p. 159
4.5.1 Preparatory Steps for the Arithmetic Logic Unit	p. 160
4.5.2 Results for the Arithmetic Logic Unit	p. 161
4.5.3 Routineness for the Arithmetic Logic Unit Circuit Problem	p. 161
4.5.4 AI Ratio for the Arithmetic Logic Unit Circuit Problem	p. 162
4.6 Square Root Circuit	p. 162
4.6.1 Preparatory Steps for Square Root Circuit	p. 163
4.6.2 Results for Square Root Circuit	p. 165
4.6.3 Routineness for the Square Root Circuit Problem	p. 168
4.6.4 AI Ratio for the Square Root Circuit Problem	p. 168
4.7 Automatic Circuit Synthesis Without an Explicit Test Fixture	p. 168
4.7.1 Preparatory Steps for the Lowpass Filter Problem Without an Explicit Test Fixture	p. 169
4.7.2 Results for the Lowpass Filter Problem without an Explicit Test Fixture	p. 173
4.7.3 Routineness for the Lowpass Filter Problem without an Explicit Test Fixture	p. 174
4.7.4 AI Ratio for the Lowpass Filter Problem without an Explicit Test Fixture	p. 174
5 Automatic Synthesis of Circuit Topology, Sizing, Placement, and Routing	p. 175
5.1 Our Approach to the Automatic Synthesis of Circuit Topology, Sizing, Placement, and Routing	p. 177
5.1.1 Initial Circuit	p. 177
5.1.2 Circuit-Constructing Functions	p. 178
5.1.3 Component-Creating Functions	p. 179
5.1.4 Topology-Modifying Functions	p. 181
5.1.5 Development-Controlling Functions	p. 186
5.1.6 Developmental Process	p. 186
5.2 Lowpass Filter with Layout	p. 186
5.2.1 Preparatory Steps for the Lowpass Filter with Layout	p. 186
5.2.2 Results for the Lowpass Filter with Layout	p. 188
5.2.3 Human-Competitiveness of the Result for the Lowpass Filter Problem with Layout	p. 195
5.2.4 Routineness of the Transition from a Problem of Circuit Synthesis without Layout to a Problem of Circuit Synthesis with Layout	p. 196
5.2.5 AI Ratio for the Lowpass Filter Problem with Layout	p. 197
5.3 60 dB Amplifier with Layout	p. 197
5.3.1 Preparatory Steps for 60 dB Amplifier with Layout	p. 197
5.3.2 Results for 60 dB Amplifier with Layout	p. 199
5.3.3 Routineness for the 60 dB Amplifier Problem with Layout	p. 202
5.3.4 AI Ratio for the 60 dB Amplifier Problem with Layout	p. 203
6 Automatic Synthesis of Antennas	p. 205
6.1 Our Approach to the Automatic Synthesis of the Geometry and Sizing of Antennas	p. 206
6.2 Illustrative Problem of Antenna Synthesis	p. 207
6.3 Repertoire of Functions and Terminals	p. 209
6.3.1 Repertoire of Functions	p. 209
6.3.2 Repertoire of Terminals	p. 210
6.3.3 Example of the Use of the Functions and Terminals	p. 211
6.4 Preparatory Steps for the Antenna Problem	p. 212
6.4.1 Program Architecture	p. 212
6.4.2 Function Set	p. 212
6.4.3 Terminal Set	p. 212
6.4.4 Fitness Measure	p. 212
6.4.5 Control Parameters	p. 216
6.5 Results for the Antenna Problem	p. 216
6.6 Routineness of the Transition from Problems of Synthesizing Controllers, Circuits, and Circuit Layout to a Problem of Synthesizing an Antenna	p. 219
6.7 AI Ratio for the Antenna Problem	p. 220
7 Automatic Synthesis of Genetic Networks	p. 221
7.1 Statement of the Illustrative Problem	p. 221
7.2 Representation of Genetic Networks by Computer Programs	p. 223
7.2.1 Repertoire of Functions	p. 223
7.2.2 Repertoire of Terminals	p. 224
7.3 Preparatory Steps	p. 224
7.3.1 Program Architecture	p. 224
7.3.2 Function Set	p. 224
7.3.3 Terminal Set	p. 224
7.3.4 Fitness Measure	p. 224
7.3.5 Control Parameters	p. 225
7.4 Results	p. 225
7.4.1 Routineness of the Transition from Problems of Synthesizing Controllers, Circuits, Circuits With Layout, and Antennas to a Problem of Genetic Network Synthesis	p. 226
7.4.2 AI Ratio for the Genetic Network Problem	p. 227
8 Automatic Synthesis of Metabolic Pathways	p. 229
8.1 Our Approach to the Automatic Synthesis of the Topology and Sizing of Networks of Chemical Reactions	p. 230
8.2 Statement of Two Illustrative Problems	p. 231
8.3 Types of Chemical Reactions	p. 234
8.3.1 One-Substrate, One-Product Reaction	p. 234
8.3.2 One-Substrate, Two-Product Reaction	p. 243
8.3.3 Two-Substrate, One-Product Reaction	p. 244
8.3.4 Two-Substrate, Two-Product Reaction	p. 248
8.4 Representation of Networks of Chemical Reactions by Computer Programs	p. 250
8.4.1 Representation as a Program Tree	p. 250
8.4.2 Representation as a Symbolic Expression	p. 255
8.4.3 Representation as a System of Nonlinear Differential Equations	p. 256
8.4.4 Representation as an Analog Electrical Circuit	p. 259
8.4.5 Flexibility of the Representation	p. 262
8.5 Preparatory Steps	p. 263
8.5.1 Program Architecture	p. 263
8.5.2 Function Set	p. 264
8.5.3 Terminal Set	p. 264
8.5.4 Fitness Measure	p. 264
8.5.5 Control Parameters	p. 267
8.6 Results for the Phospholipid Cycle	p. 267
8.6.1 Routineness of the Transition from Problem of Synthesizing Controllers, Circuits, Circuits with Layout, Antennas, and Genetic Networks to a Problem of Synthesis of a Network of Chemical Reactions	p. 274
8.6.2 AI Ratio for the Metabolic Pathway Problem for the Phospholipid Cycle	p. 275
8.7 Results for the Synthesis and Degradation of Ketone Bodies	p. 275
8.7.1 Routineness for the Metabolic Pathway Problem Involving Ketone Bodies	p. 278
8.7.2 AI Ratio for the Metabolic Pathway Problem Involving Ketone Bodies	p. 278
8.8 Future Work on Metabolic Pathways	p. 278
8.8.1 Improved Program Tree Representation	p. 278
8.8.2 Null Enzyme	p. 278
8.8.3 Minimum Amount of Data Needed	p. 278
8.8.4 Opportunities to Use Knowledge	p. 279
8.8.5 Designing Alternative Metabolisms	p. 279
9 Automatic Synthesis of Parameterized Topologies for Controllers	p. 281
9.1 Parameterized Controller for a Three-Lag Plant	p. 282
9.1.1 Preparatory Steps for the Parameterized Controller for a Three-Lag Plant	p. 283
9.1.2 Results for the Parameterized Controller for a Three-Lag Plant	p. 286
9.1.3 Routineness of the Transition from a Problem Involving a Non-Parameterized Controller to a Problem Involving a Parameterized Controller	p. 290
9.1.4 AI Ratio for the Parameterized Controller for a Three-Lag Plant	p. 291
9.2 Parameterized Controller for Two Families of Plants	p. 291
9.2.1 Preparatory Steps for the Parameterized Controller for Two Families of Plants	p. 292
9.2.2 Results for the Parameterized Controller for Two Families of Plants	p. 296
9.2.3 Human-Competitiveness of the Result for the Parameterized Controller for Two Families of Plants	p. 299
9.2.4 Routineness for the Parameterized Controller for Two Families of Plants	p. 300
9.2.5 AI Ratio for the Parameterized Controller for Two Families of Plants	p. 300
10 Automatic Synthesis of Parameterized Topologies for Circuits	p. 301
10.1 Five New Techniques	p. 301
10.1.1 New NODE Function for Connecting Distant Points	p. 302
10.1.2 Symmetry-Breaking Procedure using Geometric Coordinates	p. 303
10.1.3 Depth-First Evaluation	p. 304
10.1.4 New TWO_LEAD Function for Inserting Two-Leaded Components	p. 305
10.1.5 New Q Transistor-Creating Function	p. 305
10.2 Zobel Network with Two Free Variables	p. 306
10.2.1 Preparatory Steps for the Zobel Network Problem with Two Free Variables	p. 307
10.2.2 Results for the Zobel Network Problem with Two Free Variables	p. 310
10.2.3 Routineness fo the Transition from a Problem Involving a Non-Parameterized Circuit to a Problem Involving a Parameterized Circuit	p. 311
10.2.4 AI Ratio for the Zobel Network Problem with Two Free Variables	p. 312
10.3 Third-Order Elliptic Lowpass Filter with a Free Variable for the Modular Angle	p. 312
10.3.1 Preparatory Steps for the Third-Order Elliptic Lowpass Filter with a Free Variable for the Modular Angle	p. 313
10.3.2 Results for the Lowpass Third-Order Elliptic Filter with a Free Variable for the Modular Angle	p. 318
10.3.3 Routineness for the Lowpass Third-Order Elliptic Filter with a Free Variable for the Modular Angle	p. 324
10.3.4 AI Ratio for the Lowpass Third-Order Elliptic Filter with a Free Variable for the Modular Angle	p. 324
10.4 Passive Lowpass Filter with a Free Variable for the Passband Boundary	p. 324
10.4.1 Preparatory Steps for the Passive Lowpass Filter with a Free Variable for the Passband Boundary	p. 325
10.4.2 Results for the Passive Lowpass Filter with a Free Variable for the Passband Boundary	p. 328
10.4.3 Routineness for the Passive Lowpass Filter with a Free Variable for the Passband Boundary	p. 331
10.4.4 AI Ratio for the Passive Lowpass Filter with a Free Variable for the Passband Boundary	p. 332
10.5 Active Lowpass Filter with a Free Variable for the Passband Boundary	p. 332
10.5.1 Preparatory Steps for the Active Lowpass Filter with a Free Variable for the Passband Boundary	p. 333
10.5.2 Results for the Active Lowpass Filter with a Free Variable for the Passband Boundary	p. 335
10.5.3 Routineness for the Active Lowpass Filter with a Free Variable for the Passband Boundary	p. 339
10.5.4 AI Ratio for the Active Lowpass Filter with a Free Variable for the Passband Boundary	p. 339
11 Automatic Synthesis of Parameterized Topologies with Conditional Developmental Operators for Circuits	p. 341
11.1 Lowpass/Highpass Filter Circuit	p. 342
11.1.1 Preparatory Steps for the Lowpass/Highpass Filter	p. 342
11.1.2 Results for the Lowpass/Highpass Filter	p. 344
11.1.3 Routineness of the Transition from a Parameterized Topology Problem without Conditional Developmental Operators to a Problem with Conditional Developmental Operators	p. 348
11.1.4 AI Ratio for the Lowpass/Highpass Filter Problem	p. 348
11.2 Lowpass/Highpass Filter with Variable Passband Boundary	p. 348
11.2.1 Preparatory Steps for the Lowpass/Highpass Filter with Variable Passband Boundary	p. 349
11.2.2 Results for the Lowpass/Highpass Filter with a Variable Passband Boundary	p. 350
11.2.3 Routineness for the Lowpass/Highpass Filter with a Variable Passband Boundary	p. 357
11.2.4 AI Ratio for the Lowpass/Highpass Filter with a Variable Passband Boundary	p. 357
11.3 Quadratic/Cubic Computational Circuit	p. 358
11.3.1 Preparatory Steps for the Quadratic/Cubic Computational Circuit	p. 358
11.3.2 Results for the Quadratic/Cubic Computational Circuit	p. 360
11.4 A 40/60 dB Amplifier	p. 364
11.4.1 Preparatory Steps for the 40/60 dB Amplifier	p. 364
11.4.2 Results for 40/60 dB Amplifier	p. 366
12 Automatic Synthesis of Improved Tuning Rules for PID Controllers	p. 367
12.1 Test Bed of Plants	p. 371
12.2 Preparatory Steps for Improved PID Tuning Rules	p. 374
12.2.1 Program Architecture	p. 375
12.2.2 Terminal Set	p. 375
12.2.3 Function Set	p. 375
12.2.4 Fitness Measure	p. 375
12.2.5 Control Parameters	p. 376
12.3 Results for Improved PID Tuning Rules	p. 376
12.4 Human-Competitiveness of the Results for the Improved PID Tuning Rules	p. 382
12.5 Routineness of the Transition from Problems Involving Parameterized Topologies for Controllers to a Problem Involving PID Tuning Rules	p. 385
12.6 AI Ratio for the Improved PID Tuning Rules	p. 385
13 Automatic Synthesis of Parameterized Topologies for Improved Controllers	p. 387
13.1 Preparatory Steps for Improved General-Purpose Controllers	p. 387
13.1.1 Function Set	p. 388
13.1.2 Terminal Set	p. 388
13.1.3 Program Architecture	p. 389
13.1.4 Fitness Measure	p. 389
13.1.5 Control Parameters	p. 390
13.2 Results for Improved General-Purpose Controllers	p. 390
13.2.1 Results for First Run for Improved General-Purpose Controllers	p. 390
13.2.2 Results for Second Run for Improved General-Purpose Controllers	p. 398
13.2.3 Results for Third Run for Improved General-Purpose Controllers	p. 402
13.3 Human-Competitiveness of the Results for the Improved General-Purpose Controllers	p. 411
13.4 Routineness for the Improved General-Purpose Controllers	p. 412
13.5 AI Ratio for the Improved General-Purpose Controllers	p. 412
14 Reinvention of Negative Feedback	p. 413
14.1 Genetic Programming Takes a Ride on the Lackawanna Ferry	p. 414
14.1.1 Fitness Measure	p. 414
14.1.2 Initial Circuit, Function Set, Terminal Set, and Control Parameters	p. 415
14.2 Results for the Problem of Reducing Amplifier Distortion	p. 415
14.3 Human-Competitiveness of the Result for the Problem of Reducing Amplifier Distortion	p. 418
14.4 Routineness for the Problem of Reducing Amplifier Distortion	p. 419
14.5 AI Ratio for the Problem of Reducing Amplifier Distortion	p. 419
15 Automated Reinvention of Six Post-2000 Patented Circuits	p. 421
15.1 The Six Circuits	p. 423
15.1.1 Low-Voltage Balun Circuit	p. 423
15.1.2 Mixed Analog-Digital Variable Capacitor	p. 423
15.1.3 Voltage-Current Conversion Circuit	p. 423
15.1.4 Low-Voltage High-Current Transistor Circuit	p. 424
15.1.5 Cubic Function Generator	p. 424
15.1.6 Tunable Integrated Active Filter	p. 426
15.2 Uniformity of Treatment of the Six Problems	p. 426
15.3 Preparatory Steps for the Six Post-2000 Patented Circuits	p. 428
15.3.1 Initial Circuit	p. 428
15.3.2 Program Architecture	p. 433
15.3.3 Function Set	p. 433
15.3.4 Terminal Set	p. 434
15.3.5 Fitness Measure	p. 435
15.3.6 Control Parameters	p. 444
15.4 Results for the Six Post-2000 Patented Circuits	p. 444
15.4.1 Results for Low-Voltage Balun Circuit	p. 444
15.4.2 Results for Mixed Analog-Digital Variable Capacitor	p. 451
15.4.3 Results for High-Current Load Circuit	p. 454
15.4.4 Results for Voltage-Current Conversion Circuit	p. 458
15.4.5 Results for Cubic Function Generator	p. 461
15.4.6 Tunable Integrated Active Filter	p. 466
15.5 Commercial Practicality of Genetic Programming for Automated Circuit Synthesis	p. 478
15.6 Human-Competitiveness of the Results for the Six Post-2000 Patented Circuits	p. 481
15.7 Routineness for the Six Post-2000 Patented Circuits	p. 481
15.8 AI Ratio for the Six Post-2000 Patented Circuits	p. 482
16 Problems for Which Genetic Programming May Be Well Suited	p. 483
16.1 Characteristics Suggesting the Use of the Genetic Algorithm	p. 483
16.2 Characteristics Suggesting the Use of Genetic Programming	p. 484
16.2.1 Discovering the Size and Shape of the Solution	p. 484
16.2.2 Reuse of Substructures	p. 486
16.2.3 The Number of Substructures	p. 495
16.2.4 Hierarchical References among the Substructures	p. 496
16.2.5 Passing Parameters to Substructures	p. 497
16.2.6 Type of Substructures	p. 499
16.2.7 Number of Arguments Possessed by Substructures	p. 500
16.2.8 The Developmental Process	p. 500
16.2.9 Parameterized Topologies Containing Free Variables	p. 504
16.3 Characteristics Suggesting the Use of Genetic Methods	p. 505
16.3.1 Non-Greedy Nature of Genetic Methods	p. 505
16.3.2 Recombination in Conjunction with the Population in Genetic Methods	p. 506
17 Parallel Implementation and Computer Time	p. 515
17.1 Computer Systems Used for Work in This Book	p. 516
17.1.1 Alpha Parallel Computer System	p. 516
17.1.2 Pentium Parallel Computer System	p. 517
17.2 Computer Time for Problems in This Book	p. 518
18 Historical Perspective on Moore's Law and the Progression of Qualitatively More Substantial Results Produced by Genetic Programming	p. 523
18.1 Five Computer Systems Used in 15-Year Period	p. 523
18.2 Qualitative Nature of Results Produced by the Five Computer Systems	p. 524
18.3 Effect of Order-of-Magnitude Increases in Computer Power on the Qualitative Nature of the Results Produced by Genetic Programming	p. 526
19 Conclusion	p. 529
19.1 Genetic Programming Now Routinely Delivers High-Return Human-Competitive Machine Intelligence	p. 529
19.2 Genetic Programming Is an Automated Invention Machine	p. 530
19.3 Genetic Programming Can Automatically Create Parameterized Topologies	p. 530
19.4 Genetic Programming Has Delivered Qualitatively More Substantial Results in Synchrony with Increasing Computer Power	p. 531
Appendix A Functions and Terminals	p. 533
Appendix B Control Parameters	p. 539
Appendix C Patented or Patentable Inventions Generated by Genetic Programming	p. 551
Bibliography	p. 555
Index	p. 575

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