Genetic Programming III: Darwinian Invention and Problem Solving

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John R. Koza
Morgan Kaufmann, 1999 - Computers - 1154 pages
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Genetic programming is a method for getting a computer to solve a problem by telling it what needs to be done instead of how to do it. Koza, Bennett, Andre, and Keane present genetically evolved solutions to dozens of problems of design, optimal control, classification, system identification, function learning, and computational molecular biology. Among the solutions are 14 results competitive with human-produced results, including 10 rediscoveries of previously patented inventions.

Researchers in artificial intelligence, machine learning, evolutionary computation, and genetic algorithms will find this an essential reference to the most recent and most important results in the rapidly growing field of genetic programming.

* Explains how the success of genetic programming arises from seven fundamental differences distinguishing it from conventional approaches to artificial intelligence and machine learning
* Describes how genetic programming uses architecture-altering operations to make on-the-fly decisions on whether to use subroutines, loops, recursions, and memory
* Demonstrates that genetic programming possesses 16 attributes that can reasonably be expected of a system for automatically creating computer programs
* Presents the general-purpose Genetic Programming Problem Solver
* Focuses on the previously unsolved problem of analog circuit synthesis, presenting genetically evolved filters, amplifiers, computational circuits, a robot controller circuit, source identification circuits, a temperature-measuring circuit, a voltage reference circuit, and more
* Introduces evolvable hardware in the form of field-programmable gate arrays
* Includes an introduction to genetic programming for the uninitiated

  

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Contents

Programming 31
2
Introduction 1
39
ArchitectureAltering Operations
67
Synthesis of a TwoBand Crossover
68
Previous Methods of Determining
71
On the Origin of New Functions
75
ArchitectureAltering
78
ArchitectureAltering
87
Synthesis of a TwoBand Crossover
613
Synthesis of a ThreeBand Crossover
645
Synthesis of a Double
653
Synthesis of Butterworth
669
Synthesis of a ThreeWay Source
685
Synthesis of a Source Identification
693
Lowpass Filter with Parsimony
705
Complete Repertoire
709

Automatically Defined Iterations
121
Automatically Defined Loops
135
Defined Loops
141
Automatically Defined Recursion
147
Automatically Defined Storage
155
SelfOrganization of Hierarchies
167
Boolean Parity Problem Using
183
TimeOptimal Robot Control
215
Multiagent Problem Using
231
Digit Recognition Problem Using
247
Transmembrane Segment
257
Fibonacci Sequence
297
Cart Centering
305
The Genetic Programming Problem Solver
311
Three Problems Illustrating
317
Elements of GPPS 2 0
349
Automated Synthesis
383
Synthesis of a Lowpass Filter
391
Emergence of Structure from
503
Synthesis of a Lowpass Filter Using
515
Emergence of Hierarchy Using
561
Embryos and Test Fixtures
571
Synthesis of a Lowpass Filter Using
579
Synthesis of an Asymmetric
593
Synthesis of a 10 dB Amplifier
741
Synthesis of a 40 dB Amplifier
753
Synthesis of a 60 dB Amplifier
771
Synthesis of a 96 dB Amplifier with
787
Synthesis of an Amplifier with
803
Synthesis of Computational
815
Synthesis of a RealTime Robot
833
Synthesis of a TemperatureSensing
843
Synthesis of a Voltage Reference
851
Synthesis of a MOSFET Circuit
861
Constraints Involving Subcircuits
875
Evolvable Hardware
931
Discovery of Cellular Automata Rules
959
Discovery of Motifs and Programmatic Motifs
985
Programmatic Motifs and
1001
Parallelization and Implementation Issues
1019
Parallelization of Genetic
1025
Implementation Issues
1041
Conclusion
1049
Appendix A Acronyms
1063
Control Parameters
1069
Appendix E SPICE Transistor Models
1078
Index
1115
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About the author (1999)

Stanford University

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