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Genetic Programming III
John Koza, Forrest Bennett, David Andre, Martin Keane
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.
I. Introduction 1.Introduction II. Background 2.Background III. Architecture-Altering Operations 3.Previous Methods of Determining the Architecture of a Multi-Part Program 4. On the Origin of New Functions 5.Architecture-Altering Operations for Subroutines 6.Automatically Defined Iterations 7.Automatically Defined Loops 8.Automatically Defined Recursion 9.Automatically Defined Storage 10.Self-Organization of Hierarchies and Program Architecture 11.Rotating the Tires on an Automobile 12.Boolean Parity Problem using Architecture-Altering Operations for Subroutines 13.Time-Optimal Robot Control Problem using Architecture-Altering Operations for Subroutines 14.Multi-Agent Problem using Architecture-Altering Operations for Subroutines 15.Digit Recognition Problem using Architecture-Altering Operations for Subroutines 16.Transmembrane Segment Identification Problem using Architecture-Altering Operations for Subroutines 17.Transmembrane Segment Identification Problem using Architecture-Altering Operations for Iterations 18.Fibonacci Sequence 19.Cart Centering IV. Genetic Programming Problem Solver (GPPS) 20.Elements of GPPS 1.0 21.Three Problems Illustrating GPPS 1.0 22.Elements of GPPS 2.0 23.Six Problems Illustrating GPPS 2.0 24.Previous Work on Automated Analog Circuit Synthesis V. Automated Synthesis of Analog Electrical Circuits 25.Synthesis of a Lowpass Filter 26.Synthesis of a Highpass Filter 27.Synthesis of a Lowpass Filter Using Automatically Defined Functions 28.Synthesis of a Lowpass Filter Using Architecture-Altering Operations 29.Embryos and Test Fixtures 30.Synthesis of a Lowpass Filter Using Automatically Defined Copy 31.Synthesis of an Asymmetric Bandpass Filter 32.Synthesis of a Two-Band Crossover (Woofer-Tweeter) Filter 33.Synthesis of a Two-Band Crossover (Woofer-Tweeter) Filter Using Architecture-Altering Operations 34.Synthesis of a Three-Band Crossover (Woofer-Midrange-Tweeter) Filter 35.Synthesis of a Double Bandpass Filter Using Subcircuits 36.Synthesis of a Double Bandpass Filter Using Architecture-Altering Operations 37.Synthesis of Butterworth, Chebychev, and Elliptic Filters 38.Synthesis of a Three-Way Source Identification Circuit 39.Synthesis of a Source Identification Circuit with a Changing Number of Sources 40.Lowpass Filter with Parsimony 41.Complete Repertoire of Circuit-Constructing Functions 42.Synthesis of a 10 dB Amplifier Using Transistors 43.Synthesis of a 40 dB Amplifier 44.Synthesis of a 60 dB Amplifier 45.Synthesis of a 96 dB Amplifier with Architecture-Altering Operations 46.Synthesis of an Amplifier with a High Power Supply Rejection Ratio 47.Synthesis of Computational Circuits 48.Synthesis of a Real-Time Robot Controller Circuit with Architecture-Altering Operations 49.Synthesis of a Temperature-Sensing Circuit 50.Synthesis of a Voltage Reference Circuit 51.Synthesis of a MOSFET Circuit 52.Constraints Involving Subcircuits or Topologies 53.Minimal Embryo 54.Comparative Experiments Involving the Lowpass Filter 55.Comparative Experiments Involving the Lowpass Filter and the Automatically Defined Copy 56.The Role of Crossover in Genetic Programming VI. Evolvable Hardware 57.Evolvable Hardware and Rapidly Reconfigurable Field-Programmable Gate Arrays VII. Discovery of Cellular Automata Rules 58.Discovery of a Cellular Automata Rule for the Majority Classification Problem VIII. Discovery of Motifs and Programmatic Motifs for Molecular Biology 59.Automatic Discovery of Protein Motifs 60.Programmatic Motifs and the Cellular Location Problem IX. Parallelization and Implementation Issues 61.Computer Time 62.Parallelization of Genetic Programming 63.Implementation Issues X. Conclusion 64.Conclusion