Genetic Programming III: Darwinian Invention and Problem SolvingGenetic programming is a method for enabling a computer to solve a problem by telling it what needs to be done instead of how to do it. This method borrows from the theory and techniques of biological evolution to automatically create computer programs to solve problems. In this book, the authors present their latest and most important genetically evolved solutions to dozens of problems of design, optimal control, classification, systems identification, function learning, and computational molecular biology. More than half of the book focuses on the previously unsolved problem of analogue circuit synthesis. |
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Page ix
... Preparatory Steps . .33 4.1 Gene Duplication and 2.3.2 Executional Steps . .36 Deletion in Nature . 78 Reproduction Operation . 41 Crossover Operation . 41 4.2 Previous Work ... 84 Mutation Operation . 43 2.3.3 Automatic Creation of an ...
... Preparatory Steps . .33 4.1 Gene Duplication and 2.3.2 Executional Steps . .36 Deletion in Nature . 78 Reproduction Operation . 41 Crossover Operation . 41 4.2 Previous Work ... 84 Mutation Operation . 43 2.3.3 Automatic Creation of an ...
Page xi
... Preparatory Steps . 13.3.6 Tableau . 13.4 Results .. .180 13.4.1 Emergence of Subroutines 13.4.2 Emergence of Reuse 181 13.4.3 Emergence of Multiple Invocations of Multiple Subroutines ..... .183 13.4.4 Best - of - Run Individual with ...
... Preparatory Steps . 13.3.6 Tableau . 13.4 Results .. .180 13.4.1 Emergence of Subroutines 13.4.2 Emergence of Reuse 181 13.4.3 Emergence of Multiple Invocations of Multiple Subroutines ..... .183 13.4.4 Best - of - Run Individual with ...
Page xii
... Preparatory Steps .. 306 Transmembrane Segment 19.2.1 Program Architecture . 306 Identification Problem Using 19.2.2 Functions and Terminals .307 Architecture - Altering Operations 19.2.3 Fitness .307 for Iterations .283 19.2.4 ...
... Preparatory Steps .. 306 Transmembrane Segment 19.2.1 Program Architecture . 306 Identification Problem Using 19.2.2 Functions and Terminals .307 Architecture - Altering Operations 19.2.3 Fitness .307 for Iterations .283 19.2.4 ...
Page xxxviii
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Page xlvi
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Contents
Acknowledgments | xxiii |
Introduction 1 | lxvii |
Acknowledgments | lxxxvi |
Synthesis of a TwoBand Crossover | 4 |
for Iterations | 28 |
Background on Genetic Programming and Evolutionary | 28 |
Synthesis of a ThreeBand Crossover | 28 |
Synthesis of a Voltage Reference | 48 |
The Genetic Programming Problem Solver | 311 |
Emergence of Hierarchy Using | 561 |
Embryos and Test Fixtures | 571 |
Evolvable Hardware | 931 |
Discovery of Cellular Automata Rules | 959 |
Discovery of Motifs and Programmatic Motifs | 985 |
Parallelization and Implementation Issues | 1019 |
1138 | |
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Common terms and phrases
ADF0 ADF1 ADF2 ADF3 ADFO ADFS ADLO architecture-altering operations ARG1 ARGO argument map automatically defined functions automatically defined iteration automatically defined loop automatically defined recursion automatically defined stores best-of-run body branch Boolean capacitor Chapter component computer programs condition branch dummy variables embryo evolutionary evolved executed Figure fitness measure function-defining branch functions and terminals gene duplication genetic algorithm genetic operations genetic programming GO_S GPPS hierarchy IFGTZ GO_SW IFGTZ IFGTZ INDEX inductor input internal storage invocation iteration-performing branch Koza lowpass filter maximum number modifiable wire mutation NAND netlist newly created node number of arguments Operations for Subroutines output overall program pace-setting PARALLELO Parameters picked branch population Preparatory Steps problem PROGN Program Architecture program tree protein protein segment random result result-producing branch robot RSOURCE run of genetic S-expression Section selected program sequence solving sorting network subroutine creation subroutine deletion subroutine duplication test fixture tion transmembrane two-argument vector voltage