Genetic Programming: On the Programming of Computers by Means of Natural SelectionIn this ground-breaking book, John Koza shows how this remarkable paradigm works and provides substantial empirical evidence that solutions to a great variety of problems from many different fields can be found by genetically breeding populations of computer programs. Genetic programming may be more powerful than neural networks and other machine learning techniques, able to solve problems in a wider range of disciplines. In this ground-breaking book, John Koza shows how this remarkable paradigm works and provides substantial empirical evidence that solutions to a great variety of problems from many different fields can be found by genetically breeding populations of computer programs. Genetic Programming contains a great many worked examples and includes a sample computer code that will allow readers to run their own programs.In getting computers to solve problems without being explicitly programmed, Koza stresses two points: that seemingly different problems from a variety of fields can be reformulated as problems of program induction, and that the recently developed genetic programming paradigm provides a way to search the space of possible computer programs for a highly fit individual computer program to solve the problems of program induction. Good programs are found by evolving them in a computer against a fitness measure instead of by sitting down and writing them. |
Contents
Introduction and Overview | 1 |
algorithm operating on fixedlength character strings and variations of the con | 4 |
Pervasiveness of the Problem of Program Induction | 9 |
Introduction to Genetic Algorithms | 17 |
wide variety of problems from a wide variety of fields These chapters are divided | 20 |
The Representation Problem for Genetic Algorithms | 63 |
Overview of Genetic Programming | 73 |
Detailed Description of Genetic Programming | 79 |
Evolution of Building Blocks | 527 |
Evolution of Hierarchies of Building Blocks | 553 |
evolution of hierarchical building blocks by means of hierarchical automatic | 561 |
Parallelization of Genetic Programming | 563 |
Ruggedness of Genetic Programming | 569 |
Extraneous Variables and Functions | 583 |
Operational Issues | 597 |
Review of Genetic Programming | 619 |
Four Introductory Examples of Genetic Programming | 121 |
Amount of Processing Required to Solve a Problem | 191 |
Nonrandomness of Genetic Programming | 205 |
Symbolic RegressionErrorDriven Evolution | 237 |
ControlCostDriven Evolution | 289 |
Evolution of Emergent Behavior | 329 |
Evolution of Subsumption | 357 |
EntropyDriven Evolution | 395 |
entropydriven evolutionchapter 14 | 417 |
Evolution of Strategy | 419 |
CoEvolution | 429 |
Evolution of Classification | 439 |
Iteration Recursion and Setting | 459 |
Evolution of Constrained Syntactic Structures | 479 |
evolution of iteration and recursionchapter 18 | 512 |
Comparison with Other Paradigms | 633 |
Spontaneous Emergence of SelfReplicating | 643 |
sexuallyreproducing and selfimproving computer programs | 652 |
Conclusions | 695 |
ProblemSpecific Part of Simple LISP Code | 705 |
Kernel of the Simple LISP Code | 735 |
Embellishments to the Simple LISP Code | 757 |
Streamlined Version of EVAL | 765 |
Editor for Simplifying SExpressions | 771 |
Testing the Simple LISP Code | 777 |
TimeSaving Techniques | 783 |
List of Special Functions | 789 |
805 | |