Genetic Programming: On the Programming of Computers by Means of Natural Selection (Complex Adaptive Systems)

Features

• Cover Type: Hard Cover with 840 pages
• Published by: The MIT Press December 11, 1992
• Written in: English
• ISBN 10 Number: 0262111705
• ISBN 13 Number: 978-0262111706
• Book Dimensions: 10.2 x 7.3 x 1.8 inches
• Weighs: 3.4 pounds

Product Review


The research reported in this book is a tour de force. For the first time, since the idea was bandied about in the '40s and early '50s, we have a non-trivial, nontailored set of examples of automatic programming."
—John Holland

Product Description
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.

John R. Koza is Consulting Associate Professor in the Computer Science Department at Stanford University.

Reader Reviews
Yeah, its a big book...weighs a ton. However, only the first few chapters are concerned with the basic mechanisms of GP (should be familiar to anyone with a background in genetic algorithms or evolutionary computation). The rest of the book is chock full of examples on how to apply GP. These examples are essential and very welcome. I've found that I can usually find a solved problem in Koza that is similar to what I'm after, then I adapt it to my needs. This is a great reference, but don't be fooled into thinking this book is a tutorial. Think of it more as an exposition of GP with examples. For a tutorial, look somewhere else.