Genetic Programming IV: Routine Human-Competitive Machine Intelligence (v. 4) (Paperback)

by John R. Koza (Author), Martin A. Keane (Author), Matthew J. Streeter (Author), William Mydlowec (Author), Jessen Yu (Author), Guido Lanza (Author) "The goal of getting computers to automatically solve problems is central to artificial intelligence, machine learning, and the broad area encompassed by what Turing called..." (more)
Key Phrases: variable passband boundary, developmental operators, parameterized topologies, Time Figure, Patent Office, Arthur Samuel (more...)

Editorial Reviews

Review
`Genetic Programming IV: Routine Human-Competitive Machine Intelligence, demonstrates the everyday solution of such `holy grail' problems as the automatic synthesis of analog circuits, the design of automatic controllers, and the automated programming of computers. To specialists in any of the fields covered by this book's sample problem areas, I say read this book and discover the computer-augmented inventions that are your destiny. To remaining skeptics who doubt the inventive competence of genetics and evolution, I say read this book and change your mind or risk the strong possibility that your doubts will soon cause you significant intellectual embarrassment.' David E. Goldberg, University of Illinois `The research reported in this book is a tour de force. For the first time since the idea was bandied about in the 1940s and the early 1950s, we have a set of examples of human-competitive automatic programming.' John H. Holland, University of Michigan `John Koza and his colleagues have done remarkable work in advancing the development of genetic programming and applying this to practical problems such as electric circuit design and control system design. I strongly recommend it.' Bernard Widrow, Electrical Engineering Dept., Stanford University `John Koza's genetic programming approach to machine discovery can invent solutions to more complex specifications than any other I have seen.' John McCarthy, Computer Science Dept., Stanford University

Product Description
Genetic Programming IV: Routine Human-Competitive Machine Intelligence presents the application of GP to a wide variety of problems involving automated synthesis of controllers, circuits, antennas, genetic networks, and metabolic pathways. The book describes fifteen instances where GP has created an entity that either infringes or duplicates the functionality of a previously patented 20th-century invention, six instances where it has done the same with respect to post-2000 patented inventions, two instances where GP has created a patentable new invention, and thirteen other human-competitive results. The book additionally establishes: GP now delivers routine human-competitive machine intelligence GP is an automated invention machine GP can create general solutions to problems in the form of parameterized topologies GP has delivered qualitatively more substantial results in synchrony with the relentless iteration of Moore's Law

Product Details

• Paperback: 590 pages
• Publisher: Springer (March 21, 2005)
• Language: English
• ISBN-10: 0387250670
• ISBN-13: 978-0387250670
• Product Dimensions: 9.1 x 6.1 x 1 inches
• Shipping Weight: 1.9 pounds (View shipping rates and policies)
• Average Customer Review: 4.7 out of 5 stars See all reviews (3 customer reviews)
• Amazon.com Sales Rank: #864,924 in Books (See Bestsellers in Books)

Customer Reviews

Most Helpful Customer Reviews

21 of 21 people found the following review helpful:

5.0 out of 5 stars Gp here we Go, September 27, 2004

By	Martin Arumemi-ikhide - See all my reviews (REAL NAME)

This review is from: Genetic Programming IV: Routine Human-Competitive Machine Intelligence (Hardcover)

Since using evolutionary algorithms for my work, it is easy to see how many of the current EAs can be used to solve or tackle various real world problems. But what Koza does once again is to argue the case that GP is more than just an optimization algorithm but instead an algorithm that tries to show what AI should do and how user and AI should interact to solve a problem. Once again numerous examples are given, with detail on how problems are laid out so as to get the best results from the GP. Koza shows that with well thought out planning GP's can be applied to all sorts of fields.
In one of the chapters he presents the characteristics a problem should have for GP to be applicable.
All-round Great work, my advice get all his books and digest how he approaches various problems with GP. This example format Koza uses is far more useful than talking about what GP is and its theory. Though for a good intro into Evolutionary Algorithms including GP get either Foundations of Genetic Programming or an Introduction to Genetic Programming. An all round good intro is Introduction to Evolutionary Computing.

1 of 1 people found the following review helpful:

5.0 out of 5 stars Jaw Dropping Inspiration, June 19, 2008

By	Wesley G. Faler (Detroit, MI USA) - See all my reviews (REAL NAME)

The depth and breadth of what GP can do just isn't known by the techies. This book should scare those that are content to repeat the same old design rules. The book is a bit dry for someone with only a passing interest, but I think that was the author's intent: show in unhyped, and no-uncertain terms that GP can do what we do. For someone with a curious mind, this is proof of a brave new world.

5 of 7 people found the following review helpful:

4.0 out of 5 stars Table of contents, December 28, 2007

By	J. Leung "bookmaniac" - See all my reviews (REAL NAME)

Since the "look inside" doesn't contain the toc, here it is from Barnes&Nobles:

Table of Contents
1 Introduction 1
2 Background on genetic programming 29
3 Automatic synthesis of controllers 49
4 Automatic synthesis of circuits 129
5 Automatic synthesis of circuit topology, sizing, placement, and routing 175
6 Automatic synthesis of antennas 205
7 Automatic synthesis of genetic networks 221
8 Automatic synthesis of metabolic pathways 229
9 Automatic synthesis of parameterized topologies for controllers 281
10 Automatic synthesis of parameterized topologies for circuits 301
11 Automatic synthesis of parameterized topologies with conditional developmental operators for circuits 341
12 Automatic synthesis of improved tuning rules for PID controllers 367
13 Automatic synthesis of parameterized topologies for improved controllers 387
14 Reinvention of negative feedback 413
15 Automated reinvention of six post-2000 patented circuits 421
16 Problems for which genetic programming may be well suited 483
17 Parallel implementation and computer time 515
18 Historical perspective on Moore's law and the progression of qualitatively more substantial results produced by genetic programming 523
19 Conclusion 529