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Genetic Programming III: Darwinian Invention and Problem Solving (Vol 3) Hardcover – May 14, 1999


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Editorial Reviews

Review

"Koza, Bennett, Andre, and Keane's evolutionary algorithm builds more complex and useful structures than the other approaches to computer learning that I have seen."
—John McCarthy, Stanford University

"John Koza and colleagues have demonstrated that genetic programming can be used to search highly discontinuous spaces and thereby find amazing solutions to practical engineering problems."
—Bernard Widrow, Stanford University

"In this impressive volume, the authors demonstrate that genetic programming is more than an intriguing idea-it is a practical synthesis method for solving hard problems."
—Nils J. Nilsson, Stanford University

"Through careful experiment, keen algorithmic intuition, and relentless application the authors deliver important results that rival those achieved by human designers. All readers in genetic and evolutionary computation and the related fields of artificial life, agents, and adaptive behavior will want this volume in their collections."
—David E. Goldberg, University of Illinois at Urbana-Champaign

"John Koza and his coauthors continue their relentless pursuit of a holy grail
in computer science: automatic programming."
—Moshe Sipper, Swiss Federal Institute of Technology (EPFL), Lausanne

From the Back Cover

"Koza, Bennett, Andre, and Keane's evolutionary algorithm builds more complex and useful structures than the other approaches to computer learning that I have seen."
--John McCarthy, Stanford University

"John Koza and colleagues have demonstrated that genetic programming can be used to search highly discontinuous spaces and thereby find amazing solutions to practical engineering problems."
--Bernard Widrow, Stanford University

"In this impressive volume, the authors demonstrate that genetic programming is more than an intriguing idea-it is a practical synthesis method for solving hard problems."

-Nils J. Nilsson, Stanford University

"Through careful experiment, keen algorithmic intuition, and relentless application the authors deliver important results that rival those achieved by human designers. All readers in genetic and evolutionary computation and the related fields of artificial life, agents, and adaptive behavior will want this volume in their collections."

-David E. Goldberg, University of Illinois at Urbana-Champaign

"John Koza and his coauthors continue their relentless pursuit of a holy grail
in computer science: automatic programming."


-Moshe Sipper, Swiss Federal Institute of Technology (EPFL), Lausanne

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.

Features

  • Explains how the success of genetic programming arises from seven fundamental differences distinguishing it from conventional approaches to artificial intelligence and machine learning
  • Describes how genetic programming uses architecture-altering operations to make on-the-fly decisions on whether to use subroutines, loops, recursions, and memory
  • Demonstrates that genetic programming possesses 16 attributes that can reasonably be expected of a system for automatically creating computer programs
  • Presents the general-purpose Genetic Programming Problem Solver
  • Focuses on the previously unsolved problem of analog circuit synthesis, presenting genetically evolved filters, amplifiers, computational circuits, a robot controller circuit, source identification circuits, a temperature-measuring circuit, a voltage reference circuit, and more
  • Introduces evolvable hardware in the form of field-programmable gate arrays
  • Includes an introduction to genetic programming for the uninitiated
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Product Details

  • Hardcover: 1154 pages
  • Publisher: Morgan Kaufmann; 1st edition (May 14, 1999)
  • Language: English
  • ISBN-10: 1558605436
  • ISBN-13: 978-1558605435
  • Product Dimensions: 9.5 x 7.6 x 1.7 inches
  • Shipping Weight: 4.2 pounds
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (7 customer reviews)
  • Amazon Best Sellers Rank: #1,172,242 in Books (See Top 100 in Books)

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Most Helpful Customer Reviews

33 of 34 people found the following review helpful By Peter Nordin on December 2, 1999
Format: Hardcover
The quest for *Automatic Programming* is the holy grail of artificial intelligence. The dream of having computer programs write other useful computer programs has haunted researchers since the nineteen fifties. In Genetic Programming III -Darwinian Invention and problem solving (GP3) by John R. Koza, Forest H Bennet III, David Andre and Martin A Keane, the authors claim that the first inscription on this trophy should be the name Genetic Programming (GP). GP is about applying evolutionary algorithms to search the space of computer programs. The authors paraphrase Arthur Samuel of 1959 and argue that with this method it is possible to "tell the computer what to do without telling it explicitly how to do it".
The main hypothesis of the book is that GP is not only the first instance of true automatic programming but also creative to such an extant that it competes with humans in solving very hard problems and therefore the solutions produced by GP can sometimes be called inventions, thus the name "Darwinian Invention Machine". The book starts by listing sixteen proposed attributes of any automatic programming system. The attribute list begins with obvious properties such as the ability to produce entities that can run on a computer, continues by describing components of full computer programs and ends by expressing fuzzier concepts such as applicability, scalability and competitiveness with human-produced results. The authors argue that GP definitely has most of the 16 attributes and at least to some extent possesses the remaining few. The last attribute, human competitive results, is in turn defined by a list of eight properties where each of them gives enough evidence to conclude competitiveness to results produced by the intellect of a human.
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25 of 27 people found the following review helpful By Dr. Lee D. Carlson HALL OF FAMEVINE VOICE on July 27, 2001
Format: Hardcover
The authors have written a fine book here and it has and will continue to be a source of good information on the subject. What is most interesting about the approach of genetic programming is that it does not make use of the inference methods of formal logic in the search for the correct program. Correctly observing that logical thinking is insufficient for invention and creativity, the authors follow the "logic considered harmful" philosophy in their attempts to get a computer to find a creative/original solution to a problem. And most importantly, they discuss fourteen examples where genetic programming has produced results that are competitive with human-produced results. The book is almost 1200 pages long, but without reading all the examples one could cover the main points in a reasonable time frame. The reader knowing the LISP language will appreciate the discussion more.
After a brief introduction to the book in chapter 1, the authors move on to a detailed discussion of the philosophy and approaches used in genetic programming. They list the five steps that must be done before applying a genetic algorithm to a problem and give an overview of the LISP background needed to understand genetic programming. The authors emphasize that the genetic algorithm is probabilistic in nature, with the initial populations, individual selection, and genetic operation chosen at random. They give flowcharts illustrating a typical genetic algorithm and program, and then show executable programs can be automatically created. A very extensive list of references on genetic programming is given at the end of the chapter.
In the next part, the authors discuss how to eliminate the requirement that the programmer specify the architecture in advance to the program to be created.
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15 of 17 people found the following review helpful By A Customer on August 30, 1999
Format: Hardcover
This excellent book gives a lengthy overview of this very important paradigm in programming. For newcomers to the field in particular, it gives a good overview. The use of Lisp code is a definite plus, as it is a language well suited to this type of programming. A fine book: buy it..it is well worth the price-L.C. (Global Mathematics, Inc).
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7 of 9 people found the following review helpful By JAMIE R LAWSON on January 25, 2002
Format: Hardcover
Why this book, when there are several shorter books on GP, and its principle author, John Koza, has written two other, more general and equally voluminous books on GP? This book addresses how to evolve program architecture, that's why! Living organisms didn't grow arms and eyes through simple mutation. It required more subtle genetic operators. Traditional genetic operators (as used in genetic algorithms) may be sufficient for evolving solutions to optimization problems where the structure if not the specifics of each solution is pretty much the same. But to effectively evolve program structures, you need architecture altering genetic operators. This book provides motivations in computer science, foundations in biology, and explanations in English.
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