- Hardcover: 1154 pages
- Publisher: Morgan Kaufmann (11 May 1999)
- Language: English
- ISBN-10: 1558605436
- ISBN-13: 978-1558605435
- Product Dimensions: 24.2 x 19.3 x 4.3 cm
Amazon Bestsellers Rank:
1,351,234 in Books (See Top 100 in Books)
- #199 in Books > Science & Nature > Popular Science > Artificial Intelligence
- #697 in Books > Science & Nature > Engineering & Technology > Electronics & Communications Engineering > Electronics Engineering > Circuits
- #1063 in Books > Science & Nature > Engineering & Technology > Electronics & Communications Engineering > Telecommunications
- See Complete Table of Contents
Genetic Programming III: Darwinian Invention and Problem Solving: Vol 3 Hardcover – 11 May 1999
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"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
About the Author
John R. Koza is a consulting professor in the Section on Medical Informatics, Department of Medicine, School of Medicine at Stanford University. Forrest H Bennett III is chief scientist of Genetic Programming Inc., Los Altos, California. David Andre is a Ph.D. student in the Computer Science Division at the University of California at Berkeley.
Martin A. Keane is chief scientist of Econometrics, Inc., Chicago. Scott Brave is a research assistant in the Tangible Media Group at the MIT Media Laboratory.
Most Helpful Customer Reviews on Amazon.com (beta) (May include reviews from Early Reviewer Rewards Program)
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. This list includes concepts such as whether the results are pantentable, publishable in scientific journals or better then best known human solutions. GP3 reports 14 experiments by the authors where the they claim that GP produced results fulfilling one or more of these properties and thus are competitive with that of a skilled human such as an engineer, mathematician, designer or programmer. Examples of results with the "darwinian invention quality" include sorting networks, analogue electrical circuit synthesis and creation of motifs for protein family detection. Pointers are also given to human competitive solutions evolved by other researchers.
Overall there is no question that this is an important book putting the spotlight on one of the peak performing and most promising candidates for the general AI prize. There is no doubt that this book belongs in the standard library of all GP researchers or practitioners. This volumous book is a bit heterogeneous, probably stemming from the fact that is combined from a number of previously published papers with some new material. On the other hand is the volume important documentation of innovative work done by John Koza and his colleagues. In many place numerous pointers to work by other researchers are given but in the end I believe that the book would have a stronger case for presenting the GP state-of-the-art by including more references to similar research by other research groups.
However most important and intriguing thing about this book is the provocative questions raised concerning definitions and claims of human competitive performance, "Darwinian invention" and artificial intelligence - particularly whether we have already passed an important milestone in the history of AI - automatic programming.
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. After reviewing some methods that were previously used to make the choice of architecture, the authors move on to describing a set of architecture-altering operations that give an automated method for determining the architectures of evolving programs. The discussion on automatically defined recursion is particularly interesting.
The book then shows how to use the results so far to allow problem-solving to be done using genetic programming, the first one being the rotation of automobile tires and the second being evolving a computer program with the behavior of Boolean even-parity functions. This is followed by a discussion of how to use architecture-altering operations to solve a time-optimal control problem. The most interesting part of this discussion is that it illustrates the important point that disadvantageous actions should be taken in the short term so that the long-term objective can be achieved.
In chapter 14, the ant foraging problem is used to illustrate a form of the (Minsky) multiagent problem and architecture-altering operations. This is followed by discussions on the digit recognition problem and the transmembrane segment identification problem. The authors choose the Fibonacci sequence to illustrate how recursion can be used in solving problems with genetic programming. The necessity of using internal storage is illustrated using the cart centering problem.
The authors then overview the use of the Genetic Programming Problem Solver (GPPS) for automatically creating a computer program to solve a problem. Several problems are examined using this Solver, such as symbolic regression, sorting networks, and the intertwined spirals problem.
The next part then considers the application of genetic programming to the automated synthesis of analog electrical circuits. The authors judge, rightfully, that the design process is one that will be a good judge of automated technique versus one that was done by humans, especially considering the fact that analog design is considered by many to be an "art" rather than a "science". The authors show how to import the SPICE simulation system into the genetic programming system, and discuss how validation of circuit design using this simulator would be done by the genetic programming system. After showing how a low-pass filter may be successfully designed using the genetic programming system, the authors show how with a few changes it can be used to design many different types of circuits. Interestingly, the authors cite the rediscovery by genetic programming of the elliptic filter topology of W. Cauer. Cauer arrived at his discovery via the use of elliptic functions, but the genetic program did not make use of these, but relied solely on the problem's fitness measure and natural selection!
An interesting discussion is also given of the role of crossover in genetic programming by comparing the problem of synthesizing a lowpass filter with and without using crossover. The authors conclude that the crossover operation plays a large contribution to the actual solution of the problem.
Then later, the authors show how genetic programming actually evolved a cellular automata that performs better than a succession of algorithms written by humans in the last two decades. Specifically, they show how genetic programming evolved a rule for the majority classification problem for one-dimensional two-state cellular automata that exceeds the accuracy of all known rules.
Most interestingly, the authors show how genetic programming evolved motifs for detecting the D-E-A-D box family of proteins and for detecting the manganese superoxide dismutase family.
The actual performance and implementation issues involved in genetic programming are discussed in the last two parts of the book. They discuss the computer time needed to yield the 14 instances where they claim that genetic programming has produced results that are competitive with human-produced results.
The authors wrap things up in the last chapter of the book and discuss other instances where genetic programming has succeeded in automatically producing computer programs that are competitive with human-produced results. The evidence they have in the book is impressive but there are a few areas that will be ultimate tests of this approach, the most important being the discovery of new mathematical results or algorithms. It is this area that requires the most creativity on the part of the inventor.
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