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Books on Genetic Programming available from MIT Press:
(ordering information is at the end of the file)

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Advances in Genetic Programming
edited by Kenneth E. Kinnear, Jr.

"Genetic Programming is a very exciting field, with many people
actively pursuing research and applications, and many more becoming
interested with every passing conference. The contributions in this
book reflect that excitement, showing the broad range of GP
applications. They also show the wide variety of techniques and
approaches being pursued, including the use of C and C++ and other
languages for GP structures, various approaches to automatic function
definition, and special representational techniques. Some chapters show
how GP can be applied to difficult, `real world' problems, while others
begin to address questions about the theory behind the apparent power
and robustness of GPs; these chapters are important first steps toward
laying a solid foundation for future GP advances." - Rick Riolo, 
Research Investigator, University of Michigan

Advances in Genetic Programming reports significant results in
improving the power of genetic programming, presenting techniques that
can be employed immediately in the solution of complex problems in many
areas, including machine learning and the simulation of autonomous
behavior.  Popular languages such as C and C++ are used in many of the
applications and experiments, illustrating how genetic programming is
not restricted to symbolic computing languages such as Lisp.
Researchers interested in getting started in genetic programming will
find information on how to begin, on what public domain code is
available, and on how to become part of the active genetic programming
community via electronic mail.

Kenneth E. Kinnear, Jr., formerly a Director of Software at Sun
Microsystems, is a founder of Adaptive Computing Technology.

April 1994 - 536 pp. - $45.00 
ISBN 0-262-11188-8 KINDH

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Forthcoming
Genetic Programming II
Automatic Discovery of Reusable Programs
John R. Koza

"For any proposed machine learning technique, two questions come to
mind: Does it work? And can it scale up? John Koza's first book on
Genetic Programming demonstrated that it does indeed work. His new book
clearly shows how Genetic Programming can efficiently address larger
scale problems by evolving hierarchies of programs." - Craig Reynolds,
Senior Software Engineer, Electronic Arts

Genetic Programming II extends the results of John Koza's
groundbreaking work on programming by means of natural selection,
described in his first book, Genetic Programming. Using a hierarchical
approach, Koza shows that complex problems can be solved by breaking
them down into smaller, simpler problems using the recently developed
technique of automatic function definition. Koza illustrates this new
technique by showing how it solves (or approximately solves) a variety
of problems in Boolean function learning, symbolic regression, control,
pattern recognition, robotics, classification, and molecular biology.

In each example, the problem is automatically decomposed into
subproblems; the subproblems are automatically solved; and the
solutions to the subproblems are automatically assembled into a
solution to the original problem. Koza shows that leverage accrues
because genetic programming with automatic function definition
repeatedly uses the solutions to the subproblems in the assembly of the
solution to the overall problem. Moreover, genetic programming with
automatic function definition produces solutions that are simpler and
smaller than the solution obtained without automatic function
definition.

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

July - 350 pp. - 220 illus. - $45.00
ISBN 0-262-11189-6 KOZGH2

Contents: Introduction. Background on Genetic Algoriothms, LISP, and
Genetic Programming. The Two Boxes Problem: Introduction to Automatic
Function Definition. The Tide Turns. Boolean Parity Functions. The Lawn
Mower Problem. Finding an Impulse Response Function. Artificial Ant on
the San Mateo Trail. Obstacle-Avoiding Robot. The Mine Sweeper
Problem.  The Bumble Bee Problem. Automatic Discovery of Detectors for
Patterns.  Flushes and Four-of-a-Kinds in a Pinochle Deck. Prediction
of Transmembrane Domains in Proteins. Prediction of Omega Loops in
Proteins. Parsing Version of the Transmembrane Problem. The Role of
Representation and the "Lens" Effect. Effect of the Choice of the
Number of Defined Functions and the Number of Their Arguments.
Conclusions.  Appendixes: Default Parameters for Controlling Runs of
Genetic Programming. Computer Implementation of Automatic Function
Definition.  Annotated Bibliography of Genetic Programming. List of
Special Symbols.  List of Special Functions. Bibliography.

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Genetic Programming II Video
The Next Generation
John R. Koza

This 60-minute video offers visual representations of the applications 
of genetic programming described in Genetic Programming II.
July

0-262-61099-X VHS/NTSC $34.95 KOZGV2
0-262-61100-7 PAL      $44.95 KOZGP2
0-262-61101-5 SECAM    $44.95 KOZGS2

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Genetic Programming
On the Programming of Computers by Means of Natural Selection 
John R. Koza

"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 nontrivial, non-tailored set of examples of automatic
programming." - John Holland

"The world of genetic algorithms in general is well explored in
comparison with the subset of genetic programming. For a long time, one
book will probably fill the gap and take its place next to every
genetic programmer's computer: John Koza's Genetic Programming . . .
Koza's book is neither theoretical nor commercial, but more of an
argument from examples that manages to impart a great deal of practical
wisdom along the way." - Release 1.0

"John Koza has discovered a general and robust method of evolving
computer programs that is effective over a breathtaking range of
problems in applied mathematics, control engineering, and artificial
intelligence." - Stewart Wilson

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 book, John Koza shows how this 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.

Contents...

1   Introduction and Overview
2   Pervasiveness of the Problem of Program Induction
3   Introduction to Genetic Algorithms
4   The Representation Problem for Genetic Algorithms
5   Overview of Genetic Programming
6   Detailed Description of Genetic Programming
7   Four Introductory Examples of Genetic Programming
8   Amount of Processing Required to Solve a Problem
9   Nonrandomness of Genetic Programming
10  Symbolic Regression - Error-Driven Evolution
11  Control - Cost-Driven Evolution
12  Evolution of Emergent Behavior
13  Evolution of Subsumption
14  Entropy-Driven Evolution
15  Evolution of Strategy
16  Co-Evolution
17  Evolution of Classification
18  Iteration, Recursion, and Setting
19  Evolution of Constrained Syntactic Structures
20  Evolution of Building Blocks
21  Evolution of Hierarchies of Building Blocks
22  Parallelization of Genetic Programming
23  Ruggedness of Genetic Programming
24  Extraneous Variables and Functions
25  Operational Issues
26  Review of Genetic Programming
27  Comparison with Other Paradigms
28  Spontaneous Emergence of Self-Replicating and Self-Improving 
    Computer Programs
29  Conclusions

Appendices contain simple software in Common LISP for 
implementing experiments in genetic programming.


1992 - 840 pp - 270 illus. - $55.00
0-262-11170-5 KOZGH

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Genetic Programming
The Movie
John R. Koza and James P. Rice


The one-hour videotape (in VHS NTSC, PAL, and SECAM formats) 
provides a general introduction to genetic programming and a 
visualization of actual computer runs for 22 of the problems 
discussed in the book GENETIC PROGRAMMING: ON THE PROGRAMMING 
OF COMPUTER BY MEANS OF NATURAL SELECTION.  The problems 
include symbolic regression, the intertwined spirals, the artificial 
ant, the truck backer upper, broom balancing, wall following, box 
moving, the discrete pursuer-evader game, the differential pursuer-
evader game, inverse kinematics for controlling a robot arm, 
emergent collecting behavior, emergent central place foraging, the 
integer randomizer, the one-dimensional cellular automaton 
randomizer, the two-dimensional cellular automaton randomizer, 
task prioritization (Pac Man), programmatic image compression, 
solving numeric equations for a numeric root, optimization of lizard 
foraging, Boolean function learning for the 11-multiplexer, co-
evolution of game-playing strategies, and hierarchical automatic 
function definition as applied to learning the Boolean even-11-
parity function.


1992
VHS/NTSC $34.95 0-262-61084-1 KOZGVV
PAL 	 $44.95 0-262-61087-6 KOZGPV
SECAM 	 $44.95 0-262-61088-4 KOZGSV

======================================================================

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