David B. Fogel 
Evolutionary Computation [PDF ebook] 
Toward a New Philosophy of Machine Intelligence

This Third Edition provides the latest tools and techniques that
enable computers to learn

The Third Edition of this internationally acclaimed publication
provides the latest theory and techniques for using simulated
evolution to achieve machine intelligence. As a leading advocate
for evolutionary computation, the author has successfully
challenged the traditional notion of artificial intelligence, which
essentially programs human knowledge fact by fact, but does not
have the capacity to learn or adapt as evolutionary computation
does.

Readers gain an understanding of the history of evolutionary
computation, which provides a foundation for the author’s thorough
presentation of the latest theories shaping current research.
Balancing theory with practice, the author provides readers with
the skills they need to apply evolutionary algorithms that can
solve many of today’s intransigent problems by adapting to new
challenges and learning from experience. Several examples are
provided that demonstrate how these evolutionary algorithms learn
to solve problems. In particular, the author provides a detailed
example of how an algorithm is used to evolve strategies for
playing chess and checkers.

As readers progress through the publication, they gain an
increasing appreciation and understanding of the relationship
between learning and intelligence. Readers familiar with the
previous editions will discover much new and revised material that
brings the publication thoroughly up to date with the latest
research, including the latest theories and empirical properties of
evolutionary computation.

The Third Edition also features new knowledge-building aids.
Readers will find a host of new and revised examples. New questions
at the end of each chapter enable readers to test their knowledge.
Intriguing assignments that prepare readers to manage challenges in
industry and research have been added to the end of each chapter as
well.

This is a must-have reference for professionals in computer and
electrical engineering; it provides them with the very latest
techniques and applications in machine intelligence. With its
question sets and assignments, the publication is also recommended
as a graduate-level textbook.

Spis treści

Preface to the Third Edition.

Preface to the Second Edition.

Preface to the First Edition.

1 Defining Artificial Intelligence.

1.1 Background.

1.2 The Turing Test.

1.3 Simulation of Human Expertise.

1.3.1 Samuel’s Checker Program.

1.3.2 Chess Programs.

1.3.3 Expert Systems.

1.3.4 A Criticism of the Expert Systems or Knowledge-Based Approach.

1.3.5 Fuzzy Systems.

1.3.6 Perspective on Methods Employing Specific Heuristics.

1.4 Neural Networks.

1.5 Definition of Intelligence.

1.6 Intelligence, the Scientific Method, and Evolution.

1.7 Evolving Artificial Intelligence.

References.

Chapter 1 Exercises.

2 Natural Evolution.

2.1 The Neo-Darwinian Paradigm.

2.2 The Genotype and the Phenotype: The Optimization of Behavior.

2.3 Implications of Wright’s Adaptive Topography:Optimization Is Extensive Yet Incomplete.

2.4 The Evolution of Complexity: Minimizing Surprise.

2.5 Sexual Reproduction.

2.6 Sexual Selection.

2.7 Assessing the Beneficiary of Evolutionary Optimization.

2.8 Challenges to Neo-Darwinism.

2.8.1 Neutral Mutations and the Neo-Darwinian Paradigm.

2.8.2 Punctuated Equilibrium.

2.9 Summary.

References.

Chapter 2 Exercises.

3 Computer Simulation of Natural Evolution.

3.1 Early Speculations and Specific Attempts.

3.1.1 Evolutionary Operation.

3.1.2 A Learning Machine.

3.2 Artificial Life.

3.3 Evolutionary Programming.

3.4 Evolution Strategies.

3.5 Genetic Algorithms.

3.6 The Evolution of Evolutionary Computation.

References.

Chapter 3 Exercises.

4 Theoretical and Empirical Properties of Evolutionary Computation.

4.1 The Challenge.

4.2 Theoretical Analysis of Evolutionary Computation.

4.2.1 The Framework for Analysis.

4.2.2 Convergence in the Limit.

4.2.3 The Error of Minimizing Expected Losses in Schema Processing.

4.2.3.1 The Two-Armed Bandit Problem.

4.2.3.2 Extending the Analysis for 'Optimally’Allocating Trials.

4.2.3.3 Limitations of the Analysis.

4.2.4 Misallocating Trials and the Schema Theorem in the Presence of Noise.

4.2.5 Analyzing Selection.

4.2.6 Convergence Rates for Evolutionary Algorithms.

4.2.7 Does a Best Evolutionary Algorithm Exist?

4.3 Empirical Analysis.

4.3.1 Variations of Crossover.

4.3.2 Dynamic Parameter Encoding.

4.3.3 Comparing Crossover to Mutation.

4.3.4 Crossover as a Macromutation.

4.3.5 Self-Adaptation in Evolutionary Algorithms.

4.3.6 Fitness Distributions of Search Operators.

4.4 Discussion.

References.

Chapter 4 Exercises.

5 Intelligent Behavior.

5.1 Intelligence in Static and Dynamic Environments.

5.2 General Problem Solving: Experiments with Tic-Tac-Toe.

5.3 The Prisoner’s Dilemma: Coevolutionary Adaptation.

5.3.1 Background.

5.3.2 Evolving Finite-State Representations.

5.4 Learning How to Play Checkers without Relying on Expert Knowledge.

5.5 Evolving a Self-Learning Chess Player.

5.6 Discussion.

References.

Chapter 5 Exercises.

6 Perspective.

6.1 Evolution as a Unifying Principle of Intelligence.

6.2 Prediction and the Languagelike Nature of Intelligence.

6.3 The Misplaced Emphasis on Emulating Genetic Mechanisms.

6.4 Bottom-Up Versus Top-Down.

6.5 Toward a New Philosophy of Machine Intelligence.

References.

Chapter 6 Exercises.

Glossary.

Index.

About the Author.

O autorze

David B. Fogel is chief executive officer of Natural
Selection, Inc. in La Jolla, CA–a small business focused on
solving difficult problems in industry, medicine, and defense using
evolutionary computation, neural networks, fuzzy systems, and other
methods of computational intelligence. Dr. Fogel’s experience
in evolutionary computation spans 20 years and includes
applications in pharmaceutical design, computer-assisted
mammography, data mining, factory scheduling, financial
forecasting, traffic flow optimization, agent-based adaptive combat
systems, and many other areas. Prior to cofounding Natural
Selection, Inc. in 1993, Dr. Fogel was a systems analyst at Titan
Systems, Inc. (1984-1988), and a senior principal engineer at
ORINCON Corporation (1988-1993).

Dr. Fogel received his Ph.D. degree in engineering sciences
(systems science) from the University of California at San Diego
(UCSD) in 1992. He earned an M.S. degree in engineering sciences
(systems science) from UCSD in 1990, and a B.S. in mathematical
sciences (probability and statistics) from the University of
California at Santa Barbara in 1985. He has taught university
courses at the graduate and undergraduate level in stochastic
processes, probability and statistics, and evolutionary
computation. Dr. Fogel is a prolific author in evolutionary
computation, having published over 50 journal papers, as well as
100 conference publications, 20 contributions in book chapters, two
videos, four computer games, and six books–most recently,
Blondie24: Playing at the Edge of AI (Morgan Kaufmann,
2002). In addition, Dr. Fogel is coeditor in chief of the
Handbook of Evolutionary Computation (Oxford, 1997) and was
the founding editor-in-chief of the IEEE Transactions on
Evolutionary Computation (1996-2002). He serves as
editor-in-chief for the journal Bio Systems and is a member
of the editorial board of several other international technical
journals.

Dr. Fogel served as a Visiting Fellow of the Australian Defence
Force Academy in November 1997, and is a member of many
professional societies including the American Association for the
Advancement of Science, the American Association for Artificial
Intelligence, Sigma Xi, and the New York Academy of Sciences. He
was the founding president of the Evolutionary Programming Society
in 1991 and is a Fellow of the IEEE, as well as an associate member
of the Center for the Study of Evolution and the Origin of Life
(CSEOL) at the University of California at Los Angeles. Dr. Fogel
is a frequently invited lecturer at international conferences and a
guest for television and radio broadcasts. His honors and awards
include the 2001 Sigma Xi Southwest Region Young Investigator
Award, the 2003 Sigma Xi San Diego Section Distinguished Scientist
Award, the 2003 SPIE Computational Intelligence Pioneer Award, and
the 2004 IEEE Kiyo Tomiyasu Technical Field Award.