Evolutionary Computation
Toward a New Philosophy of Machine Intelligence
3rd Edition
Published on 6. October 2006
384 pages
978-0-471-74920-2 (ISBN)
Description
This Third Edition provides the latest tools and techniques thatenable computers to learn
The Third Edition of this internationally acclaimed publicationprovides the latest theory and techniques for using simulatedevolution to achieve machine intelligence. As a leading advocatefor evolutionary computation, the author has successfullychallenged the traditional notion of artificial intelligence, whichessentially programs human knowledge fact by fact, but does nothave the capacity to learn or adapt as evolutionary computationdoes.
Readers gain an understanding of the history of evolutionarycomputation, which provides a foundation for the author's thoroughpresentation of the latest theories shaping current research.Balancing theory with practice, the author provides readers withthe skills they need to apply evolutionary algorithms that cansolve many of today's intransigent problems by adapting to newchallenges and learning from experience. Several examples areprovided that demonstrate how these evolutionary algorithms learnto solve problems. In particular, the author provides a detailedexample of how an algorithm is used to evolve strategies forplaying chess and checkers.
As readers progress through the publication, they gain anincreasing appreciation and understanding of the relationshipbetween learning and intelligence. Readers familiar with theprevious editions will discover much new and revised material thatbrings the publication thoroughly up to date with the latestresearch, including the latest theories and empirical properties ofevolutionary computation.
The Third Edition also features new knowledge-building aids.Readers will find a host of new and revised examples. New questionsat the end of each chapter enable readers to test their knowledge.Intriguing assignments that prepare readers to manage challenges inindustry and research have been added to the end of each chapter aswell.
This is a must-have reference for professionals in computer andelectrical engineering; it provides them with the very latesttechniques and applications in machine intelligence. With itsquestion sets and assignments, the publication is also recommendedas a graduate-level textbook.
Reviews / Votes
"...a major contribution to the evolutionary computation literature...recommended reading for experienced researchers, as well as novice students..." (Computing Reviews.com, May 26, 2006)More details
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02/2006
3rd Edition
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Person
David B. Fogel is chief executive officer of NaturalSelection, Inc. in La Jolla, CA--a small business focused onsolving difficult problems in industry, medicine, and defense usingevolutionary computation, neural networks, fuzzy systems, and othermethods of computational intelligence. Dr. Fogel's experiencein evolutionary computation spans 20 years and includesapplications in pharmaceutical design, computer-assistedmammography, data mining, factory scheduling, financialforecasting, traffic flow optimization, agent-based adaptive combatsystems, and many other areas. Prior to cofounding NaturalSelection, Inc. in 1993, Dr. Fogel was a systems analyst at TitanSystems, Inc. (1984-1988), and a senior principal engineer atORINCON 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 mathematicalsciences (probability and statistics) from the University ofCalifornia at Santa Barbara in 1985. He has taught universitycourses at the graduate and undergraduate level in stochasticprocesses, probability and statistics, and evolutionarycomputation. Dr. Fogel is a prolific author in evolutionarycomputation, having published over 50 journal papers, as well as100 conference publications, 20 contributions in book chapters, twovideos, 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 theHandbook of Evolutionary Computation (Oxford, 1997) and wasthe founding editor-in-chief of the IEEE Transactions onEvolutionary Computation (1996-2002). He serves aseditor-in-chief for the journal BioSystems and is a memberof the editorial board of several other international technicaljournals.
Dr. Fogel served as a Visiting Fellow of the Australian DefenceForce Academy in November 1997, and is a member of manyprofessional societies including the American Association for theAdvancement of Science, the American Association for ArtificialIntelligence, Sigma Xi, and the New York Academy of Sciences. Hewas the founding president of the Evolutionary Programming Societyin 1991 and is a Fellow of the IEEE, as well as an associate memberof the Center for the Study of Evolution and the Origin of Life(CSEOL) at the University of California at Los Angeles. Dr. Fogelis a frequently invited lecturer at international conferences and aguest for television and radio broadcasts. His honors and awardsinclude the 2001 Sigma Xi Southwest Region Young InvestigatorAward, the 2003 Sigma Xi San Diego Section Distinguished ScientistAward, the 2003 SPIE Computational Intelligence Pioneer Award, andthe 2004 IEEE Kiyo Tomiyasu Technical Field Award.
Content
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-BasedApproach.
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 ofBehavior.
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 EvolutionaryComputation.
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 SchemaProcessing.
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 thePresence 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 ExpertKnowledge.
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.
