목차
Preface to the Third Edition ix
Preface to the Second Edition xi
Preface to the First Edition xv
Defining Artificial Intelligence 1(32)
Background 1(2)
The Turing Test 3(2)
Simulation of Human Expertise 5(12)
Samuel's Checker Program 6(2)
Chess Programs 8(3)
Expert Systems 11(2)
A Criticism of the Expert Systems or Knowledge-Based Approach 13(2)
Fuzzy Systems 15(1)
Perspective on Methods Employing Specific Heuristics 16(1)
Neural Networks 17(4)
Definition of Intelligence 21(2)
Intelligence, the Scientific Method, and Evolution 23(3)
Evolving Artificial Intelligence 26(7)
References 27(4)
Chapter 1 Exercises 31(2)
Natural Evolution 33(26)
The Neo-Darwinian Paradigm 33(1)
The Genotype and the Phenotype: The Optimization of Behavior 34(4)
Implications of Wright's Adaptive Topography: Optimization Is Extensive Yet Incomplete 38(2)
The Evolution of Complexity: Minimizing Surprise 40(1)
Sexual Reproduction 41(2)
Sexual Selection 43(1)
Assessing the Beneficiary of Evoluationary Optimization 44(5)
Challenges to Neo-Darwinism 49(2)
Neutral Mutations and the Neo-Darwinian Paradigm 49(1)
Punctuated Equilibrium 49(2)
Summary 51(8)
References 52(6)
Chapter 2 Exercises 58(1)
Computer Simulation of Natural Evolution 59(46)
Early Speculations and Specific Attempts 59(2)
Evolutionary Operation 59(1)
A Learning Machine 60(1)
Artificial Life 61(3)
Evolutionary Programming 64(7)
Evolution Strategies 71(4)
Genetic Algorithms 75(9)
The Evolution of Evolutionary Computation 84(21)
References 87(14)
Chapter 3 Exercises 101(4)
Theoretical and Empirical Properties of Evolutionary Computation 105(78)
The Challenge 105(1)
Theoretical Analysis of Evolutionary Computation 106(36)
The Framework for Analysis 106(1)
Convergence in the Limit 107(10)
The Error of Minimizing Expected Losses in Schema Processing 117(2)
The Two-Armed Bandit Problem 119(1)
Extending the Analysis for ``Optimally'' Allocating Trials 120(1)
Limitations of the Analysis 121(3)
Misallocating Trials and the Schema Theorem in the Presence of Noise 124(3)
Analyzing Selection 127(1)
Convergence Rates for Evolutionary Algorithms 128(10)
Does a Best Evolutionary Algorithm Exist? 138(4)
Empirical Analysis 142(25)
Variations of Crossover 142(5)
Dynamic Parameter Encoding 147(3)
Comparing Crossover to Mutation 150(8)
Crossover as a Macromutation 158(1)
Self-Adaptation in Evolutionary Algorithms 159(3)
Fitness Distributions of Search Operators 162(5)
Discussion 167(16)
References 172(6)
Chapter 4 Exercises 178(5)
Intelligent Behavior 183(66)
Intelligence in Static and Dynamic Environments 183(2)
General Problem Solving: Experiments with Tic-Tac-Toe 185(8)
The Prisoner's Dilemma: Coevolutionary Adaptation 193(22)
Background 198(2)
Evolving Finite-State Representations 200(15)
Learning How to Play Checkers without Relying on Expert Knowledge 215(16)
Evolving a Self-Learning Chess Player 231(8)
Discussion 239(10)
References 242(4)
Chapter 5 Exercises 246(3)
Perspective 249(14)
Evolution as a Unifying Principle of Intelligence 249(1)
Prediction and the Languagelike Nature of Intelligence 250(2)
The Misplaced Emphasis on Emulating Genetic Mechanisms 252(2)
Bottom-Up Versus Top-Down 254(2)
Toward a New Philosophy of Machine Intelligence 256(7)
References 257(3)
Chapter 6 Exercises 260(3)
Glossary 263(4)
Index 267(6)
About the Author 273