Evolutionary computation [[electronic resource] ] : toward a new philosophy of machine intelligence / / David B. Fogel

• Record Nr.: UNINA9910143574303321
• Autore: Fogel David B
• Pubbl/distr/stampa: Hoboken, N.J., : John Wiley & Sons, c2006
• ISBN: 1-280-64997-6; 9786610649976; 0-471-74921-4; 0-471-74920-6
• Edizione: [3rd ed.]
• Descrizione fisica: 1 online resource (294 p.)
• Collana: IEEE Press Series on Computational Intelligence ; ; v.1
• Disciplina: 003.3; 006.3
• Soggetti: Computer simulation; Artificial intelligence; Evolutionary computation
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: "IEEE Neural Networks Council, sponsor."
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: EVOLUTIONARY COMPUTATION; Contents; 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 IntelligenceReferences; 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 Exercises3 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 Processing4.2.3.1 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 Algorithms4.3.6 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
• Sommario/riassunto: This Third Edition provides the latest tools and techniques that enable computers to learnThe 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