LEADER 05472nam 2200697 a 450 001 9910829879903321 005 20170810185129.0 010 $a1-280-64997-6 010 $a9786610649976 010 $a0-471-74921-4 010 $a0-471-74920-6 035 $a(CKB)1000000000355463 035 $a(EBL)272211 035 $a(SSID)ssj0000151524 035 $a(PQKBManifestationID)11910612 035 $a(PQKBTitleCode)TC0000151524 035 $a(PQKBWorkID)10318389 035 $a(PQKB)10518420 035 $a(MiAaPQ)EBC272211 035 $a(OCoLC)85820773 035 $a(PPN)27186964X 035 $a(EXLCZ)991000000000355463 100 $a20060921d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aEvolutionary computation$b[electronic resource] $etoward a new philosophy of machine intelligence /$fDavid B. Fogel 205 $a3rd ed. 210 $aHoboken, N.J. $cJohn Wiley & Sons$dc2006 215 $a1 online resource (294 p.) 225 1 $aIEEE Press Series on Computational Intelligence ;$vv.1 300 $a"IEEE Neural Networks Council, sponsor." 311 $a0-471-66951-2 320 $aIncludes bibliographical references and index. 327 $aEVOLUTIONARY 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 327 $a1.7 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 327 $aChapter 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 327 $a4.2.3 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 327 $a4.3.5 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 327 $a6.1 Evolution as a Unifying Principle of Intelligence 330 $aThis Third Edition provides the latest tools and techniques that enable computers to learnThe Third Edition of this internationally acclaimed publication provides the latest theory and techniques for using simulated evolution to achieve machine intelligence. As a leading advocate for evolutionary computation, the author has successfully challenged the traditional notion of artificial intelligence, which essentially programs human knowledge fact by fact, but does not have the capacity to learn or adapt as evolutionary computation does.Readers gain an understanding of the history 410 0$aIEEE Press Series on Computational Intelligence 606 $aComputer simulation 606 $aArtificial intelligence 606 $aEvolutionary computation 615 0$aComputer simulation. 615 0$aArtificial intelligence. 615 0$aEvolutionary computation. 676 $a003.3 676 $a006.3 700 $aFogel$b David B$028273 712 02$aIEEE Neural Networks Council. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910829879903321 996 $aEvolutionary computation$9328699 997 $aUNINA