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0 (hodnocen0 x )

(1.5) Půjčeno:3x 

BK

San Francisco : Morgan Kaufmann Publishers, c2001

xxvii, 512 s. : il. ; 24 cm

ISBN 1-55860-595-9 (váz.)

The Morgan Kaufmann series in evolutionary computation

Obsahuje bibliografii na s. 475-495, rejstřík

Inteligence umělá - pojednání

000004988

Contents // Preface // Xlll // part // one Foundations // chapter one // Models and Concepts of Life and Intelligence 3 // The Mechanics of Life and Thought 4 Stochastic Adaptation: Is Anything Ever Really Random? 9 The ’Two Great Stochastic Systems" 12 The Game of Life: Emergence in Complex Systems 16 The Game of Life 17 Emergence 18 // Cellular Automata and the Edge of Chaos 20 Artificial Life in Computer Programs 26 Intelligence: Good Minds in People and Machines 30 Intelligence in People: The Boring Criterion 30 Intelligence in Machines: The Turing Criterion 32 // chapter two // Symbols, Connections, and Optimization by Trial and Error 35 // Symbols in Trees and Networks 36 Problem Solving and Optimization 48 A Super-Simple Optimization Problem 49 Three Spaces of Optimization 51 Fitness Landscapes 52 // High-Dimensional Cognitive Space and Word Meanings 55 Two Factors of Complexity: NK Landscapes 60 Combinatorial Optimization 64 // VII // Contents // Binary Optimization 67 // Random and Greedy Searches 71 Hill Climbing 72 Simulated Annealing 73 Binary and Gray Coding 74 Step Sizes and Granularity 75 Optimizing with Real Numbers 77 Summary 78 // chapter three // On Our Nonexistence as Entities: The Social Organism 81 // Views of Evolution 82 Gaia: The Living Earth 83 Differential Selection 86 Our Microscopic Masters? 91 Looking for the Right Zoom Angle 92 Flocks, Herds, Schools, and Swarms: Social Behavior as Optimization 94 Accomplishments of the Social Insects 98 // Optimizing

with Simulated Ants: Computational Swarm Intelligence 105 Staying Together but Not Colliding: Flocks, Herds, and Schools 109 Robot Societies 115 Shallow Understanding 125 Agency 129 Summary 131 // chapter four // Evolutionary Computation Theory and Paradigms 133 // Introduction 134 // Evolutionary Computation History 134 // The Four Areas of Evolutionary Computation 135 Genetic Algorithms 135 Evolutionary Programming 139 Evolution Strategies 140 Genetic Programming 141 Toward Unification 141 Evolutionary Computation Overview 142 EC Paradigm Attributes 142 Implementation 143 Genetic Algorithms 146 An Overview 146 // A Simple GA Example Problem 147 // Contents // ??/"ix } // A Review of GA Operations 152 Schemata and the Schema Theorem 159 Final Comments on Genetic Algorithms 163 Evolutionary Programming 164 // The Evolutionary Programming Procedure 165 Finite State Machine Evolution 166 Function Optimization 169 Final Comments 171 Evolution Strategies 172 Mutation 172 Recombination 174 Selection 175 // Genetic Programming 179 Summary 185 // chapter five // Humans—Actual, Imagined, and Implied 187 // Studying Minds 188 // The Fall of the Behaviorist Empire 193 The Cognitive Revolution 195 Bandura’s Social Learning Paradigm 197 Social Psychology 199 Lewin’s Field Theory 200 Norms, Conformity, and Social Influence 202 Sociocognition 205 Simulating Social Influence 206 // Paradigm Shifts in Cognitive Science 210 The Evolution of Cooperation 214 Explanatory Coherence 216 Networks

in Groups 218 Culture in Theory and Practice 220 Coordination Games 223 The El Farol Problem 226 Sugarscape 229 Tesfatsion’s ACE 232 Picker’s Competing-Norms Model 233 Latané’s Dynamic Social Impact Theory 235 Boyd and Richerson’s Evolutionary Culture Model 240 Memetics 245 Memetic Algorithms 248 Cultural Algorithms 253 Convergence of Basic and Applied Research 254 // x // Contents // Culture—and Life without It 255 Summary 258 // chapter six // Thinking Is Social 261 // Introduction 262 // Adaptation on Three Levels 263 The Adaptive Culture Model 263 Axelrod’s Culture Model 265 Experiment One: Similarity in Axelrod’s Model 267 Experiment Two: Optimization of an Arbitrary Function 268 Experiment Three: A Slightly Harder and More Interesting Function 269 Experiment Four: A Hard Function 271 Experiment Five: Parallel Constraint Satisfaction 273 Experiment Six: Symbol Processing 279 Discussion 282 Summary 284 // part // two The Particle Swarm and Collective Intelligence // chapter seven // The Particle Swarm 287 // Sociocognitive Underpinnings: Evaluate, Compare, and Imitate 288 Evaluate 288 Compare 288 Imitate 289 // A Model of Binary Decision 289 // Testing the Binary Algorithm with the De Jong Test Suite 297 No Free Lunch 299 Multimodality 302 // Minds as Parallel Constraint Satisfaction Networks in Cultures 307 The Particle Swarm in Continuous Numbers 309 The Particle Swarm in Real-Number Space 309 // Pseudocode for Particle Swarm Optimization in Continuous Numbers 313 Implementation

Issues 314 // An Example: Particle Swarm Optimization of Neural Net Weights 314 A Real-World Application 318 The Hybrid Particle Swarm 319 Science as Collaborative Search 320 // Contents // Emergent Culture, Immergent Intelligence 323 Summary 324 // chapter eight // Variations and Comparisons // Variations of the Particle Swarm Paradigm 328 Parameter Selection 328 Controlling the Explosion 337 Particle Interactions 342 Neighborhood Topology 343 Substituting Cluster Centers for Previous Bests 347 Adding Selection to Particle Swarms 353 Comparing Inertia Weights and Constriction Factors 354 Asymmetric Initialization 357 Some Thoughts on Variations 359 // Are Particle Swarms Really a Kind of Evolutionary Algorithm? 361 Evolution beyond Darwin 362 Selection and Self-Organization 363 Ergodicity: Where Can It Get from Here? 366 Convergence of Evolutionary Computation and Particle Swarms 367 Summary 368 // chapter nine // Applications // Evolving Neural Networks with Particle Swarms 370 Review of Previous Work 370 // Advantages and Disadvantages of Previous Approaches 374 The Particle Swarm Optimization Implementation Used Here 376 Implementing Neural Network Evolution 377 An Example Application 379 Conclusions 381 Human Tremor Analysis 382 // Data Acquisition Using Actigraphy 383 Data Preprocessing 385 // Analysis with Particle Swarm Optimization 386 Summary 389 Other Applications 389 // Computer Numerically Controlled Milling Optimization 389 // Ingredient Mix Optimization 391 // Reactive

Power and Voltage Control 391 // Battery Pack State-of-Charge Estimation 391 // Summary 392 // xii // Contents // chapter ten // Implications and Speculations 393 // Introduction 394 Assertions 395 // Up from Social Learning: Bandura 398 Information and Motivation 399 Vicarious versus Direct Experience 399 The Spread of Influence 400 Machine Adaptation 401 Learning or Adaptation? 402 Cellular Automata 403 Down from Culture 405 Soft Computing 408 // Interaction within Small Groups: Group Polarization 409 Informational and Normative Social Influence 411 Self-Esteem 412 // Self-Attribution and Social Illusion 414 Summary 419 // chapter eleven // And in Conclusion ... 421 // Appendix A Statistics for Swarmers 429 // Appendix ? Genetic Algorithm Implementation 451 // Glossary 457 // References 475 // Index 497