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Utgivning, distribution etc. Morgan Kaufmann Publishers , San Francisco ; London : cop. 2001
Utgivningsår
SAB klassifikationskod
Fysisk beskrivning xxvii,512 s. : ill. : 25 cm
Serietitel - biuppslagsform
Anmärkning: Bibliografi etc. Includes bibliographical references and index
Term
ISBN
Antal i kö:
*00001033nam a22003137a 4500
*00127975
*007|||||||||||||||||||||||
*008110824s2001 xxk | 001 0 eng c
*020 $a1-55860-595-9 :$cNo price : Formerly CIP
*035 $a(Ko)32129
*084 $aPud
*084 $a68T01
*1001 $aKennedy, James F.
*24510$aSwarm intelligence /$cJames Kennedy, Russell C. Eberhart, with Yuhui Shi
*260 $aSan Francisco ;$aLondon :$bMorgan Kaufmann Publishers ,$ccop. 2001
*300 $axxvii,512 s. :$bill. :$c25 cm
*440 $aThe Morgan Kaufmann series in evolutionary computation
*504 $aIncludes bibliographical references and index
*650 4$aSwarm intelligence
*650 4$aSystems engineering
*650 4$aDistributed artificial intelligence
*650 4$aArtificiell intelligens
*650 4$aArtificial intelligence
*7001 $aEberhart, Russell C.$4aut
*7001 $aShi, Yuhui$4oth
*8520 $hPu
^
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Traditional methods for creating intelligent computational systems have privileged private "internal" cognitive and computational processes. In contrast, Swarm Intelligence argues that human intelligence derives from the interactions of individuals in a social world and further, that this model of intelligence can be effectively applied to artificially intelligent systems. The authors first present the foundations of this new approach through an extensive review of the critical literature in social psychology, cognitive science, and evolutionary computation. They then show in detail how these theories and models apply to a new computational intelligence methodology--particle swarms--which focuses on adaptation as the key behavior of intelligent systems. Drilling down still further, the authors describe the practical benefits of applying particle swarm optimization to a range of engineering problems. Developed by the authors, this algorithm is an extension of cellular automata and provides a powerful optimization, learning, and problem solving method.
This important book presents valuable new insights by exploring the boundaries shared by cognitive science, social psychology, artificial life, artificial intelligence, and evolutionary computation and by applying these insights to the solving of difficult engineering problems. Researchers and graduate students in any of these disciplines will find the material intriguing, provocative, and revealing as will the curious and savvy computing professional.
Preface p. xiii Foundations Models and Concepts of Life and Intelligence p. 3 The Mechanics of Life and Thought p. 4 Stochastic Adaptation: Is Anything Ever Really Random? p. 9 The "Two Great Stochastic Systems" p. 12 The Game of Life: Emergence in Complex Systems p. 16 The Game of Life p. 17 Emergence p. 18 Cellular Automata and the Edge of Chaos p. 20 Artificial Life in Computer Programs p. 26 Intelligence: Good Minds in People and Machines p. 30 Intelligence in People: The Boring Criterion p. 30 Intelligence in Machines: The Turing Criterion p. 32 Symbols, Connections, and Optimization by Trial and Error p. 35 Symbols in Trees and Networks p. 36 Problem Solving and Optimization p. 48 A Super-Simple Optimization Problem p. 49 Three Spaces of Optimization p. 51 Fitness Landscapes p. 52 High-Dimensional Cognitive Space and Word Meanings p. 55 Two Factors of Complexity: NK Landscapes p. 60 Combinatorial Optimization p. 64 Binary Optimization p. 67 Random and Greedy Searches p. 71 Hill Climbing p. 72 Simulated Annealing p. 73 Binary and Gray Coding p. 74 Step Sizes and Granularity p. 75 Optimizing with Real Numbers p. 77 Summary p. 78 On Our Nonexistence as Entities: The Social Organism p. 81 Views of Evolution p. 82 Gaia: The Living Earth p. 83 Differential Selection p. 86 Our Microscopic Masters? p. 91 Looking for the Right Zoom Angle p. 92 Flocks, Herds, Schools, and Swarms: Social Behavior as Optimization p. 94 Accomplishments of the Social Insects p. 98 Optimizing with Simulated Ants: Computational Swarm Intelligence p. 105 Staying Together but Not Colliding: Flocks, Herds, and Schools p. 109 Robot Societies p. 115 Shallow Understanding p. 125 Agency p. 129 Summary p. 131 Evolutionary Computation Theory and Paradigms p. 133 Introduction p. 134 Evolutionary Computation History p. 134 The Four Areas of Evolutionary Computation p. 135 Genetic Algorithms p. 135 Evolutionary Programming p. 139 Evolution Strategies p. 140 Genetic Programming p. 141 Toward Unification p. 141 Evolutionary Computation Overview p. 142 EC Paradigm Attributes p. 142 Implementation p. 143 Genetic Algorithms p. 146 An Overview p. 146 A Simple GA Example Problem p. 147 A Review of GA Operations p. 152 Schemata and the Schema Theorem p. 159 Final Comments on Genetic Algorithms p. 163 Evolutionary Programming p. 164 The Evolutionary Programming Procedure p. 165 Finite State Machine Evolution p. 166 Function Optimization p. 169 Final Comments p. 171 Evolution Strategies p. 172 Mutation p. 172 Recombination p. 174 Selection p. 175 Genetic Programming p. 179 Summary p. 185 Humans--Actual, Imagined, and Implied p. 187 Studying Minds p. 188 The Fall of the Behaviorist Empire p. 193 The Cognitive Revolution p. 195 Bandura's Social Learning Paradigm p. 197 Social Psychology p. 199 Lewin's Field Theory p. 200 Norms, Conformity, and Social Influence p. 202 Sociocognition p. 205 Simulating Social Influence p. 206 Paradigm Shifts in Cognitive Science p. 210 The Evolution of Cooperation p. 214 Explanatory Coherence p. 216 Networks in Groups p. 218 Culture in Theory and Practice p. 220 Coordination Games p. 223 The El Farol Problem p. 226 Sugarscape p. 229 Tesfatsion's ACE p. 232 Picker's Competing-Norms Model p. 233 Latane's Dynamic Social Impact Theory p. 235 Boyd and Richerson's Evolutionary Culture Model p. 240 Memetics p. 245 Memetic Algorithms p. 248 Cultural Algorithms p. 253 Convergence of Basic and Applied Research p. 254 Culture--and Life without It p. 255 Summary p. 258 Thinking Is Social p. 261 Introduction p. 262 Adaptation on Three Levels p. 263 The Adaptive Culture Model p. 263 Axelrod's Culture Model p. 265 Similarity in Axelrod's Model p. 267 Optimization of an Arbitrary Function p. 268 A Slightly Harder and More Interesting Function p. 269 A Hard Function p. 271 Parallel Constraint Satisfaction p. 273 Symbol Processing p. 279 Discussion p. 282 Summary p. 284 The Particle Swarm and Collective Intelligence The Particle Swarm p. 287 Sociocognitive Underpinnings: Evaluate, Compare, and Imitate p. 288 Evaluate p. 288 Compare p. 288 Imitate p. 289 A Model of Binary Decision p. 289 Testing the Binary Algorithm with the De Jong Test Suite p. 297 No Free Lunch p. 299 Multimodality p. 302 Minds as Parallel Constraint Satisfaction Networks in Cultures p. 307 The Particle Swarm in Continuous Numbers p. 309 The Particle Swarm in Real-Number Space p. 309 Pseudocode for Particle Swarm Optimization in Continuous Numbers p. 313 Implementation Issues p. 314 An Example: Particle Swarm Optimization of Neural Net Weights p. 314 A Real-World Application p. 318 The Hybrid Particle Swarm p. 319 Science as Collaborative Search p. 320 Emergent Culture, Immergent Intelligence p. 323 Summary p. 324 Variations and Comparisons p. 327 Are Particle Swarms Really a Kind of Evolutionary Algorithm? p. 361 Evolution beyond Darwin p. 362 Selection and Self-Organization p. 363 Ergodicity: Where Can It Get from Here? p. 366 Convergence of Evolutionary Computation and Particle Swarms p. 367 Summary p. 368 Applications p. 369 Evolving Neural Networks with Particle Swarms p. 370 Review of Previous Work p. 370 Advantages and Disadvantages of Previous Approaches p. 374 The Particle Swarm Optimization Implementation Used Here p. 376 Implementing Neural Network Evolution p. 377 An Example Application p. 379 Conclusions p. 381 Human Tremor Analysis p. 382 Data Acquisition Using Actigraphy p. 383 Data Preprocessing p. 385 Analysis with Particle Swarm Optimization p. 386 Summary p. 389 Other Applications p. 389 Computer Numerically Controlled Milling Optimization p. 389 Ingredient Mix Optimization p. 391 Reactive Power and Voltage Control p. 391 Battery Pack State-of-Charge Estimation p. 391 Summary p. 392 Implications and Speculations p. 393 Introduction p. 394 Assertions p. 395 Up from Social Learning: Bandura p. 398 Information and Motivation p. 399 Vicarious versus Direct Experience p. 399 The Spread of Influence p. 400 Machine Adaptation p. 401 Learning or Adaptation? p. 402 Cellular Automata p. 403 Down from Culture p. 405 Soft Computing p. 408 Interaction within Small Groups: Group Polarization p. 409 Informational and Normative Social Influence p. 411 Self-Esteem p. 412 Self-Attribution and Social Illusion p. 414 Summary p. 419 And in Conclusion... p. 421 Statistics for Swarmers p. 429 Genetic Algorithm Implementation p. 451 Glossary p. 457 References p. 475 Index p. 497