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基本信息
作者:
出版日期：2009年
ISBN：9787115195500
版本：1版
裝幀：平裝
開本：16
英語：英語
叢書名：圖靈原版計算機科學系列
內容簡介
群體智慧型是通過模擬自然界生物群體行為來實現人工智慧的一種方法。《群體智慧型》綜合運用認知科學、社會心理學、人工智慧和演化計算等學科知識，提供了一些非常有價值的新見解，並將這些見解加以套用，以解決困難的工程問題。書中首先探討了基礎理論，然後詳盡展示如何將這些理論和模型套用於新的計算智慧型方法(粒子群)中，以適應智慧型系統的行為，最後描述了套用粒子群最佳化算法的好處，提供了強有力的最佳化、學習和問題解決的方法。
《群體智慧型》主要面向計算機相關學科的高年級本科生或研究生以及相關領域的研究與開發技術人員。
作者簡介
James Kennedy社會心理學家。自1994年起，他一直致力於粒子群算法的研究工作，並與Russell C．Eberhart共同開發了粒子群最佳化算法。在美國勞工部從事調查方法的研究工作。他在計算機科學和社會科學雜誌和學報上發表過許多關於粒子群的論文。
RusselI C．Eberhart 普度大學電子與計算機工程系主任。IEEE會士。與JamesKennedy共同提出了粒子群最佳化算法。曾任IEEE神經網路委員會的主席。除了本書之外，他還著有《計算智慧型：從概念到實現》（影印版由人民郵電出版社出版）等。
Yuhui Shi (史玉回)國際計算智慧型領域專家，現任Joumal ofSwarm Intellgence編委，IEEE CIS群體智慧型任務組主席，西交利物浦大學電子與電氣工程系教授。1992年獲東南大學博士學位，先後在美國、韓國、澳大利亞等地從事研究工作，曾任美國電子資訊系統公司專家長達9年。他還是《計算智慧型：從概念到實現》一書的作者之一。
媒體推薦
“本書內容豐富，富於啟發性和思想性，強烈推薦給所有的演進計算研究人員。”
——Genetic Programming and Evolvable'Machines
“這本書極為出色，不愧為PSO和群體智慧型的最佳參考書：”
——Konstantions E.Parsopoulos 希臘Palras大學
編輯推薦
群體智慧型是發展迅速的人工智慧學科領域。通過研究分散、自組織的動物群體和人類社會的智慧型行為，學者們提出了許多迥異於傳統思路的智慧型算法，很好地解決了不少原來非常棘手的複雜工程問題。與蟻群算法齊名的粒子群最佳化（particle swarm optimizatiotl，簡稱PSO）算法就是其中最受矚目、套用最為廣泛的成果之一。
《群體智慧型》由粒子群最佳化算法之父撰寫，是該領域毋庸置疑的經典著作。作者提出，人類智慧型來源於社會環境中個體之間的互動，這種智慧型模型可以有效地套用到人工智慧系統中去。書中首先從社會心理學、認知科學和演化計算等多個角度闡述了這種新方法的基礎，然後詳細說明了套用這些理論和模型所得出的新的計算智慧型方法——粒子群最佳化，進而深入地探討了如何將粒子群最佳化套用於廣泛的工程問題。
《群體智慧型》的C及ViSLlaI Basic原始碼可以在圖靈網站《群體智慧型》網頁免費註冊下載。
目錄
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
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
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
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
Emergent Culture, Immergent Intelligence 323
Summary 324
chapter eight Variations and Comparisons 327
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 369
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
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 B Genetic Algorithm Implementation 451
Glossary 457
References 475
Index 497
……
