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群体智能(英文版)(图灵原版计算机科学系列)

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  分類: 图书,计算机与互联网,计算机控制仿真与人工智能,人工智能,
  品牌: James Kennedy

基本信息·出版社:人民邮电出版社

·页码:512 页

·出版日期:2009年

·ISBN:7115195501/9787115195500

·条形码:9787115195500

·包装版本:1版

·装帧:平装

·开本:16

·正文语种:英语

·丛书名:图灵原版计算机科学系列

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内容简介群体智能是通过模拟自然界生物群体行为来实现人工智能的一种方法。《群体智能》综合运用认知科学、社会心理学、人工智能和演化计算等学科知识,提供了一些非常有价值的新见解,并将这些见解加以应用,以解决困难的工程问题。书中首先探讨了基础理论,然后详尽展示如何将这些理论和模型应用于新的计算智能方法(粒子群)中,以适应智能系统的行为,最后描述了应用粒子群优化算法的好处,提供了强有力的优化、学习和问题解决的方法。

《群体智能》主要面向计算机相关学科的高年级本科生或研究生以及相关领域的研究与开发技术人员。

作者简介James Kennedy社会心理学家。自1994年起,他一直致力于粒子群算法的研究工作,并与Russell C.Eberhart共同开发了粒子群优化算法。目前在美国劳工部从事调查方法的研究工作。他在计算机科学和社会科学杂志和学报上发表过许多关于粒子群的论文。

RusselI C.Eberhart普度大学电子与计算机工程系主任。IEEE会士。与JamesKennedy共同提出了粒子群优化算法。曾任IEEE神经网络委员会的主席。除了本书之外,他还著有《计算智能:从概念到实现》(影印版由人民邮电出版社出版)等。

Yuhui Shi (史玉回)国际计算智能领域专家,现任Joumal ofSwarm Intellgence编委,IEEE CIS群体智能任务组主席,西交利物浦大学电子与电气工程系教授。1992年获东南大学博士学位,先后在美国、韩国、澳大利亚等地从事研究工作,曾任美国电子资讯系统公司专家长达9年。他还是《计算智能:从概念到实现》一书的作者之一。

媒体推荐“本书内容丰富,富于启发性和思想性,强烈推荐给所有的演进计算研究人员。”

——Genetic Programming and Evolvable'Machines

“这本书极为出色,不愧为PSO和群体智能的最佳参考书:”

——Konstantions E.Parsopoulos 希腊Palras大学

编辑推荐群体智能是近年来发展迅速的人工智能学科领域。通过研究分散、自组织的动物群体和人类社会的智能行为,学者们提出了许多迥异于传统思路的智能算法,很好地解决了不少原来非常棘手的复杂工程问题。与蚁群算法齐名的粒子群优化(particle swarm optimizatiotl,简称PSO)算法就是其中最受瞩目、应用最为广泛的成果之一。

《群体智能》由粒子群优化算法之父撰写,是该领域毋庸置疑的经典著作。作者提出,人类智能来源于社会环境中个体之间的交互,这种智能模型可以有效地应用到人工智能系统中去。书中首先从社会心理学、认知科学和演化计算等多个角度阐述了这种新方法的基础,然后详细说明了应用这些理论和模型所得出的新的计算智能方法——粒子群优化,进而深入地探讨了如何将粒子群优化应用于广泛的工程问题。

《群体智能》的C及ViSLlaI Basic源代码可以在图灵网站(WWW.turingbook.com)《群体智能》网页免费注册下载。

目录

part one Foundations

chapter oneModels and Concepts of Life and Intelligence3

The Mechanics of Life and Thought4

Stochastic Adaptation: Is Anything Ever Really Random?9

The “Two Great Stochastic Systems”12

The Game of Life: Emergence in Complex Systems16

The Game of Life17

Emergence18

Cellular Automata and the Edge of Chaos20

Artificial Life in Computer Programs26

Intelligence: Good Minds in People and Machines30

Intelligence in People: The Boring Criterion30

Intelligence in Machines: The Turing Criterion32

chapter twoSymbols, Connections, and Optimization by Trial and Error35

Symbols in Trees and Networks36

Problem Solving and Optimization48

A Super-Simple Optimization Problem49

Three Spaces of Optimization51

Fitness Landscapes52

High-Dimensional Cognitive Space and Word Meanings55

Two Factors of Complexity: NK Landscapes60

Combinatorial Optimization64

Binary Optimization67

Random and Greedy Searches71

Hill Climbing72

Simulated Annealing73

Binary and Gray Coding74

Step Sizes and Granularity75

Optimizing with Real Numbers77

Summary78

chapter threeOn Our Nonexistence as Entities: The Social Organism81

Views of Evolution82

Gaia: The Living Earth83

Differential Selection86

Our Microscopic Masters?91

Looking for the Right Zoom Angle92

Flocks, Herds, Schools, and Swarms: Social Behavior as Optimization94

Accomplishments of the Social Insects98

Optimizing with Simulated Ants: Computational Swarm Intelligence105

Staying Together but Not Colliding: Flocks, Herds, and Schools109

Robot Societies115

Shallow Understanding125

Agency129

Summary131

chapter fourEvolutionary Computation Theory and Paradigms133

Introduction134

Evolutionary Computation History134

The Four Areas of Evolutionary Computation135

Genetic Algorithms135

Evolutionary Programming139

Evolution Strategies140

Genetic Programming141

Toward Unification141

Evolutionary Computation Overview142

EC Paradigm Attributes142

Implementation143

Genetic Algorithms146

An Overview146

A Simple GA Example Problem147

A Review of GA Operations152

Schemata and the Schema Theorem159

Final Comments on Genetic Algorithms163

Evolutionary Programming164

The Evolutionary Programming Procedure165

Finite State Machine Evolution166

Function Optimization169

Final Comments171

Evolution Strategies172

Mutation172

Recombination174

Selection175

Genetic Programming179

Summary185

chapter fiveHumans—Actual, Imagined, and Implied187

Studying Minds188

The Fall of the Behaviorist Empire193

The Cognitive Revolution195

Bandura’s Social Learning Paradigm197

Social Psychology199

Lewin’s Field Theory200

Norms, Conformity, and Social Influence202

Sociocognition205

Simulating Social Influence206

Paradigm Shifts in Cognitive Science210

The Evolution of Cooperation214

Explanatory Coherence216

Networks in Groups218

Culture in Theory and Practice220

Coordination Games223

The El Farol Problem226

Sugarscape229

Tesfatsion’s ACE232

Picker’s Competing-Norms Model233

Latané’s Dynamic Social Impact Theory235

Boyd and Richerson’s Evolutionary Culture Model240

Memetics245

Memetic Algorithms248

Cultural Algorithms253

Convergence of Basic and Applied Research254

Culture—and Life without It255

Summary258

chapter sixThinking Is Social261

Introduction262

Adaptation on Three Levels263

The Adaptive Culture Model263

Axelrod’s Culture Model265

Experiment One: Similarity in Axelrod’s Model267

Experiment Two: Optimization of an Arbitrary Function268

Experiment Three: A Slightly Harder and More Interesting Function269

Experiment Four: A Hard Function271

Experiment Five: Parallel Constraint Satisfaction273

Experiment Six: Symbol Processing279

Discussion282

Summary284

part twoThe Particle Swarm and Collective Intelligence

chapter sevenThe Particle Swarm287

Sociocognitive Underpinnings: Evaluate, Compare, and Imitate288

Evaluate288

Compare288

Imitate289

A Model of Binary Decision289

Testing the Binary Algorithm with the De Jong Test Suite297

No Free Lunch299

Multimodality302

Minds as Parallel Constraint Satisfaction Networks in Cultures307

The Particle Swarm in Continuous Numbers309

The Particle Swarm in Real-Number Space309

Pseudocode for Particle Swarm Optimization in Continuous Numbers313

Implementation Issues314

An Example: Particle Swarm Optimization of Neural Net Weights314

A Real-World Application318

The Hybrid Particle Swarm319

Science as Collaborative Search320

Emergent Culture, Immergent Intelligence323

Summary324

chapter eightVariations and Comparisons327

Variations of the Particle Swarm Paradigm328

Parameter Selection328

Controlling the Explosion337

Particle Interactions342

Neighborhood Topology343

Substituting Cluster Centers for Previous Bests347

Adding Selection to Particle Swarms353

Comparing Inertia Weights and Constriction Factors354

Asymmetric Initialization357

Some Thoughts on Variations359

Are Particle Swarms Really a Kind of Evolutionary Algorithm?361

Evolution beyond Darwin362

Selection and Self-Organization363

Ergodicity: Where Can It Get from Here?366

Convergence of Evolutionary Computation and Particle Swarms367

Summary368

chapter nineApplications369

Evolving Neural Networks with Particle Swarms370

Review of Previous Work370

Advantages and Disadvantages of Previous Approaches374

The Particle Swarm Optimization Implementation Used Here376

Implementing Neural Network Evolution377

An Example Application379

Conclusions381

Human Tremor Analysis382

Data Acquisition Using Actigraphy383

Data Preprocessing385

Analysis with Particle Swarm Optimization386

Summary389

Other Applications389

Computer Numerically Controlled Milling Optimization389

Ingredient Mix Optimization391

Reactive Power and Voltage Control391

Battery Pack State-of-Charge Estimation391

Summary392

chapter tenImplications and Speculations393

Introduction394

Assertions395

Up from Social Learning: Bandura398

Information and Motivation399

Vicarious versus Direct Experience399

The Spread of Influence400

Machine Adaptation401

Learning or Adaptation?402

Cellular Automata403

Down from Culture405

Soft Computing408

Interaction within Small Groups: Group Polarization409

Informational and Normative Social Influence411

Self-Esteem412

Self-Attribution and Social Illusion414

Summary419

chapter elevenAnd in Conclusion . . .421

Appendix A Statistics for Swarmers429

Appendix B Genetic Algorithm Implementation451

Glossary457

References475

Index497

……[看更多目录]

序言At this moment a half.dozen astronauts are assembling a new space station hundreds of miles above the surface of the earth.Thousands of sailors live and work under the sea in submarines.Incas iog through theAndes.Nomads roam the Arabian sands.Homo sapiensliterally,“intelli-gent man” has adapted to nearly every environment on the face of theearth.below it,and as far above it as we can propel ourselves.W_e must bedoing something right.In this book we argue that what we do right is related to our socialit.We will investigate that elusive quality known as intelligence,which isconsidered first of all a trait of humans and second as something thatmight be created in a computer,and our conclusion will be that whatever this“intelligence”is。it arises from interactions among individuals.We humans are the most social of animals:we live together in families,tribes.cities,nations,behaving and thinking according to the rules andnorms of our communities,adopting the customs of our fellows,including the facts they believe and the explanations they use to tie those factstogether.Even when we are alone,we think about other people,andeven when we think about inanimate things,we think using language the medium of interpersonal communication.

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群体智能(英文版)(图灵原版计算机科学系列)

 
 
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