群体智能（英文版）
part one Foundations
chapter one Models and Concepts of Life and Intelligence
The Mechanics of Life and Thought
Stochastic Adaptation： Is Anything Ever Really Random?
The “Two Great Stochastic Systems”
The Game of Life： Emergence in Complex Systems
The Game of Life
查看完整
chapter one Models and Concepts of Life and Intelligence
The Mechanics of Life and Thought
Stochastic Adaptation： Is Anything Ever Really Random?
The “Two Great Stochastic Systems”
The Game of Life： Emergence in Complex Systems
The Game of Life
查看完整
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，…
查看完整
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，…
查看完整
《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》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。
《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。
part one Foundations
chapter one Models and Concepts of Life and Intelligence
The Mechanics of Life and Thought
Stochastic Adaptation： Is Anything Ever Really Random?
The “Two Great Stochastic Systems”
The Game of Life： Emergence in Complex Systems
The Game of Life
Emergence
Cellular Automata and the Edge of Chaos
Artificial Life in Computer Programs
Intelligence： Good Minds in People and Machines
Intelligence in People： The Boring Criterion
Intelligence in Machines： The Turing Criterion
chapter two Symbols， Connections， and Optimization by Trial and Error
Symbols in Trees and Networks
Problem Solving and Optimization
A Super-Simple Optimization Problem
Three Spaces of Optimization
Fitness Landscapes
High-Dimensional Cognitive Space and Word Meanings
Two Factors of Complexity： NK Landscapes
Combinatorial Optimization
Binary Optimization
Random and Greedy Searches
Hill Climbing
Simulated Annealing
Binary and Gray Coding
Step Sizes and Granularity
Optimizing with Real Numbers
Summary
chapter three On Our Nonexistence as Entities： The Social Organism
Views of Evolution
Gaia： The Living Earth
Differential Selection
Our Microscopic Masters?
Looking for the Right Zoom Angle
Flocks， Herds， Schools， and Swarms： Social Behavior as Optimization
Accomplishments of the Social Insects
Optimizing with Simulated Ants： Computational Swarm Intelligence
Staying Together but Not Colliding： Flocks， Herds， and Schools
Robot Societies
Shallow Understanding
Agency
Summary
chapter four Evolutionary Computation Theory and Paradigms
Introduction
Evolutionary Computation History
The Four Areas of Evolutionary Computation
Genetic Algorithms
Evolutionary Programming
Evolution Strategies
Genetic Programming
Toward Unification
Evolutionary Computation Overview
EC Paradigm Attributes
Implementation
Genetic Algorithms
An Overview
A Simple GA Example Problem
A Review of GA Operations
Schemata and the Schema Theorem
Final Comments on Genetic Algorithms
Evolutionary Programming
The Evolutionary Programming Procedure
Finite State Machine Evolution
Function Optimization
Final Comments
Evolution Strategies
Mutation
Recombination
Selection
Genetic Programming
Summary
chapter five Humans-Actual， Imagined， and Implied
Studying Minds
The Fall of the Behaviorist Empire
The Cognitive Revolution
Banduras Social Learning Paradigm
Social Psychology
Lewins Field Theory
Norms， Conformity， and Social Influence
Sociocognition
Simulating Social Influence
Paradigm Shifts in Cognitive Science
The Evolution of Cooperation
Explanatory Coherence
Networks in Groups
Culture in Theory and Practice
Coordination Games
The El Farol Problem
Sugarscape
Tesfatsions ACE
Pickers Competing-Norms Model
Latanés Dynamic Social Impact Theory
Boyd and Richersons Evolutionary Culture Model
Memetics
Memetic Algorithms
Cultural Algorithms
Convergence of Basic and Applied Research
Culture-and Life without It
Summary
chapter six Thinking Is Social
Introduction
Adaptation on Three Levels
The Adaptive Culture Model
Axelrods Culture Model
Experiment One： Similarity in Axelrods Model
Experiment Two： Optimization of an Arbitrary Function
Experiment Three： A Slightly Harder and More Interesting Function
Experiment Four： A Hard Function
Experiment Five： Parallel Constraint Satisfaction
Experiment Six： Symbol Processing
Discussion
Summary
part two The Particle Swarm and Collective Intelligence
chapter seven The Particle Swarm
Sociocognitive Underpinnings： Evaluate， Compare， and Imitate
Evaluate
Compare
Imitate
A Model of Binary Decision
Testing the Binary Algorithm with the De Jong Test Suite
No Free Lunch
Multimodality
Minds as Parallel Constraint Satisfaction Networks in Cultures
The Particle Swarm in Continuous Numbers
The Particle Swarm in Real-Number Space
Pseudocode for Particle Swarm Optimization in Continuous Numbers
Implementation Issues
An Example： Particle Swarm Optimization of Neural Net Weights
A Real-World Application
The Hybrid Particle Swarm
Science as Collaborative Search
Emergent Culture， Immergent Intelligence
Summary
chapter eight Variations and Comparisons
Variations of the Particle Swarm Paradigm
Parameter Selection
Controlling the Explosion
Particle Interactions
Neighborhood Topology
Substituting Cluster Centers for Previous Bests
Adding Selection to Particle Swarms
Comparing Inertia Weights and Constriction Factors
Asymmetric Initialization
Some Thoughts on Variations
Are Particle Swarms Really a Kind of Evolutionary Algorithm?
Evolution beyond Darwin
Selection and Self-Organization
Ergodicity： Where Can It Get from Here?
Convergence of Evolutionary Computation and Particle Swarms
Summary
chapter nine Applications
Evolving Neural Networks with Particle Swarms
Review of Previous Work
Advantages and Disadvantages of Previous Approaches
The Particle Swarm Optimization Implementation Used Here
Implementing Neural Network Evolution
An Example Application
Conclusions
Human Tremor Analysis
Data Acquisition Using Actigraphy
Data Preprocessing
Analysis with Particle Swarm Optimization
Summary
Other Applications
Computer Numerically Controlled Milling Optimization
Ingredient Mix Optimization
Reactive Power and Voltage Control
Battery Pack State-of-Charge Estimation
Summary
chapter ten Implications and Speculations
Introduction
Assertions
Up from Social Learning： Bandura
Information and Motivation
Vicarious versus Direct Experience
The Spread of Influence
Machine Adaptation
Learning or Adaptation?
Cellular Automata
Down from Culture
Soft Computing
Interaction within Small Groups： Group Polarization
Informational and Normative Social Influence
Self-Esteem
Self-Attribution and Social Illusion
Summary
chapter eleven And in Conclusion
Appendix A Statistics for Swarmers
Appendix B Genetic Algorithm Implementation
Glossary
References
Index
^ 收 起
chapter one Models and Concepts of Life and Intelligence
The Mechanics of Life and Thought
Stochastic Adaptation： Is Anything Ever Really Random?
The “Two Great Stochastic Systems”
The Game of Life： Emergence in Complex Systems
The Game of Life
Emergence
Cellular Automata and the Edge of Chaos
Artificial Life in Computer Programs
Intelligence： Good Minds in People and Machines
Intelligence in People： The Boring Criterion
Intelligence in Machines： The Turing Criterion
chapter two Symbols， Connections， and Optimization by Trial and Error
Symbols in Trees and Networks
Problem Solving and Optimization
A Super-Simple Optimization Problem
Three Spaces of Optimization
Fitness Landscapes
High-Dimensional Cognitive Space and Word Meanings
Two Factors of Complexity： NK Landscapes
Combinatorial Optimization
Binary Optimization
Random and Greedy Searches
Hill Climbing
Simulated Annealing
Binary and Gray Coding
Step Sizes and Granularity
Optimizing with Real Numbers
Summary
chapter three On Our Nonexistence as Entities： The Social Organism
Views of Evolution
Gaia： The Living Earth
Differential Selection
Our Microscopic Masters?
Looking for the Right Zoom Angle
Flocks， Herds， Schools， and Swarms： Social Behavior as Optimization
Accomplishments of the Social Insects
Optimizing with Simulated Ants： Computational Swarm Intelligence
Staying Together but Not Colliding： Flocks， Herds， and Schools
Robot Societies
Shallow Understanding
Agency
Summary
chapter four Evolutionary Computation Theory and Paradigms
Introduction
Evolutionary Computation History
The Four Areas of Evolutionary Computation
Genetic Algorithms
Evolutionary Programming
Evolution Strategies
Genetic Programming
Toward Unification
Evolutionary Computation Overview
EC Paradigm Attributes
Implementation
Genetic Algorithms
An Overview
A Simple GA Example Problem
A Review of GA Operations
Schemata and the Schema Theorem
Final Comments on Genetic Algorithms
Evolutionary Programming
The Evolutionary Programming Procedure
Finite State Machine Evolution
Function Optimization
Final Comments
Evolution Strategies
Mutation
Recombination
Selection
Genetic Programming
Summary
chapter five Humans-Actual， Imagined， and Implied
Studying Minds
The Fall of the Behaviorist Empire
The Cognitive Revolution
Banduras Social Learning Paradigm
Social Psychology
Lewins Field Theory
Norms， Conformity， and Social Influence
Sociocognition
Simulating Social Influence
Paradigm Shifts in Cognitive Science
The Evolution of Cooperation
Explanatory Coherence
Networks in Groups
Culture in Theory and Practice
Coordination Games
The El Farol Problem
Sugarscape
Tesfatsions ACE
Pickers Competing-Norms Model
Latanés Dynamic Social Impact Theory
Boyd and Richersons Evolutionary Culture Model
Memetics
Memetic Algorithms
Cultural Algorithms
Convergence of Basic and Applied Research
Culture-and Life without It
Summary
chapter six Thinking Is Social
Introduction
Adaptation on Three Levels
The Adaptive Culture Model
Axelrods Culture Model
Experiment One： Similarity in Axelrods Model
Experiment Two： Optimization of an Arbitrary Function
Experiment Three： A Slightly Harder and More Interesting Function
Experiment Four： A Hard Function
Experiment Five： Parallel Constraint Satisfaction
Experiment Six： Symbol Processing
Discussion
Summary
part two The Particle Swarm and Collective Intelligence
chapter seven The Particle Swarm
Sociocognitive Underpinnings： Evaluate， Compare， and Imitate
Evaluate
Compare
Imitate
A Model of Binary Decision
Testing the Binary Algorithm with the De Jong Test Suite
No Free Lunch
Multimodality
Minds as Parallel Constraint Satisfaction Networks in Cultures
The Particle Swarm in Continuous Numbers
The Particle Swarm in Real-Number Space
Pseudocode for Particle Swarm Optimization in Continuous Numbers
Implementation Issues
An Example： Particle Swarm Optimization of Neural Net Weights
A Real-World Application
The Hybrid Particle Swarm
Science as Collaborative Search
Emergent Culture， Immergent Intelligence
Summary
chapter eight Variations and Comparisons
Variations of the Particle Swarm Paradigm
Parameter Selection
Controlling the Explosion
Particle Interactions
Neighborhood Topology
Substituting Cluster Centers for Previous Bests
Adding Selection to Particle Swarms
Comparing Inertia Weights and Constriction Factors
Asymmetric Initialization
Some Thoughts on Variations
Are Particle Swarms Really a Kind of Evolutionary Algorithm?
Evolution beyond Darwin
Selection and Self-Organization
Ergodicity： Where Can It Get from Here?
Convergence of Evolutionary Computation and Particle Swarms
Summary
chapter nine Applications
Evolving Neural Networks with Particle Swarms
Review of Previous Work
Advantages and Disadvantages of Previous Approaches
The Particle Swarm Optimization Implementation Used Here
Implementing Neural Network Evolution
An Example Application
Conclusions
Human Tremor Analysis
Data Acquisition Using Actigraphy
Data Preprocessing
Analysis with Particle Swarm Optimization
Summary
Other Applications
Computer Numerically Controlled Milling Optimization
Ingredient Mix Optimization
Reactive Power and Voltage Control
Battery Pack State-of-Charge Estimation
Summary
chapter ten Implications and Speculations
Introduction
Assertions
Up from Social Learning： Bandura
Information and Motivation
Vicarious versus Direct Experience
The Spread of Influence
Machine Adaptation
Learning or Adaptation?
Cellular Automata
Down from Culture
Soft Computing
Interaction within Small Groups： Group Polarization
Informational and Normative Social Influence
Self-Esteem
Self-Attribution and Social Illusion
Summary
chapter eleven And in Conclusion
Appendix A Statistics for Swarmers
Appendix B Genetic Algorithm Implementation
Glossary
References
Index
^ 收 起
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。
^ 收 起
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。
^ 收 起
《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》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。
《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。
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