Book description
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.
* Places particle swarms within the larger context of intelligent
adaptive behavior and evolutionary computation.
* Describes recent results of experiments with the particle swarm
optimization (PSO) algorithm
* Includes a basic overview of statistics to ensure readers can
properly analyze the results of their own experiments using the
algorithm.
* Support software which can be downloaded from the publishers
website, includes a Java PSO applet, C and Visual Basic source
code.
Table of contents
- Preface
-
Part One Foundations
-
1 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
- Cellular Automata and the Edge of Chaos (1/2)
- Cellular Automata and the Edge of Chaos (2/2)
- Artificial Life in Computer Programs
- Intelligence: Good Minds in People and Machines
-
2 Symbols, Connections, and Optimization by Trial and Error
- Symbols in Trees and Networks (1/3)
- Symbols in Trees and Networks (2/3)
- Symbols in Trees and Networks (3/3)
- Problem Solving and Optimization (1/2)
- Problem Solving and Optimization (2/2)
- High-Dimensional Cognitive Space and Word Meanings
- Two Factors of Complexity:
- Landscapes
- Combinatorial Optimization
- Binary Optimization (1/2)
- Binary Optimization (2/2)
- Optimizing with Real Numbers
- Summary
-
3 On Our Nonexistence as Entities: The Social Organism
- Views of Evolution (1/3)
- Views of Evolution (2/3)
- Views of Evolution (3/3)
- Flocks, Herds, Schools, and Swarms: Social Behavior as Optimization (1/5)
- Flocks, Herds, Schools, and Swarms: Social Behavior as Optimization (2/5)
- Flocks, Herds, Schools, and Swarms: Social Behavior as Optimization (3/5)
- Flocks, Herds, Schools, and Swarms: Social Behavior as Optimization (4/5)
- Flocks, Herds, Schools, and Swarms: Social Behavior as Optimization (5/5)
- Robot Societies (1/2)
- Robot Societies (2/2)
- Shallow Understanding
- Agency
- Summary
-
4 Evolutionary Computation Theory and Paradigms
- Introduction
- Evolutionary Computation History
- Evolutionary Computation Overview
- Genetic Algorithms (1/4)
- Genetic Algorithms (2/4)
- Genetic Algorithms (3/4)
- Genetic Algorithms (4/4)
- Evolutionary Programming (1/2)
- Evolutionary Programming (2/2)
- Evolution Strategies (1/2)
- Evolution Strategies (2/2)
- Genetic Programming (1/2)
- Genetic Programming (2/2)
- Summary
-
5 Humans — Actual, Imagined, and Implied
- Studying Minds (1/3)
- Studying Minds (2/3)
- Studying Minds (3/3)
- Social Psychology (1/2)
- Social Psychology (2/2)
- Simulating Social Influence (1/3)
- Simulating Social Influence (2/3)
- Simulating Social Influence (3/3)
- Culture in Theory and Practice (1/7)
- Culture in Theory and Practice (2/7)
- Culture in Theory and Practice (3/7)
- Culture in Theory and Practice (4/7)
- Culture in Theory and Practice (5/7)
- Culture in Theory and Practice (6/7)
- Culture in Theory and Practice (7/7)
- Culture-and Life without It
- Summary
-
6 Thinking Is Social
- Introduction
- Axelrod’s Culture Model
- Experiment One: Similarity in Axelrod’s 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 (1/2)
- Experiment Five: Parallel Constraint Satisfaction (2/2)
- Experiment Six: Symbol Processing
- Discussion
- Summary
-
1 Models and Concepts of Life and Intelligence
-
Part Two The Particle Swarm and Collective Intelligence
-
7 The Particle Swarm
- Sociocognitive Underpinnings: Evaluate, Compare, and Imitate
- A Model of Binary Decision (1/4)
- A Model of Binary Decision (2/4)
- A Model of Binary Decision (3/4)
- A Model of Binary Decision (4/4)
- The Particle Swarm in Continuous Numbers (1/2)
- The Particle Swarm in Continuous Numbers (2/2)
- The Hybrid Particle Swarm
- Science as Collaborative Search
- Emergent Culture, Immergent Intelligence
- Summary
-
8 Variations and Comparisons
- Variations of the Particle Swarm Paradigm (1/7)
- Variations of the Particle Swarm Paradigm (2/7)
- Variations of the Particle Swarm Paradigm (3/7)
- Variations of the Particle Swarm Paradigm (4/7)
- Variations of the Particle Swarm Paradigm (5/7)
- Variations of the Particle Swarm Paradigm (6/7)
- Variations of the Particle Swarm Paradigm (7/7)
- Are Particle Swarms Really a Kind of Evolutionary Algorithm? (1/2)
- Are Particle Swarms Really a Kind of Evolutionary Algorithm? (2/2)
- Summary
- 9 Applications
-
10 Implications and Speculations
- Introduction
- Assertions
- Up from Social Learning: Bandura
- 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
- 11 And in Conclusion . . . (1/2)
- 11 And in Conclusion . . . (2/2)
- Appendix A Statistics for Swarmers
-
Appendix B Genetic Algorithm Implementation
- The Run File
- Recompiling
- Running the Program (1/13)
- Running the Program (2/13)
- Running the Program (3/13)
- Running the Program (4/13)
- Running the Program (5/13)
- Running the Program (6/13)
- Running the Program (7/13)
- Running the Program (8/13)
- Running the Program (9/13)
- Running the Program (10/13)
- Running the Program (11/13)
- Running the Program (12/13)
- Running the Program (13/13)
-
7 The Particle Swarm
Product information
- Title: Swarm Intelligence
- Author(s):
- Release date: April 2001
- Publisher(s): Morgan Kaufmann
- ISBN: 9780080518268
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