UID:
almahu_9948026568902882
Format:
1 online resource (544 p.)
ISBN:
1-281-18698-8
,
9786611186982
,
0-08-051826-5
,
0-585-44835-3
Series Statement:
The Morgan Kaufmann series in evolutionary computation
Content:
Traditional methods for creating intelligent computational systems haveprivileged private ""internal"" cognitive and computational processes. Incontrast, Swarm Intelligence argues that humanintelligence derives from the interactions of individuals in a social worldand further, that this model of intelligence can be effectively applied toartificially intelligent systems. The authors first present the foundations ofthis new approach through an extensive review of the critical literature insocial psychology, cognitive science, and evolutionary computation. Th
Note:
Description based upon print version of record.
,
Preface; A Thumbnail Sketch of Particle Swarm Optimization; What This Book Is, and Is Not, About; Assertions; Organization of the Book; Software; Definitions; Acknowledgments; 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; The Game of Life; Emergence; Cellular Automata and the Edge of Chaos; Artificial Life in Computer Programs; Intelligence: Good Minds in People and Machines
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Intelligence in People: The Boring CriterionIntelligence in Machines: The Turing Criterion; 2 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:; 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
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3 On Our Nonexistence as Entities: The Social OrganismViews 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; 4 Evolutionary Computation Theory and Paradigms; Introduction; Evolutionary Computation History
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The Four Areas of Evolutionary ComputationGenetic 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
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Genetic ProgrammingSummary; 5 Humans - Actual, Imagined, and Implied; Studying Minds; The Fall of the Behaviorist Empire; The Cognitive Revolution; Bandura's Social Learning Paradigm; Social Psychology; Lewin's 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; Tesfatsion's ACE; Picker's Competing-Norms Model; Latan''s Dynamic Social Impact Theory
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Boyd and Richerson's Evolutionary Culture Model
,
English
Additional Edition:
ISBN 1-4933-0358-9
Additional Edition:
ISBN 1-55860-595-9
Language:
English
