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群体智能：swarm intelligence

作者: [美] 肯尼迪著 , [美] 埃伯哈特著 , 史玉回著

出版社: 人民邮电出版社

出版时间: 2009-02

版次: 1

ISBN: 9787115195500

定价: 75.00

装帧: 平装

开本: 16开

纸张: 胶版纸

页数: 512页

字数: 648千字

正文语种: 英语

丛书: 图灵原版计算机科学系列

分类: 计算机与互联网

1 张插图图片

37人买过

《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。 JamesKennedy，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RussellC．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。
RusselIC．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。
YuhuiShi（史玉回）国际计算智能领域专家，现任JoumalofSwarmIntellgence编委，IEEECIS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。 partoneFoundations
chapteroneModelsandConceptsofLifeandIntelligence
TheMechanicsofLifeandThought
StochasticAdaptation：IsAnythingEverReallyRandom?
The“TwoGreatStochasticSystems”
TheGameofLife：EmergenceinComplexSystems
TheGameofLife
Emergence
CellularAutomataandtheEdgeofChaos
ArtificialLifeinComputerPrograms
Intelligence：GoodMindsinPeopleandMachines
IntelligenceinPeople：TheBoringCriterion
IntelligenceinMachines：TheTuringCriterion

chaptertwoSymbols，Connections，andOptimizationbyTrialandError
SymbolsinTreesandNetworks
ProblemSolvingandOptimization
ASuper-SimpleOptimizationProblem
ThreeSpacesofOptimization
FitnessLandscapes
High-DimensionalCognitiveSpaceandWordMeanings
TwoFactorsofComplexity：NKLandscapes
CombinatorialOptimization
BinaryOptimization
RandomandGreedySearches
HillClimbing
SimulatedAnnealing
BinaryandGrayCoding
StepSizesandGranularity
OptimizingwithRealNumbers
Summary

chapterthreeOnOurNonexistenceasEntities：TheSocialOrganism
ViewsofEvolution
Gaia：TheLivingEarth
DifferentialSelection
OurMicroscopicMasters?
LookingfortheRightZoomAngle
Flocks，Herds，Schools，andSwarms：SocialBehaviorasOptimization
AccomplishmentsoftheSocialInsects
OptimizingwithSimulatedAnts：ComputationalSwarmIntelligence
StayingTogetherbutNotColliding：Flocks，Herds，andSchools
RobotSocieties
ShallowUnderstanding
Agency
Summary

chapterfourEvolutionaryComputationTheoryandParadigms
Introduction
EvolutionaryComputationHistory
TheFourAreasofEvolutionaryComputation
GeneticAlgorithms
EvolutionaryProgramming
EvolutionStrategies
GeneticProgramming
TowardUnification
EvolutionaryComputationOverview
ECParadigmAttributes
Implementation
GeneticAlgorithms
AnOverview
ASimpleGAExampleProblem
AReviewofGAOperations
SchemataandtheSchemaTheorem
FinalCommentsonGeneticAlgorithms
EvolutionaryProgramming
TheEvolutionaryProgrammingProcedure
FiniteStateMachineEvolution
FunctionOptimization
FinalComments
EvolutionStrategies
Mutation
Recombination
Selection
GeneticProgramming
Summary

chapterfiveHumans-Actual，Imagined，andImplied
StudyingMinds
TheFalloftheBehavioristEmpire
TheCognitiveRevolution
BandurasSocialLearningParadigm
SocialPsychology
LewinsFieldTheory
Norms，Conformity，andSocialInfluence
Sociocognition
SimulatingSocialInfluence
ParadigmShiftsinCognitiveScience
TheEvolutionofCooperation
ExplanatoryCoherence
NetworksinGroups
CultureinTheoryandPractice
CoordinationGames
TheElFarolProblem
Sugarscape
TesfatsionsACE
PickersCompeting-NormsModel
LatanésDynamicSocialImpactTheory
BoydandRichersonsEvolutionaryCultureModel
Memetics
MemeticAlgorithms
CulturalAlgorithms
ConvergenceofBasicandAppliedResearch
Culture-andLifewithoutIt
Summary

chaptersixThinkingIsSocial
Introduction
AdaptationonThreeLevels
TheAdaptiveCultureModel
AxelrodsCultureModel
ExperimentOne：SimilarityinAxelrodsModel
ExperimentTwo：OptimizationofanArbitraryFunction
ExperimentThree：ASlightlyHarderandMoreInterestingFunction
ExperimentFour：AHardFunction
ExperimentFive：ParallelConstraintSatisfaction
ExperimentSix：SymbolProcessing
Discussion
Summary

parttwoTheParticleSwarmandCollectiveIntelligence
chaptersevenTheParticleSwarm
SociocognitiveUnderpinnings：Evaluate，Compare，andImitate
Evaluate
Compare
Imitate
AModelofBinaryDecision
TestingtheBinaryAlgorithmwiththeDeJongTestSuite
NoFreeLunch
Multimodality
MindsasParallelConstraintSatisfactionNetworksinCultures
TheParticleSwarminContinuousNumbers
TheParticleSwarminReal-NumberSpace
PseudocodeforParticleSwarmOptimizationinContinuousNumbers
ImplementationIssues
AnExample：ParticleSwarmOptimizationofNeuralNetWeights
AReal-WorldApplication
TheHybridParticleSwarm
ScienceasCollaborativeSearch
EmergentCulture，ImmergentIntelligence
Summary

chaptereightVariationsandComparisons
VariationsoftheParticleSwarmParadigm
ParameterSelection
ControllingtheExplosion
ParticleInteractions
NeighborhoodTopology
SubstitutingClusterCentersforPreviousBests
AddingSelectiontoParticleSwarms
ComparingInertiaWeightsandConstrictionFactors
AsymmetricInitialization
SomeThoughtsonVariations
AreParticleSwarmsReallyaKindofEvolutionaryAlgorithm?
EvolutionbeyondDarwin
SelectionandSelf-Organization
Ergodicity：WhereCanItGetfromHere?
ConvergenceofEvolutionaryComputationandParticleSwarms
Summary

chapternineApplications
EvolvingNeuralNetworkswithParticleSwarms
ReviewofPreviousWork
AdvantagesandDisadvantagesofPreviousApproaches
TheParticleSwarmOptimizationImplementationUsedHere
ImplementingNeuralNetworkEvolution
AnExampleApplication
Conclusions
HumanTremorAnalysis
DataAcquisitionUsingActigraphy
DataPreprocessing
AnalysiswithParticleSwarmOptimization
Summary
OtherApplications
ComputerNumericallyControlledMillingOptimization
IngredientMixOptimization
ReactivePowerandVoltageControl
BatteryPackState-of-ChargeEstimation
Summary

chaptertenImplicationsandSpeculations
Introduction
Assertions
UpfromSocialLearning：Bandura
InformationandMotivation
VicariousversusDirectExperience
TheSpreadofInfluence
MachineAdaptation
LearningorAdaptation?
CellularAutomata
DownfromCulture
SoftComputing
InteractionwithinSmallGroups：GroupPolarization
InformationalandNormativeSocialInfluence
Self-Esteem
Self-AttributionandSocialIllusion
Summary
chapterelevenAndinConclusion
AppendixAStatisticsforSwarmers
AppendixBGeneticAlgorithmImplementation
Glossary
References
Index
内容简介:
《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。
作者简介:
JamesKennedy，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RussellC．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。
RusselIC．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。
YuhuiShi（史玉回）国际计算智能领域专家，现任JoumalofSwarmIntellgence编委，IEEECIS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。
目录:
partoneFoundations
chapteroneModelsandConceptsofLifeandIntelligence
TheMechanicsofLifeandThought
StochasticAdaptation：IsAnythingEverReallyRandom?
The“TwoGreatStochasticSystems”
TheGameofLife：EmergenceinComplexSystems
TheGameofLife
Emergence
CellularAutomataandtheEdgeofChaos
ArtificialLifeinComputerPrograms
Intelligence：GoodMindsinPeopleandMachines
IntelligenceinPeople：TheBoringCriterion
IntelligenceinMachines：TheTuringCriterion

chaptertwoSymbols，Connections，andOptimizationbyTrialandError
SymbolsinTreesandNetworks
ProblemSolvingandOptimization
ASuper-SimpleOptimizationProblem
ThreeSpacesofOptimization
FitnessLandscapes
High-DimensionalCognitiveSpaceandWordMeanings
TwoFactorsofComplexity：NKLandscapes
CombinatorialOptimization
BinaryOptimization
RandomandGreedySearches
HillClimbing
SimulatedAnnealing
BinaryandGrayCoding
StepSizesandGranularity
OptimizingwithRealNumbers
Summary

chapterthreeOnOurNonexistenceasEntities：TheSocialOrganism
ViewsofEvolution
Gaia：TheLivingEarth
DifferentialSelection
OurMicroscopicMasters?
LookingfortheRightZoomAngle
Flocks，Herds，Schools，andSwarms：SocialBehaviorasOptimization
AccomplishmentsoftheSocialInsects
OptimizingwithSimulatedAnts：ComputationalSwarmIntelligence
StayingTogetherbutNotColliding：Flocks，Herds，andSchools
RobotSocieties
ShallowUnderstanding
Agency
Summary

chapterfourEvolutionaryComputationTheoryandParadigms
Introduction
EvolutionaryComputationHistory
TheFourAreasofEvolutionaryComputation
GeneticAlgorithms
EvolutionaryProgramming
EvolutionStrategies
GeneticProgramming
TowardUnification
EvolutionaryComputationOverview
ECParadigmAttributes
Implementation
GeneticAlgorithms
AnOverview
ASimpleGAExampleProblem
AReviewofGAOperations
SchemataandtheSchemaTheorem
FinalCommentsonGeneticAlgorithms
EvolutionaryProgramming
TheEvolutionaryProgrammingProcedure
FiniteStateMachineEvolution
FunctionOptimization
FinalComments
EvolutionStrategies
Mutation
Recombination
Selection
GeneticProgramming
Summary

chapterfiveHumans-Actual，Imagined，andImplied
StudyingMinds
TheFalloftheBehavioristEmpire
TheCognitiveRevolution
BandurasSocialLearningParadigm
SocialPsychology
LewinsFieldTheory
Norms，Conformity，andSocialInfluence
Sociocognition
SimulatingSocialInfluence
ParadigmShiftsinCognitiveScience
TheEvolutionofCooperation
ExplanatoryCoherence
NetworksinGroups
CultureinTheoryandPractice
CoordinationGames
TheElFarolProblem
Sugarscape
TesfatsionsACE
PickersCompeting-NormsModel
LatanésDynamicSocialImpactTheory
BoydandRichersonsEvolutionaryCultureModel
Memetics
MemeticAlgorithms
CulturalAlgorithms
ConvergenceofBasicandAppliedResearch
Culture-andLifewithoutIt
Summary

chaptersixThinkingIsSocial
Introduction
AdaptationonThreeLevels
TheAdaptiveCultureModel
AxelrodsCultureModel
ExperimentOne：SimilarityinAxelrodsModel
ExperimentTwo：OptimizationofanArbitraryFunction
ExperimentThree：ASlightlyHarderandMoreInterestingFunction
ExperimentFour：AHardFunction
ExperimentFive：ParallelConstraintSatisfaction
ExperimentSix：SymbolProcessing
Discussion
Summary

parttwoTheParticleSwarmandCollectiveIntelligence
chaptersevenTheParticleSwarm
SociocognitiveUnderpinnings：Evaluate，Compare，andImitate
Evaluate
Compare
Imitate
AModelofBinaryDecision
TestingtheBinaryAlgorithmwiththeDeJongTestSuite
NoFreeLunch
Multimodality
MindsasParallelConstraintSatisfactionNetworksinCultures
TheParticleSwarminContinuousNumbers
TheParticleSwarminReal-NumberSpace
PseudocodeforParticleSwarmOptimizationinContinuousNumbers
ImplementationIssues
AnExample：ParticleSwarmOptimizationofNeuralNetWeights
AReal-WorldApplication
TheHybridParticleSwarm
ScienceasCollaborativeSearch
EmergentCulture，ImmergentIntelligence
Summary

chaptereightVariationsandComparisons
VariationsoftheParticleSwarmParadigm
ParameterSelection
ControllingtheExplosion
ParticleInteractions
NeighborhoodTopology
SubstitutingClusterCentersforPreviousBests
AddingSelectiontoParticleSwarms
ComparingInertiaWeightsandConstrictionFactors
AsymmetricInitialization
SomeThoughtsonVariations
AreParticleSwarmsReallyaKindofEvolutionaryAlgorithm?
EvolutionbeyondDarwin
SelectionandSelf-Organization
Ergodicity：WhereCanItGetfromHere?
ConvergenceofEvolutionaryComputationandParticleSwarms
Summary

chapternineApplications
EvolvingNeuralNetworkswithParticleSwarms
ReviewofPreviousWork
AdvantagesandDisadvantagesofPreviousApproaches
TheParticleSwarmOptimizationImplementationUsedHere
ImplementingNeuralNetworkEvolution
AnExampleApplication
Conclusions
HumanTremorAnalysis
DataAcquisitionUsingActigraphy
DataPreprocessing
AnalysiswithParticleSwarmOptimization
Summary
OtherApplications
ComputerNumericallyControlledMillingOptimization
IngredientMixOptimization
ReactivePowerandVoltageControl
BatteryPackState-of-ChargeEstimation
Summary

chaptertenImplicationsandSpeculations
Introduction
Assertions
UpfromSocialLearning：Bandura
InformationandMotivation
VicariousversusDirectExperience
TheSpreadofInfluence
MachineAdaptation
LearningorAdaptation?
CellularAutomata
DownfromCulture
SoftComputing
InteractionwithinSmallGroups：GroupPolarization
InformationalandNormativeSocialInfluence
Self-Esteem
Self-AttributionandSocialIllusion
Summary
chapterelevenAndinConclusion
AppendixAStatisticsforSwarmers
AppendixBGeneticAlgorithmImplementation
Glossary
References
Index

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