群体智能:swarm intelligence

群体智能
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作者: [美] [美]
出版社: 人民邮电出版社
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
  • 《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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