Russell C.Eberhart，普度大学电子与计算机工程系主任，IEEE会士。与James Kennedy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《群体智能》（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Journal of Swarm Intelligence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《群体智能》一书的作者之一。
图书目录
chapter one Foundations Definitions Biological Basis for Neural Networks Neurons Biological versus Artificial Neural Networks Biological Basis for Evolutionary Computation Chromosomes Biological versus Artificial Chromosomes Behavioral Motivations for Fuzzy Logic Myths about Computational Intelligence Computational Intelligence Application Areas Neural Networks Evolutionary Computation Fuzzy Logic Summary Exercises chapter two Computational Intelligence Adaptation Adaptation versus Learning Three Types of Adaptation Three Spaces of Adaptation Self-organization and Evolution Evolution beyond Darwin Historical Views of Computational Intelligence Computational Intelligence as Adaptation and Self-organization The Ability to Generalize Computational Intelligence and Soft Computing versus Artificial Intelligence and Hard Computing Summary Exercises chapter three Evolutionary Computation Concepts and Paradigms History of Evolutionary Computation Genetic Algorithms Evolutionary Programming Evolution Strategies Genetic Programming Particle Swarm Optimization Toward Unification Evolutionary Computation Overview EC Paradigm Attributes Implementation Genetic Algorithms Overview of Genetic Algorithms A Sample GA Problem Review of GA Operations in the Simple Example Schemata and the Schema Theorem Comments on Genetic Algorithms Evolutionary Programming Evolutionary Programming Procedure Finite State Machine Evolution for Prediction Function Optimization Comments on Evolutionary Programming Evolution Strategies Selection Key Issues in Evolution Strategies Genetic Programming Particle Swarm Optimization Developments Resources Summary Exercises chapter four Evolutionary Computation Implementations Implementation Issues Homogeneous versus Heterogeneous Representation Population Adaptation versus Individual Adaptation Static versus Dynamic Adaptation Flowcharts versus Finite State Machines Handling Multiple Similar Cases Allocating and Freeing Memory Space Error Checking Genetic Algorithm Implementation Programming Genetic Algorithms Running the GA Implementation Particle Swarm Optimization Implementation Programming the PSO Implementation Programming the Co-evolutionary PSO Running the PSO Implementation Summary Exercises chapter five Neural Network Concepts and Paradigms Neural Network History Where Did Neural Networks Get Their Name? The Age of Camelot The Dark Age The Renaissance The Age of Neoconnectionism The Age of Computational Intelligence What Neural Networks Are andWhy They Are Useful Neural Network Components and Terminology Terminology Input and Output Patterns NetworkWeights Processing Elements Processing Element Activation Functions Neural Network Topologies Terminology Two-layer Networks Multilayer Networks Neural Network Adaptation Terminology Hebbian Adaptation Competitive Adaptation Multilayer Error Correction Adaptation Summary of Adaptation Procedures ComparingNeuralNetworks and Other Information ProcessingMethods Stochastic Approximation Kalman Filters Linear and Nonlinear Regression Correlation Bayes Classification Vector Quantization Radial Basis Functions Computational Intelligence Preprocessing Selecting Training， Test， and Validation Datasets Preparing Data Postprocessing Denormalization of Output Data Summary Exercises chapter six Neural Network Implementations Implementation Issues Topology Back-propagation Network Initialization and Normalization LearningVector QuantizerNetwork Initialization andNormalization Feedforward Calculations for the Back-propagation Network Feedforward Calculations for the LVQ-I Net Back-propagation SupervisedAdaptation by Error Back-propagation LVQ Unsupervised Adaptation Calculations The LVQ Supervised Adaptation Algorithm Issues in Evolving Neural Networks Advantages and Disadvantages of Previous EvolutionaryApproaches Evolving Neural Networks with Particle Swarm Optimization Back-propagation Implementation Programming a Back-propagation Neural Network Running the Back-propagation Implementation The Kohonen Network Implementations Programming the Learning Vector Quantizer Running the LVQ Implementation Programming the Self-organizing Feature Map Running the SOFM Implementation Evolutionary Back-propagation Network Implementation Programming the Evolutionary Back-propagation Network Running the Evolutionary Back-propagation Network Summary Exercises chapter seven Fuzzy Systems Concepts and Paradigms History Fuzzy Sets and Fuzzy Logic Logic， Fuzzy and Otherwise Fuzziness Is Not Probability The Theory of Fuzzy Sets Fuzzy Set Membership Functions Linguistic Variables Linguistic Hedges Approximate Reasoning Paradoxes in Fuzzy Logic Equality of Fuzzy Sets Containment NOT： The Complement of a Fuzzy Set AND： The Intersection of Fuzzy Sets OR： The Union of Fuzzy Sets Compensatory Operators Fuzzy Rules Fuzzification Fuzzy Rules Fire in Parallel Defuzzification Other Defuzzification Methods Measures of Fuzziness Developing a Fuzzy Controller Why Fuzzy Control A Fuzzy Controller Building a Mamdani-type Fuzzy Controller Fuzzy Controller Operation Takagi-Sugeno-Kang Method Summary Exercises chapter eight Fuzzy Systems Implementations Implementation Issues Fuzzy Rule Representation Evolutionary Design of a Fuzzy Rule System An Object-oriented Language： C++ Fuzzy Rule System Implementation Programming Fuzzy Rule Systems Running the Fuzzy Rule System Iris Dataset Application Evolving Fuzzy Rule Systems Programming the Evolutionary Fuzzy Rule System Running the Evolutionary Fuzzy Rule System Summary Exercises chapter nine Computational Intelligence Implementations Implementation Issues Adaptation of Genetic Algorithms Fuzzy Adaptation Knowledge Elicitation Fuzzy Evolutionary Fuzzy Rule System Implementation Programming the Fuzzy Evolutionary Fuzzy Rule System Running the Fuzzy Evolutionary Fuzzy Rule System Choosing the Best Tools Strengths andWeaknesses Modeling and Optimization Practical Issues Applying Computational Intelligence to Data Mining An Example Data Mining System Summary Exercises chapter ten Performance Metrics General Issues Selecting Gold Standards Partitioning the Patterns for Training， Testing， and Validation Cross Validation Fitness and Fitness Functions Parametric and Nonparametric Statistics Percent Correct Average Sum-squared Error Absolute Error Normalized Error Evolutionary Algorithm Effectiveness Metrics Mann-Whitney U Test Receiver Operating Characteristic Curves Recall and Precision Other ROC-related Measures Confusion Matrices Chi-square Test Summary Exercises chapter eleven Analysis and Explanation Sensitivity Analysis Relation Factors Zurada Sensitivity Analysis Evolutionary Computation Sensitivity Analysis Hinton Diagrams Computational Intelligence Tools for Explanation Facilities Explanation Facility Requirements Neural Network Explanation Fuzzy Expert System Explanation Evolutionary Computation Tools for Explanation An Example Neural Network Explanation Facility Summary Exercises Bibliography Index About the Authors
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