{"product_id":"computational-intelligence-an-introduction-hardback-9780470035610","title":"Computational Intelligence; An Introduction (Hardback) 9780470035610","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eComputational Intelligence\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eAn Introduction\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eAndries P. Engelbrecht (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780470035610, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 12 October 2007\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e640 pages\u003cbr\u003e25.2 x 17.6 x 4 cm, 1.247 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003ci\u003eComputational Intelligence: An Introduction, Second Edition\u003c\/i\u003e offers an in-depth exploration into the adaptive mechanisms that enable intelligent behaviour in complex and changing environments. The main focus of this text is centred on the computational modelling of biological and natural intelligent systems, encompassing swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems and evolutionary computation.  \u003cp\u003eEngelbrecht provides readers with a wide knowledge of Computational Intelligence (CI) paradigms and algorithms; inviting readers to implement and problem solve real-world, complex problems within the CI development framework. This implementation framework will enable readers to tackle new problems without any difficulty through a single Java class as part of the CI library.\u003c\/p\u003e \u003cp\u003eKey features of this second edition include:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eA tutorial, hands-on based presentation of the material.\u003c\/li\u003e \u003cli\u003eState-of-the-art coverage of the most recent developments in computational intelligence with more elaborate discussions on intelligence and artificial intelligence (AI).\u003c\/li\u003e \u003cli\u003eNew discussion of Darwinian evolution versus Lamarckian evolution, also including swarm robotics, hybrid systems and artificial immune systems.\u003c\/li\u003e \u003cli\u003eA section on how to perform empirical studies; topics including statistical analysis of stochastic algorithms, and an open source library of CI algorithms.\u003c\/li\u003e \u003cli\u003eTables, illustrations, graphs, examples, assignments, Java code implementing the algorithms, and a complete CI implementation and experimental framework.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eComputational Intelligence: An Introduction, Second Edition\u003c\/i\u003e is essential reading for third and fourth year undergraduate and postgraduate students studying CI. The first edition has been prescribed by a number of overseas universities and is thus a valuable teaching tool. In addition, it will also be a useful resource for researchers in Computational Intelligence and Artificial Intelligence, as well as engineers, statisticians, operational researchers, and bioinformaticians with an interest in applying AI or CI to solve problems in their domains.\u003c\/p\u003e \u003cp\u003e Check out \u003ca href=\"http:\/\/www.ci.cs.up.ac.za\/\"\u003ehttp:\/\/www.ci.cs.up.ac.za\u003c\/a\u003e for examples, assignments and Java code implementing the algorithms.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cb\u003eFigures.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003eTables.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAlgorithms.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePreface.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I INTRODUCTION.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Computational Intelligence.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Computational Intelligence Paradigms.\u003c\/p\u003e \u003cp\u003e1.2 Short History.\u003c\/p\u003e \u003cp\u003e1.3 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II ARTIFICIAL NEURAL NETWORKS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The Artificial Neuron.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Calculating the Net Input Signal.\u003c\/p\u003e \u003cp\u003e2.2 Activation Functions.\u003c\/p\u003e \u003cp\u003e2.3 Artificial Neuron Geometry.\u003c\/p\u003e \u003cp\u003e2.4 Artificial Neuron Learning.\u003c\/p\u003e \u003cp\u003e2.5 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Supervised Learning Neural Networks.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Neural Network Types.\u003c\/p\u003e \u003cp\u003e3.2 Supervised Learning Rules.\u003c\/p\u003e \u003cp\u003e3.3 Functioning of Hidden Units.\u003c\/p\u003e \u003cp\u003e3.4 Ensemble Neural Networks.\u003c\/p\u003e \u003cp\u003e3.5 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Unsupervised Learning Neural Networks.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Background.\u003c\/p\u003e \u003cp\u003e4.2 Hebbian Learning Rule.\u003c\/p\u003e \u003cp\u003e4.3 Principal Component Learning Rule.\u003c\/p\u003e \u003cp\u003e4.4 Learning Vector Quantizer-I.\u003c\/p\u003e \u003cp\u003e4.5 Self-Organizing Feature Maps.\u003c\/p\u003e \u003cp\u003e4.6 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Radial Basis Function Networks.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Learning Vector Quantizer-II.\u003c\/p\u003e \u003cp\u003e5.2 Radial Basis Function Neural Networks.\u003c\/p\u003e \u003cp\u003e5.3 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Reinforcement Learning.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Learning through Awards.\u003c\/p\u003e \u003cp\u003e6.2 Model-Free Reinforcement LearningModel.\u003c\/p\u003e \u003cp\u003e6.3 Neural Networks and Reinforcement Learning.\u003c\/p\u003e \u003cp\u003e6.4 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Performance Issues (Supervised Learning).\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 PerformanceMeasures.\u003c\/p\u003e \u003cp\u003e7.2 Analysis of Performance.\u003c\/p\u003e \u003cp\u003e7.3 Performance Factors.\u003c\/p\u003e \u003cp\u003e7.4 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III EVOLUTIONARY COMPUTATION.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Introduction to Evolutionary Computation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Generic Evolutionary Algorithm.\u003c\/p\u003e \u003cp\u003e8.2 Representation – The Chromosome.\u003c\/p\u003e \u003cp\u003e8.3 Initial Population.\u003c\/p\u003e \u003cp\u003e8.4 Fitness Function.\u003c\/p\u003e \u003cp\u003e8.5 Selection.\u003c\/p\u003e \u003cp\u003e8.6 Reproduction Operators.\u003c\/p\u003e \u003cp\u003e8.7 Stopping Conditions.\u003c\/p\u003e \u003cp\u003e8.8 Evolutionary Computation versus Classical Optimization.\u003c\/p\u003e \u003cp\u003e8.9 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Genetic Algorithms.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Canonical Genetic Algorithm.\u003c\/p\u003e \u003cp\u003e9.2 Crossover.\u003c\/p\u003e \u003cp\u003e9.3 Mutation.\u003c\/p\u003e \u003cp\u003e9.4 Control Parameters.\u003c\/p\u003e \u003cp\u003e9.5 Genetic Algorithm Variants.\u003c\/p\u003e \u003cp\u003e9.6 Advanced Topics.\u003c\/p\u003e \u003cp\u003e9.7 Applications.\u003c\/p\u003e \u003cp\u003e9.8 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Genetic Programming.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Tree-Based Representation.\u003c\/p\u003e \u003cp\u003e10.2 Initial Population.\u003c\/p\u003e \u003cp\u003e10.3 Fitness Function.\u003c\/p\u003e \u003cp\u003e10.4 Crossover Operators.\u003c\/p\u003e \u003cp\u003e10.5 Mutation Operators.\u003c\/p\u003e \u003cp\u003e10.6 Building Block Genetic Programming.\u003c\/p\u003e \u003cp\u003e10.7 Applications.\u003c\/p\u003e \u003cp\u003e10.8 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Evolutionary Programming.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Basic Evolutionary Programming.\u003c\/p\u003e \u003cp\u003e11.2 Evolutionary Programming Operators.\u003c\/p\u003e \u003cp\u003e11.3 Strategy Parameters.\u003c\/p\u003e \u003cp\u003e11.4 Evolutionary Programming Implementations.\u003c\/p\u003e \u003cp\u003e11.5 Advanced Topics.\u003c\/p\u003e \u003cp\u003e11.6 Applications.\u003c\/p\u003e \u003cp\u003e11.7 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Evolution Strategies.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 (1+1)-ES.\u003c\/p\u003e \u003cp\u003e12.2 Generic Evolution Strategy Algorithm.\u003c\/p\u003e \u003cp\u003e12.3 Strategy Parameters and Self-Adaptation.\u003c\/p\u003e \u003cp\u003e12.4 Evolution Strategy Operators.\u003c\/p\u003e \u003cp\u003e12.5 Evolution Strategy Variants.\u003c\/p\u003e \u003cp\u003e12.6 Advanced Topics.\u003c\/p\u003e \u003cp\u003e12.7 Applications of Evolution Strategies.\u003c\/p\u003e \u003cp\u003e12.8 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Differential Evolution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Basic Differential Evolution.\u003c\/p\u003e \u003cp\u003e13.2 DE\/\u003ci\u003ex\u003c\/i\u003e\/\u003ci\u003ey\u003c\/i\u003e\/\u003ci\u003ez.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.3 Variations to Basic Differential Evolution.\u003c\/p\u003e \u003cp\u003e13.4 Differential Evolution for Discrete-Valued Problems.\u003c\/p\u003e \u003cp\u003e13.5 Advanced Topics.\u003c\/p\u003e \u003cp\u003e13.6 Applications.\u003c\/p\u003e \u003cp\u003e13.7 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Cultural Algorithms.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Culture and Artificial Culture.\u003c\/p\u003e \u003cp\u003e14.2 Basic Cultural Algorithm.\u003c\/p\u003e \u003cp\u003e14.3 Belief Space.\u003c\/p\u003e \u003cp\u003e14.4 Fuzzy Cultural Algorithm.\u003c\/p\u003e \u003cp\u003e14.5 Advanced Topics.\u003c\/p\u003e \u003cp\u003e14.6 Applications.\u003c\/p\u003e \u003cp\u003e14.7 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Coevolution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Coevolution Types.\u003c\/p\u003e \u003cp\u003e15.2 Competitive Coevolution.\u003c\/p\u003e \u003cp\u003e15.3 Cooperative Coevolution.\u003c\/p\u003e \u003cp\u003e15.4 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV COMPUTATIONAL SWARM INTELLIGENCE.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Particle Swarm Optimization.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Basic Particle Swarm Optimization.\u003c\/p\u003e \u003cp\u003e16.2 Social Network Structures.\u003c\/p\u003e \u003cp\u003e16.3 Basic Variations.\u003c\/p\u003e \u003cp\u003e16.4 Basic PSO Parameters.\u003c\/p\u003e \u003cp\u003e16.5 Single-Solution Particle SwarmOptimization.\u003c\/p\u003e \u003cp\u003e16.6 Advanced Topics.\u003c\/p\u003e \u003cp\u003e16.7 Applications.\u003c\/p\u003e \u003cp\u003e16.8 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Ant Algorithms.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Ant Colony OptimizationMeta-Heuristic.\u003c\/p\u003e \u003cp\u003e17.2 Cemetery Organization and Brood Care.\u003c\/p\u003e \u003cp\u003e17.3 Division of Labor.\u003c\/p\u003e \u003cp\u003e17.4 Advanced Topics.\u003c\/p\u003e \u003cp\u003e17.5 Applications.\u003c\/p\u003e \u003cp\u003e17.6 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V ARTIFICIAL IMMUNE SYSTEMS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Natural Immune System.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Classical View.\u003c\/p\u003e \u003cp\u003e18.2 Antibodies and Antigens.\u003c\/p\u003e \u003cp\u003e18.3 TheWhite Cells.\u003c\/p\u003e \u003cp\u003e18.4 Immunity Types.\u003c\/p\u003e \u003cp\u003e18.5 Learning the Antigen Structure.\u003c\/p\u003e \u003cp\u003e18.6 The Network Theory.\u003c\/p\u003e \u003cp\u003e18.7 The Danger Theory.\u003c\/p\u003e \u003cp\u003e18.8 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Artificial Immune Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Artificial Immune System Algorithm.\u003c\/p\u003e \u003cp\u003e19.2 Classical ViewModels.\u003c\/p\u003e \u003cp\u003e19.3 Clonal Selection TheoryModels.\u003c\/p\u003e \u003cp\u003e19.4 Network TheoryModels.\u003c\/p\u003e \u003cp\u003e19.5 Danger TheoryModels.\u003c\/p\u003e \u003cp\u003e19.6 Applications and Other AIS models.\u003c\/p\u003e \u003cp\u003e19.7 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VI FUZZY SYSTEMS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Fuzzy Sets.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Formal Definitions.\u003c\/p\u003e \u003cp\u003e20.2 Membership Functions.\u003c\/p\u003e \u003cp\u003e20.3 Fuzzy Operators.\u003c\/p\u003e \u003cp\u003e20.4 Fuzzy Set Characteristics.\u003c\/p\u003e \u003cp\u003e20.5 Fuzziness and Probability.\u003c\/p\u003e \u003cp\u003e20.6 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Fuzzy Logic and Reasoning.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Fuzzy Logic.\u003c\/p\u003e \u003cp\u003e21.2 Fuzzy Inferencing.\u003c\/p\u003e \u003cp\u003e21.3 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Fuzzy Controllers.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Components of Fuzzy Controllers.\u003c\/p\u003e \u003cp\u003e22.2 Fuzzy Controller Types.\u003c\/p\u003e \u003cp\u003e22.3 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Rough Sets.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23.1 Concept of Discernibility.\u003c\/p\u003e \u003cp\u003e23.2 Vagueness in Rough Sets.\u003c\/p\u003e \u003cp\u003e23.3 Uncertainty in Rough Sets.\u003c\/p\u003e \u003cp\u003e23.4 Assignments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA Optimization Theory.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Basic Ingredients of Optimization Problems.\u003c\/p\u003e \u003cp\u003eA.2 Optimization ProblemClassifications.\u003c\/p\u003e \u003cp\u003eA.3 Optima Types.\u003c\/p\u003e \u003cp\u003eA.4 OptimizationMethod Classes.\u003c\/p\u003e \u003cp\u003eA.5 Unconstrained Optimization.\u003c\/p\u003e \u003cp\u003eA.6 Constrained Optimization.\u003c\/p\u003e \u003cp\u003eA.7 Multi-Solution Problems.\u003c\/p\u003e \u003cp\u003eA.8 Multi-Objective Optimization.\u003c\/p\u003e \u003cp\u003eA.9 Dynamic Optimization Problems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52173819281688,"sku":"9780470035610","price":76.69,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780470035610.jpg?v=1781173328","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/computational-intelligence-an-introduction-hardback-9780470035610","provider":"Freshly Printed Books","version":"1.0","type":"link"}