{"product_id":"differential-evolution-isbn-9780470823927","title":"Differential Evolution","description":"Differential evolution is a very simple but very powerful stochastic optimizer. Since its inception, it has proved very efficient and robust in function optimization and has been applied to solve problems in many scientific and engineering fields. In \u003ci\u003eDifferential Evolution\u003c\/i\u003e , Dr. Qing begins with an overview of optimization, followed by a state-of-the-art review of differential evolution, including its fundamentals and up-to-date advances. He goes on to explore the relationship between differential evolution strategies, intrinsic control parameters, non-intrinsic control parameters, and problem features through a parametric study. Findings and recommendations on the selection of strategies and intrinsic control parameter values are presented. Lastly, after an introductory review of reported applications in electrical and electronic engineering fields, different research groups demonstrate how the methods can be applied to such areas as: multicast routing, multisite mapping in grid environments, antenna arrays, analog electric circuit sizing, electricity markets, stochastic tracking in video sequences, and color quantization.  \u003cul type=\"disc\"\u003e \u003cli\u003eContains a systematic and comprehensive overview of differential evolution\u003c\/li\u003e \u003cli\u003eReviews the latest differential evolution research\u003c\/li\u003e \u003cli\u003eDescribes a comprehensive parametric study conducted over a large test bed\u003c\/li\u003e \u003c\/ul\u003e \u003cul type=\"disc\"\u003e \u003cli\u003eShows how methods can be practically applied to  \u003cul type=\"circle\"\u003e \u003cli\u003emobile communications\u003c\/li\u003e \u003cli\u003egrid computing\u003c\/li\u003e \u003cli\u003ecircuits\u003c\/li\u003e \u003cli\u003eimage processing\u003c\/li\u003e \u003cli\u003epower engineering\u003c\/li\u003e \u003c\/ul\u003e \u003c\/li\u003e \u003cli\u003eSample applications demonstrated by research groups in the United Kingdom, Australia, Italy, Turkey, China, and Eastern Europe\u003c\/li\u003e \u003cli\u003eProvides access to companion website with code examples for download\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eDifferential Evolution\u003c\/i\u003e is ideal for application engineers, who can use the methods described to solve specific engineering problems. It is also a valuable reference for post-graduates and researchers working in evolutionary computation, design optimization and artificial intelligence. Researchers in the optimization field or engineers and managers involved in operations research will also find the book a helpful introduction to the topic.\u003c\/p\u003e  \u003cb\u003ePreface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003eList of Figures.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eList of Tables.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 An Introduction to Optimization.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 A General Optimization Problem.\u003c\/p\u003e \u003cp\u003e1.2 Deterministic Optimization Algorithms.\u003c\/p\u003e \u003cp\u003e1.3 Stochastic Optimization Algorithms.\u003c\/p\u003e \u003cp\u003e1.4 Evolutionary Algorithms.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Fundamentals of Differential Evolution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Differential Evolution at a Glimpse.\u003c\/p\u003e \u003cp\u003e2.2 Classic Differential Evolution.\u003c\/p\u003e \u003cp\u003e2.3 Intrinsic Control Parameters of Differential Evolution.\u003c\/p\u003e \u003cp\u003e2.4 Differential Evolution as an Evolutionary Algorithm.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Advances in Differential Evolution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Handling Mixed Optimization Parameters.\u003c\/p\u003e \u003cp\u003e3.2 Advanced Differential Evolution Strategies.\u003c\/p\u003e \u003cp\u003e3.3 Multi-objective Differential Evolution.\u003c\/p\u003e \u003cp\u003e3.4 Parametric Study on Differential Evolution.\u003c\/p\u003e \u003cp\u003e3.5 Adaptation of Intrinsic Control Parameters of Differential Evolution.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Configuring a Parametric Study on Differential Evolution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Motivations.\u003c\/p\u003e \u003cp\u003e4.2 Objectives.\u003c\/p\u003e \u003cp\u003e4.3 Scopes.\u003c\/p\u003e \u003cp\u003e4.4 Implementation Terminologies.\u003c\/p\u003e \u003cp\u003e4.5 Performance Indicators.\u003c\/p\u003e \u003cp\u003e4.6 Test Bed.\u003c\/p\u003e \u003cp\u003e4.7 Similar Works.\u003c\/p\u003e \u003cp\u003e4.8 A Comparative Study.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Benchmarking a Single-Objective Optimization Test Bed for Parametric Study on Differential Evolution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Motivation.\u003c\/p\u003e \u003cp\u003e5.2 A Survey on Test Problems.\u003c\/p\u003e \u003cp\u003e5.3 Generating New Test Problems.\u003c\/p\u003e \u003cp\u003e5.4 Tentative Benchmark Test Bed.\u003c\/p\u003e \u003cp\u003e5.5 An Overview of Numerical Simulation.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Differential Evolution Strategies.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Sphere Function.\u003c\/p\u003e \u003cp\u003e6.2 Step Function 2.\u003c\/p\u003e \u003cp\u003e6.3 Hyper-ellipsoid Function.\u003c\/p\u003e \u003cp\u003e6.4 Qing Function.\u003c\/p\u003e \u003cp\u003e6.5 Schwefel Function 2.22.\u003c\/p\u003e \u003cp\u003e6.6 Schwefel Function 2.26.\u003c\/p\u003e \u003cp\u003e6.7 Schwefel Function 1.2.\u003c\/p\u003e \u003cp\u003e6.8 Rastrigin Function.\u003c\/p\u003e \u003cp\u003e6.9 Ackley Function.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Optimal Intrinsic Control Parameters.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Sphere Function.\u003c\/p\u003e \u003cp\u003e7.2 Step Function 2.\u003c\/p\u003e \u003cp\u003e7.3 Hyper-ellipsoid Function.\u003c\/p\u003e \u003cp\u003e7.4 Qing Function.\u003c\/p\u003e \u003cp\u003e7.5 Schwefel Function 2.22.\u003c\/p\u003e \u003cp\u003e7.6 Schwefel Function 2.26.\u003c\/p\u003e \u003cp\u003e7.7 Schwefel Function 1.2.\u003c\/p\u003e \u003cp\u003e7.8 Rastrigin Function.\u003c\/p\u003e \u003cp\u003e7.9 Ackley Function.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Non-Intrinsic Control Parameters.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.2 Alternative Search Space.\u003c\/p\u003e \u003cp\u003e8.3 Performance of Differential Evolution.\u003c\/p\u003e \u003cp\u003e8.4 Optimal Population Size and Safeguard Zone.\u003c\/p\u003e \u003cp\u003e8.5 Optimal Mutation Intensity and Crossover Probability for Sphere Function.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 An Introductory Survey on Differential Evolution in Electrical and Electronic Engineering.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Communication.\u003c\/p\u003e \u003cp\u003e9.2 Computer Engineering.\u003c\/p\u003e \u003cp\u003e9.3 Control Theory and Engineering.\u003c\/p\u003e \u003cp\u003e9.4 Electrical Engineering.\u003c\/p\u003e \u003cp\u003e9.5 Electromagnetics.\u003c\/p\u003e \u003cp\u003e9.6 Electronics.\u003c\/p\u003e \u003cp\u003e9.7 Magnetics.\u003c\/p\u003e \u003cp\u003e9.8 Power Engineering.\u003c\/p\u003e \u003cp\u003e9.9 Signal and Information Processing.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Flexible QoS Multicast Routing in Next-Generation Internet.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction.\u003c\/p\u003e \u003cp\u003e10.2 Mathematical Models.\u003c\/p\u003e \u003cp\u003e10.3 Performance Evaluation.\u003c\/p\u003e \u003cp\u003e10.4 Conclusions.\u003c\/p\u003e \u003cp\u003e10.5 Acknowledgement.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Multisite Mapping onto Grid Environments.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction.\u003c\/p\u003e \u003cp\u003e11.2 Working Environment.\u003c\/p\u003e \u003cp\u003e11.3 Differential Evolution for Grid Mapping.\u003c\/p\u003e \u003cp\u003e11.4 Experiments in Predetermined Conditions.\u003c\/p\u003e \u003cp\u003e11.5 More Realistic Experiments.\u003c\/p\u003e \u003cp\u003e11.6 Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Synthesis of Time-Modulated Antenna Arrays.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction.\u003c\/p\u003e \u003cp\u003e12.2 Antenna Arrays.\u003c\/p\u003e \u003cp\u003e12.3 Synthesis of Multiple Patterns from Time-Modulated Arrays.\u003c\/p\u003e \u003cp\u003e12.4 Pattern Synthesis of Time-Modulated Planar Arrays.\u003c\/p\u003e \u003cp\u003e12.5 Adaptive Nulling with Time-Modulated Antenna Arrays.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Automated Analog Electronic Circuits Sizing.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction.\u003c\/p\u003e \u003cp\u003e13.2 Cost Function.\u003c\/p\u003e \u003cp\u003e13.3 Hybrid Differential Evolution.\u003c\/p\u003e \u003cp\u003e13.4 Device Sizing.\u003c\/p\u003e \u003cp\u003e13.5 Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Strategic Bidding in a Competitive Electricity Market.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Electrical Energy Market.\u003c\/p\u003e \u003cp\u003e14.2 Bidding Strategies in an Electricity Market.\u003c\/p\u003e \u003cp\u003e14.3 Application of Differential Evolution in Strategic Bidding Systems.\u003c\/p\u003e \u003cp\u003e14.4 Case Study.\u003c\/p\u003e \u003cp\u003e14.5 Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 3D Tracking of License Plates in Video Sequences.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction.\u003c\/p\u003e \u003cp\u003e15.2 3D License Plate Tracking Acquisition Setup.\u003c\/p\u003e \u003cp\u003e15.3 Statistical Bayesian Estimation and Particle Filtering.\u003c\/p\u003e \u003cp\u003e15.4 3D License Plate Tracking Using DEMC Particle Filter.\u003c\/p\u003e \u003cp\u003e15.5 Comparison.\u003c\/p\u003e \u003cp\u003e15.6 Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Color Quantization.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction.\u003c\/p\u003e \u003cp\u003e16.2 Differential Evolution Based Color Map Generation.\u003c\/p\u003e \u003cp\u003e16.3 Hybrid Differential Evolution for Color Map Generation.\u003c\/p\u003e \u003cp\u003e16.4 Experimental Results.\u003c\/p\u003e \u003cp\u003e16.5 Conclusions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e \u003cb\u003eAnyong Qing\u003c\/b\u003e is a Research Scientist with Temasek Laboratories at the National University of Singapore. He has been involved in various areas of research in electromagnetics and evolutionary computation, producing pioneering work in solving electromagnetic problems using evolutionary algorithms. Qing has authored 4 book chapters, 49 peer reviewed journal papers, and 37 conference presentations, and altogether has been cited by other researchers over 150 times. He was invited to contribute a review on electromagnetic inverse problems for the Wiley Encyclopedia of RF and Microwave Engineering. He is also an invited speaker for EuMW, IST, PIERS, etc. Qing received a Guest Professorship at \u003cst1:placename w:st=\"on\"\u003e\u003cst1:place w:st=\"on\"\u003eSouthwest\u003c\/st1:place\u003e \u003cst1:placename w:st=\"on\"\u003eJiaotong\u003c\/st1:placename\u003e \u003cst1:placetype w:st=\"on\"\u003eUniversity\u003c\/st1:placetype\u003e\u003c\/st1:placename\u003e and was elected as a senior member of the IEEE in 2005. Qing holds a B.E from \u003cst1:placename w:st=\"on\"\u003eTsinghua\u003c\/st1:placename\u003e \u003cst1:placetype w:st=\"on\"\u003eUniversity\u003c\/st1:placetype\u003e and a PhD from \u003cst1:place w:st=\"on\"\u003eSouthwest Jiaotong\u003c\/st1:place\u003e.  Differential evolution is a very simple but very powerful stochastic optimizer. Since its inception, it has proved very efficient and robust in function optimization and has been applied to solve problems in many scientific and engineering fields. In \u003ci\u003eDifferential Evolution\u003c\/i\u003e, Dr. Qing begins with an overview of optimization, followed by a state-of-the-art review of differential evolution, including its fundamentals and up-to-date advances. He goes on to explore the relationship between differential evolution strategies, intrinsic control parameters, non-intrinsic control parameters, and problem features through a parametric study. Findings and recommendations on the selection of strategies and intrinsic control parameter values are presented. Lastly, after an introductory review of reported applications in electrical and electronic engineering fields, different research groups demonstrate how the methods can be applied to such areas as: multicast routing, multisite mapping in grid environments, antenna arrays, analog electric circuit sizing, electricity markets, stochastic tracking in video sequences, and color quantization.  \u003cul type=\"disc\"\u003e \u003cli\u003eContains a systematic and comprehensive overview of differential evolution\u003c\/li\u003e \u003cli\u003eReviews the latest differential evolution research\u003c\/li\u003e \u003cli\u003eDescribes a comprehensive parametric study conducted over a large test bed\u003c\/li\u003e \u003c\/ul\u003e \u003cul type=\"disc\"\u003e \u003cli\u003eShows how methods can be practically applied to  \u003cul type=\"circle\"\u003e \u003cli\u003emobile communications\u003c\/li\u003e \u003cli\u003egrid computing\u003c\/li\u003e \u003cli\u003ecircuits\u003c\/li\u003e \u003cli\u003eimage processing\u003c\/li\u003e \u003cli\u003epower engineering\u003c\/li\u003e \u003c\/ul\u003e \u003c\/li\u003e \u003cli\u003eSample applications demonstrated by research groups in the United Kingdom, Australia, Italy, Turkey, China, and Eastern Europe\u003c\/li\u003e \u003cli\u003eProvides access to companion website with code examples for download\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eDifferential Evolution\u003c\/i\u003e is ideal for application engineers, who can use the methods described to solve specific engineering problems. It is also a valuable reference for post-graduates and researchers working in evolutionary computation, design optimization and artificial intelligence. Researchers in the optimization field or engineers and managers involved in operations research will also find the book a helpful introduction to the topic.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989062664421,"sku":"NP9780470823927","price":203.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470823927.jpg?v=1761782638","url":"https:\/\/k12savings.com\/products\/differential-evolution-isbn-9780470823927","provider":"K12savings","version":"1.0","type":"link"}