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• An overview of evolutionary algorithms and their advantages
• evolving models of time series
• evolutionary clustering and classification
• evolving control systems
• theory and tools for improving evolutionary algorithms.
• (source: Nielsen Book Data)

Evolutionary cmputation is one of the fastest growing areas of computer science, partly because of its broad applicability to engineering problems. The methods can be applied to problems as diverse as supply-chain optimization, routing and planning, task assignment, pharmaceutical design, interactive gaming, and many others within the signal processing domain. The text is an outgrowth of a series of SPIE short courses taught by the author. The examples span a range of applications and should be useful to a variety of readers of mixed backgrounds and expertise.
(source: Nielsen Book Data)

• Preface xiii PART I BASIC CONCEPTS AND LITERATURE REVIEW 1
• 1 INTRODUCTION TO MOBILE AD HOC NETWORKS 3 1.1 Mobile Ad Hoc Networks 6 1.2 Vehicular Ad Hoc Networks 9 1.2.1 Wireless Access in Vehicular Environment (WAVE) 11 1.2.2 Communication Access for Land Mobiles (CALM) 12 1.2.3 C2C Network 13 1.3 Sensor Networks 14 1.3.1 IEEE 1451 17 1.3.2 IEEE 802.15.4 17 1.3.3 ZigBee 18 1.3.4 6LoWPAN 19 1.3.5 Bluetooth 19 1.3.6 Wireless Industrial Automation System 20 1.4 Conclusion 20 References 21
• 2 INTRODUCTION TO EVOLUTIONARY ALGORITHMS 27 2.1 Optimization Basics 28 2.2 Evolutionary Algorithms 29 2.3 Basic Components of Evolutionary Algorithms 32 2.3.1 Representation 32 2.3.2 Fitness Function 32 2.3.3 Selection 32 2.3.4 Crossover 33 2.3.5 Mutation 34 2.3.6 Replacement 35 2.3.7 Elitism 35 2.3.8 Stopping Criteria 35 2.4 Panmictic Evolutionary Algorithms 36 2.4.1 Generational EA 36 2.4.2 Steady-State EA 36 2.5 Evolutionary Algorithms with Structured Populations 36 2.5.1 Cellular EAs 37 2.5.2 Cooperative Coevolutionary EAs 38 2.6 Multi-Objective Evolutionary Algorithms 39 2.6.1 Basic Concepts in Multi-Objective Optimization 40 2.6.2 Hierarchical Multi-Objective Problem Optimization 42 2.6.3 Simultaneous Multi-Objective Problem Optimization 43 2.7 Conclusion 44 References 45
• 3 SURVEY ON OPTIMIZATION PROBLEMS FOR MOBILE AD HOC NETWORKS 49 3.1 Taxonomy of the Optimization Process 51 3.1.1 Online and Offline Techniques 51 3.1.2 Using Global or Local Knowledge 52 3.1.3 Centralized and Decentralized Systems 52 3.2 State of the Art 53 3.2.1 Topology Management 53 3.2.2 Broadcasting Algorithms 58 3.2.3 Routing Protocols 59 3.2.4 Clustering Approaches 63 3.2.5 Protocol Optimization 64 3.2.6 Modeling the Mobility of Nodes 65 3.2.7 Selfish Behaviors 66 3.2.8 Security Issues 67 3.2.9 Other Applications 67 3.3 Conclusion 68 References 69
• 4 MOBILE NETWORKS SIMULATION 79 4.1 Signal Propagation Modeling 80 4.1.1 Physical Phenomena 81 4.1.2 Signal Propagation Models 85 4.2 State of the Art of Network Simulators 89 4.2.1 Simulators 89 4.2.2 Analysis 92 4.3 Mobility Simulation 93 4.3.1 Mobility Models 93 4.3.2 State of the Art of Mobility Simulators 96 4.4 Conclusion 98 References 98 PART II PROBLEMS OPTIMIZATION 105
• 5 PROPOSED OPTIMIZATION FRAMEWORK 107 5.1 Architecture 108 5.2 Optimization Algorithms 110 5.2.1 Single-Objective Algorithms 110 5.2.2 Multi-Objective Algorithms 115 5.3 Simulators 121 5.3.1 Network Simulator: ns-3 121 5.3.2 Mobility Simulator: SUMO 123 5.3.3 Graph-Based Simulations 126 5.4 Experimental Setup 127 5.5 Conclusion 131 References 131
• 6 BROADCASTING PROTOCOL 135 6.1 The Problem 136 6.1.1 DFCN Protocol 136 6.1.2 Optimization Problem Definition 138 6.2 Experiments 140 6.2.1 Algorithm Configurations 140 6.2.2 Comparison of the Performance of the Algorithms 141 6.3 Analysis of Results 142 6.3.1 Building a Representative Subset of Best Solutions 143 6.3.2 Interpretation of the Results 145 6.3.3 Selected Improved DFCN Configurations 148 6.4 Conclusion 150 References 151
• 7 ENERGY MANAGEMENT 153 7.1 The Problem 154 7.1.1 AEDB Protocol 154 7.1.2 Optimization Problem Definition 156 7.2 Experiments 159 7.2.1 Algorithm Configurations 159 7.2.2 Comparison of the Performance of the Algorithms 160 7.3 Analysis of Results 161 7.4 Selecting Solutions from the Pareto Front 164 7.4.1 Performance of the Selected Solutions 167 7.5 Conclusion 170 References 171
• 8 NETWORK TOPOLOGY 173 8.1 The Problem 175 8.1.1 Injection Networks 175 8.1.2 Optimization Problem Definition 176 8.2 Heuristics 178 8.2.1 Centralized 178 8.2.2 Distributed 179 8.3 Experiments 180 8.3.1 Algorithm Configurations 180 8.3.2 Comparison of the Performance of the Algorithms 180 8.4 Analysis of Results 183 8.4.1 Analysis of the Objective Values 183 8.4.2 Comparison with Heuristics 185 8.5 Conclusion 187 References 188
• 9 REALISTIC VEHICULAR MOBILITY 191 9.1 The Problem 192 9.1.1 Vehicular Mobility Model 192 9.1.2 Optimization Problem Definition 196 9.2 Experiments 199 9.2.1 Algorithms Configuration 199 9.2.2 Comparison of the Performance of the Algorithms 200 9.3 Analysis of Results 202 9.3.1 Analysis of the Decision Variables 202 9.3.2 Analysis of the Objective Values 204 9.4 Conclusion 206 References 206
• 10 SUMMARY AND DISCUSSION 209 10.1 A New Methodology for Optimization in Mobile Ad Hoc Networks 211 10.2 Performance of the Three Algorithmic Proposals 213 10.2.1 Broadcasting Protocol 213 10.2.2 Energy-Efficient Communications 214 10.2.3 Network Connectivity 214 10.2.4 Vehicular Mobility 215 10.3 Global Discussion on the Performance of the Algorithms 215 10.3.1 Single-Objective Case 216 10.3.2 Multi-Objective Case 217 10.4 Conclusion 218 References 218 INDEX 221.
• (source: Nielsen Book Data)

Describes how evolutionary algorithms (EAs) can be used to identify, model, and minimize day-to-day problems that arise for researchers in optimization and mobile networking Mobile ad hoc networks (MANETs), vehicular networks (VANETs), sensor networks (SNs), and hybrid networks each of these require a designer s keen sense and knowledge of evolutionary algorithms in order to help with the common issues that plague professionals involved in optimization and mobile networking. This book introduces readers to both mobile ad hoc networks and evolutionary algorithms, presenting basic concepts as well as detailed descriptions of each. It demonstrates how metaheuristics and evolutionary algorithms (EAs) can be used to help provide low-cost operations in the optimization process allowing designers to put some intelligence or sophistication into the design. It also offers efficient and accurate information on dissemination algorithms, topology management, and mobility models to address challenges in the field. Evolutionary Algorithms for Mobile Ad Hoc Networks: * Instructs on how to identify, model, and optimize solutions to problems that arise in daily research * Presents complete and up-to-date surveys on topics like network and mobility simulators * Provides sample problems along with solutions/descriptions used to solve each, with performance comparisons * Covers current, relevant issues in mobile networks, like energy use, broadcasting performance, device mobility, and more Evolutionary Algorithms for Mobile Ad Hoc Networks is an ideal book for researchers and students involved in mobile networks, optimization, advanced search techniques, and multi-objective optimization.
(source: Nielsen Book Data)

• Preface xiii PART I BASIC CONCEPTS AND LITERATURE REVIEW 1
• 1 INTRODUCTION TO MOBILE AD HOC NETWORKS 3 1.1 Mobile Ad Hoc Networks 6 1.2 Vehicular Ad Hoc Networks 9 1.2.1 Wireless Access in Vehicular Environment (WAVE) 11 1.2.2 Communication Access for Land Mobiles (CALM) 12 1.2.3 C2C Network 13 1.3 Sensor Networks 14 1.3.1 IEEE 1451 17 1.3.2 IEEE 802.15.4 17 1.3.3 ZigBee 18 1.3.4 6LoWPAN 19 1.3.5 Bluetooth 19 1.3.6 Wireless Industrial Automation System 20 1.4 Conclusion 20 References 21
• 2 INTRODUCTION TO EVOLUTIONARY ALGORITHMS 27 2.1 Optimization Basics 28 2.2 Evolutionary Algorithms 29 2.3 Basic Components of Evolutionary Algorithms 32 2.3.1 Representation 32 2.3.2 Fitness Function 32 2.3.3 Selection 32 2.3.4 Crossover 33 2.3.5 Mutation 34 2.3.6 Replacement 35 2.3.7 Elitism 35 2.3.8 Stopping Criteria 35 2.4 Panmictic Evolutionary Algorithms 36 2.4.1 Generational EA 36 2.4.2 Steady-State EA 36 2.5 Evolutionary Algorithms with Structured Populations 36 2.5.1 Cellular EAs 37 2.5.2 Cooperative Coevolutionary EAs 38 2.6 Multi-Objective Evolutionary Algorithms 39 2.6.1 Basic Concepts in Multi-Objective Optimization 40 2.6.2 Hierarchical Multi-Objective Problem Optimization 42 2.6.3 Simultaneous Multi-Objective Problem Optimization 43 2.7 Conclusion 44 References 45
• 3 SURVEY ON OPTIMIZATION PROBLEMS FOR MOBILE AD HOC NETWORKS 49 3.1 Taxonomy of the Optimization Process 51 3.1.1 Online and Offline Techniques 51 3.1.2 Using Global or Local Knowledge 52 3.1.3 Centralized and Decentralized Systems 52 3.2 State of the Art 53 3.2.1 Topology Management 53 3.2.2 Broadcasting Algorithms 58 3.2.3 Routing Protocols 59 3.2.4 Clustering Approaches 63 3.2.5 Protocol Optimization 64 3.2.6 Modeling the Mobility of Nodes 65 3.2.7 Selfish Behaviors 66 3.2.8 Security Issues 67 3.2.9 Other Applications 67 3.3 Conclusion 68 References 69
• 4 MOBILE NETWORKS SIMULATION 79 4.1 Signal Propagation Modeling 80 4.1.1 Physical Phenomena 81 4.1.2 Signal Propagation Models 85 4.2 State of the Art of Network Simulators 89 4.2.1 Simulators 89 4.2.2 Analysis 92 4.3 Mobility Simulation 93 4.3.1 Mobility Models 93 4.3.2 State of the Art of Mobility Simulators 96 4.4 Conclusion 98 References 98 PART II PROBLEMS OPTIMIZATION 105
• 5 PROPOSED OPTIMIZATION FRAMEWORK 107 5.1 Architecture 108 5.2 Optimization Algorithms 110 5.2.1 Single-Objective Algorithms 110 5.2.2 Multi-Objective Algorithms 115 5.3 Simulators 121 5.3.1 Network Simulator: ns-3 121 5.3.2 Mobility Simulator: SUMO 123 5.3.3 Graph-Based Simulations 126 5.4 Experimental Setup 127 5.5 Conclusion 131 References 131
• 6 BROADCASTING PROTOCOL 135 6.1 The Problem 136 6.1.1 DFCN Protocol 136 6.1.2 Optimization Problem Definition 138 6.2 Experiments 140 6.2.1 Algorithm Configurations 140 6.2.2 Comparison of the Performance of the Algorithms 141 6.3 Analysis of Results 142 6.3.1 Building a Representative Subset of Best Solutions 143 6.3.2 Interpretation of the Results 145 6.3.3 Selected Improved DFCN Configurations 148 6.4 Conclusion 150 References 151
• 7 ENERGY MANAGEMENT 153 7.1 The Problem 154 7.1.1 AEDB Protocol 154 7.1.2 Optimization Problem Definition 156 7.2 Experiments 159 7.2.1 Algorithm Configurations 159 7.2.2 Comparison of the Performance of the Algorithms 160 7.3 Analysis of Results 161 7.4 Selecting Solutions from the Pareto Front 164 7.4.1 Performance of the Selected Solutions 167 7.5 Conclusion 170 References 171
• 8 NETWORK TOPOLOGY 173 8.1 The Problem 175 8.1.1 Injection Networks 175 8.1.2 Optimization Problem Definition 176 8.2 Heuristics 178 8.2.1 Centralized 178 8.2.2 Distributed 179 8.3 Experiments 180 8.3.1 Algorithm Configurations 180 8.3.2 Comparison of the Performance of the Algorithms 180 8.4 Analysis of Results 183 8.4.1 Analysis of the Objective Values 183 8.4.2 Comparison with Heuristics 185 8.5 Conclusion 187 References 188
• 9 REALISTIC VEHICULAR MOBILITY 191 9.1 The Problem 192 9.1.1 Vehicular Mobility Model 192 9.1.2 Optimization Problem Definition 196 9.2 Experiments 199 9.2.1 Algorithms Configuration 199 9.2.2 Comparison of the Performance of the Algorithms 200 9.3 Analysis of Results 202 9.3.1 Analysis of the Decision Variables 202 9.3.2 Analysis of the Objective Values 204 9.4 Conclusion 206 References 206
• 10 SUMMARY AND DISCUSSION 209 10.1 A New Methodology for Optimization in Mobile Ad Hoc Networks 211 10.2 Performance of the Three Algorithmic Proposals 213 10.2.1 Broadcasting Protocol 213 10.2.2 Energy-Efficient Communications 214 10.2.3 Network Connectivity 214 10.2.4 Vehicular Mobility 215 10.3 Global Discussion on the Performance of the Algorithms 215 10.3.1 Single-Objective Case 216 10.3.2 Multi-Objective Case 217 10.4 Conclusion 218 References 218 INDEX 221.
• (source: Nielsen Book Data)

Describes how evolutionary algorithms (EAs) can be used to identify, model, and minimize day-to-day problems that arise for researchers in optimization and mobile networking Mobile ad hoc networks (MANETs), vehicular networks (VANETs), sensor networks (SNs), and hybrid networks each of these require a designer s keen sense and knowledge of evolutionary algorithms in order to help with the common issues that plague professionals involved in optimization and mobile networking. This book introduces readers to both mobile ad hoc networks and evolutionary algorithms, presenting basic concepts as well as detailed descriptions of each. It demonstrates how metaheuristics and evolutionary algorithms (EAs) can be used to help provide low-cost operations in the optimization process allowing designers to put some intelligence or sophistication into the design. It also offers efficient and accurate information on dissemination algorithms, topology management, and mobility models to address challenges in the field. Evolutionary Algorithms for Mobile Ad Hoc Networks: * Instructs on how to identify, model, and optimize solutions to problems that arise in daily research * Presents complete and up-to-date surveys on topics like network and mobility simulators * Provides sample problems along with solutions/descriptions used to solve each, with performance comparisons * Covers current, relevant issues in mobile networks, like energy use, broadcasting performance, device mobility, and more Evolutionary Algorithms for Mobile Ad Hoc Networks is an ideal book for researchers and students involved in mobile networks, optimization, advanced search techniques, and multi-objective optimization.
(source: Nielsen Book Data)

This book consitutes the refereed joint proceedings of the First European Workshop on Evolutionary Computation in Image Analysis and Signal Processing, EvoIASP '99 and of the First European Workshop on Evolutionary Telecommunications, EuroEcTel '99, held in Goteborg, Sweden in May 1999. The 18 revised full papers presented were carefully reviewed and selected for inclusion in the volume. The book presents state-of-the-art research results applying techniques from evolutionary computing in the specific application areas.
(source: Nielsen Book Data)

• Overview of applications in power and energy systems
• Power system planning and operation
• Power system and power plant control
• Distribution system
• Integration of renewable energy in smart grid
• Electricity markets.

Reviews state-of-the-art technologies in modern heuristic optimization techniques and presents case studies showing how they have been applied in complex power and energy systems problems Written by a team of international experts, this book describes the use of metaheuristic applications in the analysis and design of electric power systems. This includes a discussion of optimum energy and commitment of generation (nonrenewable & renewable) and load resources during day-to-day operations and control activities in regulated and competitive market structures, along with transmission and distribution systems. Applications of Modern Heuristic Optimization Methods in Power and Energy Systems begins with an introduction and overview of applications in power and energy systems before moving on to planning and operation, control, and distribution. Further chapters cover the integration of renewable energy and the smart grid and electricity markets. The book finishes with final conclusions drawn by the editors. Applications of Modern Heuristic Optimization Methods in Power and Energy Systems: Explains the application of differential evolution in electric power systems' active power multi-objective optimal dispatch Includes studies of optimization and stability in load frequency control in modern power systems Describes optimal compliance of reactive power requirements in near-shore wind power plants Features contributions from noted experts in the field Ideal for power and energy systems designers, planners, operators, and consultants, Applications of Modern Heuristic Optimization Methods in Power and Energy Systems will also benefit engineers, software developers, researchers, academics, and students.
(source: Nielsen Book Data)

• Preface. Contributors.
• Part 1: Theory of Modern Heuristic Optimization. 1. Introduction to Evolutionary Computation (David B. Fogel) 2. Fundamentals of Genetic Algorithms (Alexandre P. Alves da Silva and Djalma M. Falcao) 3. Fundamentals of Evolution Strategies and Evolutionary Programming (Vladimiro Miranda) 4. Fundamentals of Particle Swarm Optimization Techniques (Yoshikazu Fukuyama) 5. Fundamentals of Ant Colony Search Algorithms (Yong-Hua Song, Haiyan Lu, Kwang Y. Lee, and I.K. Yu) 6. Fundamentals of Tabu Search (Alcir J. Monticelli, Ruben Romero, and Eduardo Nobuhiro Asada) 7. Fundamentals of Simulated Annealing (Alcir J. Monticelli, Ruben Romero, and Eduardo Nobuhiro Asada) 8. Fuzzy Systems (Germano Lambert-Torres) 9. Differential Evolution, an Alternative Approach to Evolutionary Algorithm (Kit Po Wong and Zhao Yang Dong) 10. Pareto Multiobjective Optimization (Patrick N. Ngatchou, Anahita Zarei, Warren L. J. Fox, and Mohamed A. El-Sharkawi) 11. Trust-Tech Paradigm for Computing High-Quality Optimal Solutions: Method and Theory (Hsiao-Dong Chaing and Jaewook Lee)
• Part 2: Selected Applications of Modern Heuristic Optimization in Power Systems. 12. Overview of Applications in Power Systems (Alexandre P. Alves da Silva, Djalma M. Falcao, and Kwang Y. Lee) 13. Application of Evolutionary Technique to Power System Vulnerability Assessment (Mingoo Kim, Mohamed A. el-Sharkawi, Robert J. Marks, and Ioannis N. Kassabalidis) 14. Applications to System Planning (Eduardo Nobuhiro Asada, Youngjae Jeon, Kwang Y. Lee, Vladimiro Miranda, Alcir J. Monticelli, Koichi Nara, Jong-Bae Park, Ruben Romero, and Yong-Hua Song) 15. Applications to Power System Scheduling (Koay Chin Aik, Loi Lei Lai, Kwang Y. Lee, Haiyan Lu, Jong-Bae Park, Young-Hua Song, Dipti Srinivasan, John G. Vlachogiannis, and I.K. Yu) 16. Power System Controls (Yoshikazu Fukuyama, Hamid Ghezelayagh, Kwang Y. Lee, Chen-Ching Liu, Yong-Hua Song, and Ying Xiao) 17. Genetic Algorithms for Solving Optimal Power Flow Problems (Loi Lei Lai and Nidul Sinha) 18. An Interactive Compromise Programming-Based Multiobjective Approach to FACTS Control (Ying Xiao, Young-Hua Song, and Chen-Ching Liu) 19. Hybrid Systems (Vladimiro Miranda) Index.
• (source: Nielsen Book Data)

This book explores how developing solutions with heuristic tools offers two major advantages: shortened development time and more robust systems. It begins with an overview of modern heuristic techniques and goes on to cover specific applications of heuristic approaches to power system problems, such as security assessment, optimal power flow, power system scheduling and operational planning, power generation expansion planning, reactive power planning, transmission and distribution planning, network reconfiguration, power system control, and hybrid systems of heuristic methods.
(source: Nielsen Book Data)

• PREFACE. ACKNOWLEDGMENTS. ACRONYMS.
• 1 INTRODUCTION. 1.1 Characteristics of Evolvable Circuits and Systems. 1.2 Why Evolvable Hardware Is Good (and Bad!). 1.3 Technology. 1.4 Evolvable Hardware vs. Evolved Hardware. 1.5 Intrinsic vs. Extrinsic Evolution. 1.6 Online vs. Offline Evolution. 1.7 Evolvable Hardware Applications. References.
• 2 FUNDAMENTALS OF EVOLUTIONARY COMPUTATION. 2.1 What Is an EA? 2.2 Components of an EA. 2.2.1 Representation. 2.2.2 Variation. 2.2.3 Evaluation. 2.2.4 Selection. 2.2.5 Population. 2.2.6 Termination Criteria. 2.3 Getting the EA to Work. 2.4 Which EA Is Best? References.
• 3 RECONFIGURABLE DIGITAL DEVICES. 3.1 Basic Architectures. 3.1.1 Programmable Logic Devices. 3.1.2 Field Programmable Gate Array. 3.2 Using Reconfigurable Hardware. 3.2.1 Design Phase. 3.2.2 Execution Phase. 3.3 Experimental Results. 3.4 Functional Overview of the POEtic Architecture. 3.4.1 Organic Subsystem. 3.4.2 Description of the Molecules. 3.4.3 Description of the Routing Layer. 3.4.4 Dynamic Routing. 3.5 Remarks. References.
• 4 RECONFIGURABLE ANALOG DEVICES. 4.1 Basic Architectures. 4.2 Transistor Arrays. 4.2.1 The NASA FTPA. 4.2.2 The Heidelberg FPTA. 4.3 Analog Arrays. 4.4 Remarks. References.
• 5 PUTTING EVOLVABLE HARDWARE TO USE. 5.1 Synthesis vs. Adaption. 5.2 Designing Self-Adaptive Systems. 5.2.1 Fault Tolerant Systems. 5.2.2 Real-Time Systems. 5.3 Creating Fault Tolerant Systems Using EHW. 5.4 Why Intrinsic Reconfiguration for Online Systems? 5.5 Quantifying Intrinsic Reconfiguration Time. 5.6 Putting Theory Into Practice. 5.6.1 Minimizing Risk With Anticipated Faults. 5.6.2 Minimizing Risk With Unanticipated Faults. 5.6.3 Suggested Practices. 5.7 Examples of EHW-Based Fault Recovery. 5.7.1 Population vs. Fitness-Based Designs. 5.7.2 EHW Compensators. 5.7.3 Robot Control. 5.7.4 The POEtic Project. 5.7.5 Embryo Development. 5.8 Remarks. References.
• 6 FUTURE WORK. 6.1 Circuit Synthesis Topics. 6.1.1 Digital Design. 6.1.2 Analog Design. 6.2 Circuit Adaption Topics. References. INDEX . ABOUT THE AUTHORS.
• (source: Nielsen Book Data)

"Introduction to Evolvable Hardware: A Practical Guide for Designing Self-Adaptive Systems" provides a fundamental introduction for engineers, designers, and managers involved in the development of adaptive, high reliability systems. It also introduces the concepts of evolvable hardware (EHW) to new researchers in a structured way. With this practical book, you'll be able to quickly apply the techniques presented to existing design problems.
(source: Nielsen Book Data)

Focusing on architecture/parallel and high-performance computing, this book should be of interest to researchers, professors, practitioners, students, and other professionals.
(source: Nielsen Book Data)

• Part I Basic principles: introduction
• evolutionary algorithms
• characteristics of problem instances
• performance evaluation. Part 2 Practice: implementation
• applications of EAs
• heuristic learning
• conclusions.
• (source: Nielsen Book Data)

In VLSI CAD, difficult optimization problems have to be solved on a constant basis. Various optimization techniques have been proposed in the past. While some of these methods have been shown to work well in applications and have become somewhat established over the years, other techniques have been ignored. Recently, there has been a growing interest in optimization algorithms based on principles observed in nature, termed Evolutionary Algorithms (EAs). "Evolutionary Algorithms in VLSI CAD" presents the basic concepts of EAs, and considers the application of EAs in VLSI CAD. It is the first book to show how EAs could be used to improve IC design tools and processes. Several successful applications from different areas of circuit design, like logic synthesis, mapping and testing, are described in detail."Evolutionary Algorithms in VLSI CAD" consists of two parts. The first part discusses basic principles of EAs and provides some easy-to-understand examples. Furthermore, a theoretical model for multi-objective optimization is presented. In the second part a software implementation of EAs is supplied together with detailed descriptions of several EA applications. These applications cover a wide range of VLSI CAD, and different methods for using EAs are described. "Evolutionary Algorithms in VLSI CAD" is intended for CAD developers and researchers as well as those working in evolutionary algorithms and techniques supporting modern design tools and processes.
(source: Nielsen Book Data)

• Fuzzy Logic.- Neural Network.- Support Vector Machine.- Signal Processing.
• (source: Nielsen Book Data)

This highly experienced author sets out to build a bridge between two inter-disciplinary power engineering practices. The book looks into two major fields used in modern power systems: intelligent systems and the signal processing. The intelligent systems section comprises fuzzy logic, neural network and support vector machine. The author looks at relevant theories on the topics without assuming much particular background. Following the theoretical basics, he studies their applications in various problems in power engineering, like, load forecasting, phase balancing, or disturbance analysis.
(source: Nielsen Book Data)

Evolutionary computing, inspired by the biological world, is one of the emergent technologies of our time. Being essentially a software activity, it has been successfully applied, e.g. for optimization and machine learning in various areas. The tremendous increase in computational power and, more recently, the appearance of a new generation of programmable logic devices allow for a new approach to designing computing machines inspired by biological models: it is now possible to make the hardware itself evolve.This book is based on a workshop on evolvable hardware, held in Lausanne, Switzerland, in October 1995. It reports the state of the art of research in this field and presents two introductory chapters, written with the novice reader in mind.
(source: Nielsen Book Data)

• Fuzzy Logic.- Neural Network.- Support Vector Machine.- Signal Processing.
• (source: Nielsen Book Data)

This highly experienced author sets out to build a bridge between two inter-disciplinary power engineering practices. The book looks into two major fields used in modern power systems: intelligent systems and the signal processing. The intelligent systems section comprises fuzzy logic, neural network and support vector machine. The author looks at relevant theories on the topics without assuming much particular background. Following the theoretical basics, he studies their applications in various problems in power engineering, like, load forecasting, phase balancing, or disturbance analysis.
(source: Nielsen Book Data)