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• Chapter 1. Recent neuro-fuzzy approaches for feature selection and classification
• Chapter 2. An approach to license plate recognition system using neural network
• Chapter 3. Intuitionistic fuzzy time series forecasting based on dual hesitant fuzzy set for stock market: DHFS-based IFTS model for stock market
• Chapter 4. Design and implementation of an intelligent traffic management system: a neural approach
• Chapter 5. DNA fragment assembly using quantum-inspired genetic algorithm
• Chapter 6. Effective prevention and reduction in the rate of accidents using internet of things and data analytics
• Chapter 7. Nature-inspired algorithms for bi-criteria parallel machine scheduling
• Chapter 8. Hybrid honey bees meta-heuristic for benchmark data classification
• Chapter 9. Guided search-based multi-objective evolutionary algorithm for grid workflow scheduling
• Chapter 10. An optimal configuration of sensitive parameters of PSO applied to textual clustering
• Chapter 11. An improved hybridized evolutionary algorithm based on rules for local sequence alignment
• Chapter 12. Bi-objective supply chain optimization with supplier selection
• Chapter 13. Overview and optimized design for energy recovery patents applied to hydraulic systems
• Chapter 14. Wireless robotics networks for search and rescue in underground mines: taxonomy and open issues
• Chapter 15. Solving job scheduling problem in computational grid systems using a hybrid algorithm
• Chapter 16. An enhanced clustering method for image segmentation

Modern optimization approaches have attracted an increasing number of scientists, decision makers, and researchers. As new issues in this field emerge, different optimization methodologies must be developed and implemented. Exploring Critical Approaches of Evolutionary Computation is a vital scholarly publication that explores the latest developments, methods, approaches, and applications of evolutionary models in a variety of fields. It also emphasizes evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, genetic programming, and related fields such as swarm intelligence and other evolutionary computation techniques. Highlighting a range of pertinent topics such as neural networks, data mining, and data analytics, this book is designed for IT developers, IT theorists, computer engineers, researchers, practitioners, and upper-level students seeking current research on enhanced information exchange methods and practical aspects of computational systems.
(source: Nielsen Book Data)

Modern optimization approaches have attracted an increasing number of scientists, decision makers, and researchers. As new issues in this field emerge, different optimization methodologies must be developed and implemented. Exploring Critical Approaches of Evolutionary Computation is a vital scholarly publication that explores the latest developments, methods, approaches, and applications of evolutionary models in a variety of fields. It also emphasizes evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, genetic programming, and related fields such as swarm intelligence and other evolutionary computation techniques. Highlighting a range of pertinent topics such as neural networks, data mining, and data analytics, this book is designed for IT developers, IT theorists, computer engineers, researchers, practitioners, and upper-level students seeking current research on enhanced information exchange methods and practical aspects of computational systems.
(source: Nielsen Book Data)

• Preface.- Introduction.- Multilevel segmentation in digital images.- Multi-Circle detection on images.- Template matching.- Motion estimation.- Photovoltaic cell design.- Parameter identification of induction motors.- White blood cells Detection in images.- Estimation of view transformations in images.- Filter Design.
• (source: Nielsen Book Data)

This book compares the performance of various evolutionary computation (EC) techniques when they are faced with complex optimization problems extracted from different engineering domains. Particularly focusing on recently developed algorithms, it is designed so that each chapter can be read independently. Several comparisons among EC techniques have been reported in the literature, however, they all suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. In each chapter, a complex engineering optimization problem is posed, and then a particular EC technique is presented as the best choice, according to its search characteristics. Lastly, a set of experiments is conducted in order to compare its performance to other popular EC methods.
(source: Nielsen Book Data)

• Preface
• Chapter 1. Introduction to Nature-inspired Algorithms
• Chapter 2. Ant Colony Optimizer: Theory, Literature Review, and Application in AUV Path Planning.-Chapter 3. Ant Lion Optimizer: Theory, Literature Review, and Application in Multi-layer Perceptron Neural Network
• Chapter 4. Dragonfly Algorithm: Theory, Literature Review, and Application in Feature Selection
• Chapter 5. Genetic Algorithm: Theory, Literature Review, and Application in Image Reconstruction etc.

This book covers the conventional and most recent theories and applications in the area of evolutionary algorithms, swarm intelligence, and meta-heuristics. Each chapter offers a comprehensive description of a specific algorithm, from the mathematical model to its practical application. Different kind of optimization problems are solved in this book, including those related to path planning, image processing, hand gesture detection, among others. All in all, the book offers a tutorial on how to design, adapt, and evaluate evolutionary algorithms. Source codes for most of the proposed techniques have been included as supplementary materials on a dedicated webpage.

• 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. Committee and Sponsors. Evolutionary computation in crack problems
• W. Beluch. Investigation of evolutionary algorithm effectiveness in optimal synthesis of certain mechanisms
• K. Bialas-Hetltowski, et al. Minimum heat losses subjected to stiffness constraints: window frame optimization
• R.A. Bialecki, M. Krol. Evolutionary computation in inverse problems
• T. Burczynski, et al. Hang-glider wing design by genetic optimization
• S. D'Angelo, et al. An error function for optimum dimension synthesis of mechanisms using genetic algorithms
• I.F. de Bustos, et al. Evolutionary computation in thermoelastic problems
• A. Dlugosz. Management of evolutionary MAS for multiobjective optimisation
• G. Dobrowalski, M. Kisiel-Dorohinicki. PAMUC: a new method to handle with constraints and multiobjectivity in evolutionary algorithms
• R. Filomeno Coelho, et al. A comparative analysis of "controlled elitism" in the NGSA-II applied to frame optimization
• D. Greiner, et al. IS-PAES: multiobjective optimization with efficient constraint handling
• A. Hernandez Aguirre, et al. Optimization of aligned fiber laminate composites
• Z. Hu, et al. Genetic algorithm for damage assessment
• V.T. Johnson, et al. Estimation of parameters for a hydrodynamic transmission system mathematical model with the application of genetic algorithm
• A. Kesy, et al. Study of safety of high-rise buildings using evolutionary search
• S. Khajhpour, D.E. Grierson. Structural design using genetic algorithm
• E. Kita, et al. The topology optimization using evolutionary search
• G. Kokot, P. Orantek. Identification of CMM parametric errors by hierarchical genetic strategy
• J. Kolodziej, et al. Genetic algorithm for fatigue crack detection in Timoshenko beam
• M. Krawczuk, et al. Multicriteria design optimization of robot gripper mechanisms
• S. Krenich. Optimal design of multiple clutch brakes using a multistage evolutionary method
• S. Krenich, A. Osyczka. Distributed evolutionary algorithms in optimization of nonlinear solids
• W. Kus, T. Burczynski. Adaptive penalty strategies in genetic search for problems with inequality and equality constraints
• C.-Y. Lin, W.-H. Wu. On the identification of linear elastic mechanical behaviour of orthopedic materials using evolutionary algorithms
• M. Magalhaes Dourado, et al. Ranking pareto optimal solutions in genetic algorithm by using undifferentiation interval method
• J. Montusiewicz. The effectiveness of probabilistic algorithms in shape and topology discrete optimisation of 2-D composite structures
• A. Muc. Genetic algorithms in optimisation of resin hardening technological processes
• A. Muc, P. Saj. Hybrid evolutionary algorithms in optimization of structures under dynamical loads
• P. Orantek. Evolutionary optimization system (EOS) for design automation
• O. Osyczka, et al. Evolutionary method for a universal motor geometry optimization
• G. Papa, B. Korousic-Seljak. A review of the development and application of cluster oriented genetic algorithms
• I.C. Parmee. Genetic algorithm optimization of hole shapes in a perforated elastic plate over a range of loads
• S. Vigdergauz. An object oriented library for evolutionary programs with applications in partitioning of finite element meshes
• J. Zola, et al.
• (source: Nielsen Book Data)

This book contains papers presented at the IUTAM Symposium on Evolutionary Methods in Mechanics held in Cracow, Poland, September 24-27, 2002. Recently, Evolutionary algorithms and methods have become the most effective tools for solving specific kinds of problems in mechanics, especially in structural and multidisciplinary optimization. The book is devoted to both theoretical and practical developments of computational mechanics methods drawing their inspiration from nature with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming and other evolutionary computation techniques in mechanics. The special emphasis is put on evolutionary optimization in various fields of mechanics. The subject of evolutionary optimization has recently experienced a remarkable growth. New concepts, approaches and applications are being continually and exploited to provide efficient tools for solving a variety of optimization problems in mechanics. Several important topics are presented: evolutionary methods in shape and topology optimization, evolutionary methods in size and material optimization, evolutionary methods in multiobjective optimization, in inverse problems of engineering mechanics, in biomechanics, coupling of evolutionary methods and neural networks in mechanics, methods of artificial intelligence in mechanics based on evolutionary approaches and other unconventional applications in mechanics. Research workers, specialists and professionals who deal with methods of artificial intelligence, especially in the field of evolutionary computation, structural and multidisciplinary optimization would be interested in this book.
(source: Nielsen Book Data)

• NEW DEVELOPMENTS IN EVOLUTIONARY COMPUTATION RESEARCH; NEW DEVELOPMENTS IN EVOLUTIONARY COMPUTATION RESEARCH; LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA; CONTENTS; PREFACE;
• Chapter 1: MULTI-OBJECTIVE OPTIMIZATIONOF TRADING STRATEGIES USING GENETICALGORITHMS IN UNSTABLE ENVIRONMENTS; Abstract; A. Part A. Review of the Main Problem Solving and Optimization Techniques; B. Part B.A Review of Main Multi-Objective Optimization Techniques; C. Part C.A Case Study; Conclusion; References.

• Chapter 2: PROMOTING BETTER GENERALISATION IN MULTI-LAYER PERCEPTRONS USING A SIMULATED SYNAPTIC DOWNSCALING MECHANISMAbstract; 1. Introduction; 2. Background; 3. Model and Experiments; 4. Results and Analysis; 5. Conclusion; Acknowledgment; References;
• Chapter 3: PLANT PROPAGATION-INSPIRED ALGORITHMS; Abstract; 1. Introduction; 2. Background; 3. Plant Propagation Algorithms; 4. Applications; 5. Conclusion; References;
• Chapter 4: TOPOGRAPHICAL CLEARING DIFFERENTIAL EVOLUTION APPLIED TO REAL-WORLD MULTIMODAL OPTIMIZATION PROBLEMS; Abstract; 1. Introduction.

• 2. The Differential Evolution Algorithm3. Topographical Clearing; 4. Numerical Comparisons; 5. Conclusion; Appendix A. Nonlinear Systems Formulated as Optimization Problems; Appendix B. Data and Fitted Variables for the Catalytic Reactor Model; References;
• Chapter 5: ROBOTICS, EVOLUTION AND INTERACTIVITY IN SONIC ART INSTALLATIONS; Abstract; Introduction; 1. JaVOX, an Evolutionary Composition System; 2. Generative Sonification; 3. Automation x Interactivity; 4. Interactivity, Evolution and Structure; Conclusion; Acknowledgments; References.

• Chapter
• 6: AN ANALYSIS OF EVOLUTIONARY-BASED SAMPLING METHODOLOGIESAbstract;
• 1. Introduction;
• 2. Background;
• 3. Numerical Experiments: Design and Implementation;
• 4. Results and Discussion;
• 5. Conclusion; References; Blank Page; INDEX.

A common approach for solving simulation-driven engineering problems is by using metamodel-assisted optimization algorithms, namely, in which a metamodel approximates the computationally expensive simulation and provides predicted values at a lower computational cost. Such algorithms typically generate an initial sample of solutions which are then used to train a preliminary metamodel and to initiate an optimization process. One approach for generating the initial sample is with the design of experiment methods which are statistically oriented, while the more recent search-driven sampling approach invokes a computational intelligence optimizer such as an evolutionary algorithm, and then uses the vectors it generated as the initial sample. This book discusses research and new developments on evolutionary computation.
(source: Nielsen Book Data)

• Computational Models of Evolution and Development
• Analysis of Gene Regulatory Networks
• Evolutionary Synthesis of Gene Regulatory Dynamics
• Evolution of Morphological Development
• Evolution of Neural Development
• Computational Brain-Body Co-Evolution
• Evolutionary Morphogenetic Self-Organization of Swarm Robots
• Towards Evolutionary Developmental Systems

This book provides a basic yet unified overview of theory and methodologies for evolutionary developmental systems. Based on the authors extensive research into the synergies between various approaches to artificial intelligence including evolutionary computation, artificial neural networks, and systems biology, it also examines the inherent links between biological intelligence and artificial intelligence. The book begins with an introduction to computational algorithms used to understand and simulate biological evolution and development, including evolutionary algorithms, gene regulatory network models, multi-cellular models for neural and morphological development, and computational models of neural plasticity. Chap. 2 discusses important properties of biological gene regulatory systems, including network motifs, network connectivity, robustness and evolvability. Going a step further, Chap. 3 presents methods for synthesizing regulatory motifs from scratch and creating more complex regulatory dynamics by combining basic regulatory motifs using evolutionary algorithms. Multi-cellular growth models, which can be used to simulate either neural or morphological development, are presented in Chapters 4 and 5. Chap. 6 examines the synergies and coupling between neural and morphological evolution and development. In turn, Chap. 7 provides preliminary yet promising examples of how evolutionary developmental systems can help in self-organized pattern generation, referred to as morphogenetic self-organization, highlighting the great potentials of evolutionary developmental systems. Finally, Chap. 8 rounds out the book, stressing the importance and promise of the evolutionary developmental approach to artificial intelligence. Featuring a wealth of diagrams, graphs and charts to aid in comprehension, this book offers a valuable asset for graduate students, researchers and practitioners who are interested in pursuing a different approach to artificial intelligence

Multi-objective optimization problems (MOPs) and uncertain optimization problems (UOPs) which widely exist in real life are challengeable problems in the fields of decision making, system designing, and scheduling, amongst others. Decomposition exploits the ideas of aEURO~making things simpleaEURO (TM) and aEURO~divide and conqueraEURO (TM) to transform a complex problem into a series of simple ones with the aim of reducing the computational complexity. In order to tackle the abovementioned two types of complicated optimization problems, this book introduces the decomposition strategy and conducts a systematic study to perfect the usage of decomposition in the field of multi-objective optimization, and extend the usage of decomposition in the field of uncertain optimization.
(source: Nielsen Book Data)

• part 1. Theory and applications in engineering systems
• part 2. Theory and applications of single objective optimization studies
• part 3. Theory and applications of single and multiobjective optimization studies

"Science has made great strides in modeling space, time, mass and energy. Yet little attention has been paid to the precise representation of the information ubiquitous in nature. Introduction to Evolutionary Informatics fuses results from complexity modeling and information theory that allow both meaning and design difficulty in nature to be measured in bits. Built on the foundation of a series of peer-reviewed papers published by the authors, the book is written at a level easily understandable to readers with knowledge of rudimentary high school math. Those seeking a quick first read or those not interested in mathematical detail can skip marked sections in the monograph and still experience the impact of this new and exciting model of nature's information. This book is written for enthusiasts in science, engineering and mathematics interested in understanding the essential role of information in closely examined evolution theory."--Publisher's website.

The Symposiums aim to bring together researchers and practitioners from countries of the Asia Pacific Region in the fields of intelligent systems and evolutionary computation IES 2017 welcomes fundamental science, methodology, method, and practical application papers in the IES Symposiums traditional range of topics, which includes but is not limited to Artificial Intelligence Artificial Life and Societies Machine Learning Neural Networks Data Science and Decision Analytics Evolutionary and Nature Inspired computation Game Theory Human Brain Computer Interaction Agents and Complex Systems Cognitive and Developmental Systems Robotics Systems Intelligent Transport Systems.

• 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)

• Improved Grey wolf Optimization based Feature selection and classification using CNN for Diabetic Retinopathy detection.- Feature Selection Using Modified Sine Cosine Algorithm with COVID-19 Dataset.- Blood Cell Image Denoising based on Tunicate Rat Swarm Optimization with Median Filter.- A Hybrid Approach for Deep Noise Suppression using Deep Neural Networks.- Human Health Care Systems Analysis for Cloud Data Structure of Biometric System using ECG Analysis.- Data mining for Solving Medical Diagnostics Problems.- Classification of Diabetic Retinopathy using Ensemble of Machine Learning Classifiers with IDRID Dataset.- Epileptic Seizure Prediction Using Geometrical Features Extracted From HRV Signal.- An Extensive Survey on Outlier Prediction using Mining and Learning Approaches.- Performance Comparison of Data Security Strategies in Fog Computing.- Design and Simulation of a Direct-PSK Based Telecommand Receiver for Small Satellite
• Analysis of Data Aggregation and Clustering Protocol in Wireless Sensor Networks using Machine Learning.- DetecSec : A Framework to Detect and Mitigate ARP Cache Poisoning Attacks.- PAPR Reduction in SDR based OFDM System.
• (source: Nielsen Book Data)

This book mainly reflects the recent research works in evolutionary computation technologies and mobile sustainable networks with a specific focus on computational intelligence and communication technologies that widely ranges from theoretical foundations to practical applications in enhancing the sustainability of mobile networks. Today, network sustainability has become a significant research domain in both academia and industries present across the globe. Also, the network sustainability paradigm has generated a solution for existing optimization challenges in mobile communication networks. Recently, the research advances in evolutionary computing technologies including swarm intelligence algorithms and other evolutionary algorithm paradigms are considered as the widely accepted descriptors for mobile sustainable networks virtualization, optimization, and automation. To deal with the emerging impacts on mobile communication networks, this book discusses about the state-of-the research works on developing a sustainable design and their implementation in mobile networks. With the advent of evolutionary computation algorithms, this book contributes varied research chapters to develop a new perspective on mobile sustainable networks.
(source: Nielsen Book Data)

• Chapter 1. Design and Construction of a Dual Axis Solar Tracking System by Astronomical Algorithm.-
• Chapter 2. Estimation of Magnetic Flux linkage in SRM using various defuzzification techniques.-
• Chapter 3. Multilevel Inverter based STATCOM for Distribution System.-
• Chapter 4. Sensitivity Analysis and Design Optimization of Synchronous Reluctance and Permanent Magnet Motors.-
• Chapter 5. A New Heuristic algorithm for Economic Load Dispatch incorporating wind power.-
• Chapter 6. Enhanced Grasshopper Optimization Algorithm For Numerical Optimization.-
• Chapter 7. Eco-Routing - To Reduce Vehicle CO2 Emissions by CACC: An IoT Application.-
• Chapter 8. Fuzzy Sliding Mode Control of DC-DC Boost Converter with Right-Half Plane Zero.-
• Chapter 9. Liquid Level Control of Non Linear Process Using Big Bang - Big Crunch Optimization Based Controller.-
• Chapter 10. Impact of PV Cells and MPPT Controller on Power System Dynamics.-
• Chapter 11. Wavelet Feature Based Microcalcification Detection in Mammogram.-
• Chapter 12. Reliable Radiation Hardened Memory Cells for Single-Event Multiple Effects.-
• Chapter 13. Finger Vein Identification Using Deep Convolutional Generative Adversarial Networks.-
• Chapter 14. Computer Aided Detection of Malignant Mass in Mammogram using U-Net Architecture.-
• Chapter 15. Visualization and Evaluation of Methane Gas Leakage by Thermal Image Processing using Supervised Deep Learning Models.
• (source: Nielsen Book Data)

This book gathers selected papers presented at the 6th International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems, held at the Anna University, Chennai, India, from 20 to 22 April 2020. It covers advances and recent developments in various computational intelligence techniques, with an emphasis on the design of communication systems. In addition, it shares valuable insights into advanced computational methodologies such as neural networks, fuzzy systems, evolutionary algorithms, hybrid intelligent systems, uncertain reasoning techniques, and other machine learning methods and their application to decision-making and problem-solving in mobile and wireless communication networks.
(source: Nielsen Book Data)

• Evolutionary Algorithms: Revisited.- A Novel Evolution Strategy Algorithm.-
• 3. Evolutionary Optimization of Constrained Problems.- An Incest Prevented Evolution Strategy Algorithm.- Evolutionary Solution of Optimal Control Problems.- Evolutionary Design of Robot Controllers.- Evolutionary Behavior-Based Control of Mobile Robots.- Evolutionary Trajectory Planning of Autonomous Robots.- A. Definitions from Probability Theory and Statistics.- B. C-Language Source Code of the NES Algorithm.- C. Convergence Behavior of Evolution Strategies.
• (source: Nielsen Book Data)

Evolutionary computation, a broad field that includes genetic algorithms, evolution strategies, and evolutionary programming, has proven to offer well-suited techniques for industrial and management tasks - therefore receiving considerable attention from scientists and engineers during the last decade. This monograph develops and analyzes evolutionary algorithms that can be successfully applied to real-world problems such as robotic control. Although of particular interest to robotic control engineers, Evolutionary Computations also may interest the large audience of researchers, engineers, designers and graduate students confronted with complicated optimization tasks.
(source: Nielsen Book Data)