Tabla de Contenidos: “…Skodda, ON THE HARMONIC-TO-NOISE RATIO AS A CUE FOR AUTOMATIC CLASSIFICATION OF PARKINSON'S DISEASE -- R. …”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: From Data to Decisions - Getting Started with Analytic Applications -- Designing an advanced analytic solution -- Data layer: warehouses, lakes, and streams -- Modeling layer -- Deployment layer -- Reporting layer -- Case study: sentiment analysis of social media feeds -- Data input and transformation -- Sanity checking -- Model development -- Scoring -- Visualization and reporting -- Case study: targeted e-mail campaigns -- Data input and transformation -- Sanity checking -- Model development -- Scoring -- Visualization and reporting -- Summary -- Chapter 2: Exploratory Data Analysis and Visualization in Python -- Exploring categorical and numerical data in IPython -- Installing IPython notebook -- The notebook interface -- Loading and inspecting data -- Basic manipulations - grouping, filtering, mapping, and pivoting -- Charting with Matplotlib -- Time series analysis -- Cleaning and converting -- Time series diagnostics -- Joining signals and correlation -- Working with geospatial data -- Loading geospatial data -- Working in the cloud -- Introduction to PySpark -- Creating the SparkContext -- Creating an RDD -- Creating a Spark DataFrame -- Summary -- Chapter 3: Finding Patterns in the Noise - Clustering and Unsupervised Learning -- Similarity and distance metrics -- Numerical distance metrics -- Correlation similarity metrics and time series -- Similarity metrics for categorical data -- K-means clustering -- Affinity propagation - automatically choosing cluster numbers -- k-medoids -- Agglomerative clustering -- Where agglomerative clustering fails -- Streaming clustering in Spark -- Summary -- Chapter 4: Connecting the Dots with Models - Regression Methods -- Linear regression -- Data preparation…”

Tabla de Contenidos: “…Front Cover -- Temporal Data Mining via Unsupervised Ensemble Learning -- Temporal Data Mining via Unsupervised Ensemble Learning -- Copyright -- Contents -- List of Figures -- List of Tables -- Acknowledgments -- 1 - Introduction -- 1.1 BACKGROUND -- 1.2 PROBLEM STATEMENT -- 1.3 OBJECTIVE OF BOOK -- 1.4 OVERVIEW OF BOOK -- 2 - Temporal Data Mining -- 2.1 INTRODUCTION -- 2.2 REPRESENTATIONS OF TEMPORAL DATA -- 2.2.1 TIME DOMAIN-BASED REPRESENTATIONS -- 2.2.2 TRANSFORMATION-BASED REPRESENTATIONS -- Piecewise Local Statistics -- Piecewise Discrete Wavelet Transforms -- Polynomial Curve Fitting -- Discrete Fourier Transforms -- 2.2.3 GENERATIVE MODEL-BASED REPRESENTATIONS -- 2.3 SIMILARITY MEASURES -- 2.3.1 SIMILARITY IN TIME -- 2.3.2 SIMILARITY IN SHAPE -- 2.3.3 SIMILARITY IN CHANGE -- 2.4 MINING TASKS -- 2.5 SUMMARY -- 3 - Temporal Data Clustering -- 3.1 INTRODUCTION -- 3.2 OVERVIEW OF CLUSTERING ALGORITHMS -- 3.2.1 PARTITIONAL CLUSTERING -- K-means -- Hidden Markov Model-Based K-Models Clustering -- 3.2.2 HIERARCHICAL CLUSTERING -- Single Linkage -- Complete Linkage -- Average Linkage -- HMM-Based Agglomerative Clustering -- HMM-Based Divisive Clustering -- 3.2.3 DENSITY-BASED CLUSTERING -- Density-Based Spatial Clustering of Applications with Noise -- 3.2.4 MODEL-BASED CLUSTERING -- EM Algorithm -- HMM-Based Hybrid Partitional-Hierarchical Clustering -- HMM-Based Hierarchical Metaclustering -- 3.3 CLUSTERING VALIDATION -- 3.3.1 CLASSIFICATION ACCURACY -- 3.3.2 ADJUSTED RAND INDEX -- 3.3.3 JACCARD INDEX -- 3.3.4 MODIFIED HUBERT'S Γ INDEX -- 3.3.5 DUNN'S VALIDITY INDEX -- 3.3.6 DAVIES-BOULDIN VALIDITY INDEX -- 3.3.7 NORMALIZED MUTUAL INFORMATION -- 3.4 SUMMARY -- 4 - Ensemble Learning -- 4.1 INTRODUCTION -- 4.2 ENSEMBLE LEARNING ALGORITHMS -- Bagging -- Boosting -- 4.3 COMBINING METHODS -- Linear Combiner -- Product Combiner…”

Tabla de Contenidos: “…6.4 AIC Criterion -- 6.5 MDL Criterion -- 6.6 Durbin-Levinson Algorithm -- 7 Identification of State Space Models -- 7.1 Introduction -- 7.2 Hankel Matrix -- 7.3 Order Determination -- 7.4 Determination of Matrices and -- 7.5 Determination of Matrix -- 7.6 Mid Summary: An Ideal Procedure -- 7.7 Order Determination with SVD -- 7.8 Reliable Identification of a State Space Model -- 8 Predictive Control -- 8.1 Introduction -- 8.2 Minimum Variance Control -- 8.3 Generalized Minimum Variance Control -- 8.4 Model‐Based Predictive Control -- 8.5 Data‐Driven Control Synthesis -- 9 Kalman Filtering and Prediction -- 9.1 Introduction -- 9.2 Kalman Approach to Prediction and Filtering Problems -- 9.3 The Bayes Estimation Problem -- 9.4 One‐step‐ahead Kalman Predictor -- 9.5 Multistep Optimal Predictor -- 9.6 Optimal Filter -- 9.7 Steady‐State Predictor -- 9.8 Innovation Representation -- 9.9 Innovation Representation Versus Canonical Representation -- 9.10 K‐Theory Versus K-W Theory -- 9.11 Extended Kalman Filter - EKF -- 9.12 The Robust Approach to Filtering -- 10 Parameter Identification in a Given Model -- 10.1 Introduction -- 10.2 Kalman Filter‐Based Approaches -- 10.3 Two‐Stage Method -- 11 Case Studies -- 11.1 Introduction -- 11.2 Kobe Earthquake Data Analysis -- 11.3 Estimation of a Sinusoid in Noise -- Appendix A: Linear Dynamical Systems -- A.1 State Space and Input-Output Models -- A.2 Lagrange Formula -- A.3 Stability -- A.4 Impulse Response -- A.5 Frequency Response -- A.6 Multiplicity of State Space Models -- A.7 Reachability and Observability -- A.8 System Decomposition -- A.9 Stabilizability and Detectability -- Appendix B: Matrices -- B.1 Basics -- B.2 Eigenvalues -- B.3 Determinant and Inverse -- B.4 Rank -- B.5 Annihilating Polynomial -- B.6 Algebraic and Geometric Multiplicity -- B.7 Range and Null Space -- B.8 Quadratic Forms…”

Tabla de Contenidos: “…Automated phase unwrapping in DHM with deep learning -- 9.1 Introduction -- 9.2 Deep learning model -- 9.3 Unwrapping with deep learning model -- 9.4 Conclusions -- References -- 10. Noise-free phase imaging in Gabor DHM with deep learning -- 10.1 Introduction -- 10.2 A deep learning model for Gabor DHM -- 10.3 Experimental results -- 10.4 Discussion -- 10.5 Conclusions -- References -- -- Part III. …”

Tabla de Contenidos: “…3.9 Voice Quality of Codecs3.9.1 Discussion on Wideband codec Voice Quality; 3.10 C-Source Code for Codecs; 3.11 Codecs in VoIP Deployment; 4 Generic VAD/CNG for Waveform codecs; 4.1 VAD/CNG and Codecs; 4.2 Generic VAD/CNG Functionality; 4.3 Comfort Noise Payload Format; 4.4 G.711 Appendix II VAD/CNG Algorithm; 4.4.1 DTX Conditions; 4.4.2 CNG Algorithm; 4.5 Power-Based VAD/CNG; 4.5.1 Signal-Level Mapping Differences; 4.6 VAD/CNG in Low-Bit-Rate Codecs; 4.7 Miscellaneous Aspects of VAD/CNG; 4.7.1 RTP Packetization of VAD/CNG Packets; 4.7.2 VAD Duplicate Packets; 4.7.3 VAD/CNG Interoperability…”

Tabla de Contenidos: “…Distilling the Problem; 3.2.2. Whittling Away the Noise; 3.2.3. Refining Your Design; 3.3. Layering Your Architecture; 3.3.2. …”

Tabla de Contenidos: “…5.2.10 Filters5.3 Applications; 5.3.1 Technique; 5.3.2 Fix; 5.3.3 Feature; 5.3.4 Fit; 5.3.5 Special Effects; 5.3.6 Audio Ear Training; Chapter 6:Compression and Limiting; 6.1 Parameters; 6.1.1 Threshold; 6.1.2 Ratio; 6.1.3 Attack; 6.1.4 Release; 6.1.5 Make-Up Gain; 6.1.6 User Interface; 6.2 Technologies; 6.2.1 Tube Compressors; 6.2.2 Optical Compressors; 6.2.3 VCA Compressors; 6.2.4 Digital Compressors; 6.3 Nominal Application: Dynamic Range Reduction; 6.3.1 Prevent Overload; 6.3.2 Overcome Noise; 6.3.3 Increase Perceived Loudness; 6.3.4 Improve Intelligibility and Articulation…”

Tabla de Contenidos: “…Preface xi -- Acknowledgments xiii -- About the Author xv -- About the Reviewers xvii -- Note to Instructors xxi -- 1 HOW TO DESIGN RESISTIVE CIRCUITS 1 -- 1.1 Design of a Resistive Thevenin Source 2 -- 1.2 Design of a Coupling Circuit 4 -- 1.3 Design of a Pi Attenuator 8 -- Problems 14 -- References 17 -- 2 HOW TO PREVENT A POWER TRANSISTOR FROM OVERHEATING 19 -- 2.1 Electrical Model for Heat Transfer 20 -- 2.2 Using Manufacturer's Data for Thermal Analysis 23 -- 2.3 Forced-Air Cooling 26 -- 2.4 Dynamic Response of a Thermal System 27 -- Problems 30 -- Reference 32 -- 3 HOW TO ANALYZE A CIRCUIT 33 -- 3.1 Frequency Response of a Transfer Function 34 -- 3.2 Frequency Response and Impedance of Simple Circuits 38 -- 3.3 Frequency Response for Ladder Networks 51 -- 3.4 Generalized Technique for Determining Frequency Response 54 -- Problems 58 -- References 60 -- 4 HOW TO USE STATISTICS TO ENSURE A MANUFACTURABLE DESIGN 61 -- 4.1 Independent Component Failures 62 -- 4.2 Using the Gaussian Distribution 63 -- 4.3 Setting a Manufacturing Test Limit 68 -- 4.4 Procuring a Custom Component 71 -- Problems 76 -- References 77 -- 5 HOW TO DESIGN A FEEDBACK CONTROL SYSTEM 79 -- 5.1 Intuitive Description of a Control System 80 -- 5.2 Review of Control System Operation 81 -- 5.3 Performance of Control Systems 84 -- 5.4 First-Order Control System Design 84 -- 5.5 Second-Order Control System Design 88 -- 5.6 Circuit Realization of a Second-Order Control System 94 -- 5.7 First-Order Discrete Control System 95 -- Problems 101 -- References 102 -- 6 HOW TO WORK WITH OP-AMP CIRCUITS 103 -- 6.1 The Ideal Op-Amp 104 -- 6.2 Practical Op-Amps 108 -- 6.2.1 Effect of Input Offset Voltage 108 -- 6.2.2 Noise Contribution from Op-Amp Circuits 110 -- 6.2.3 Dynamic Characteristics of Op-Amp Circuits 113 -- 6.2.4 Effect of Capacitive Loading 116 -- 6.2.5 A Nagging Issue 118 -- Problems 119 -- References 121 -- 7 HOW TO DESIGN ANALOG FILTERS 123 -- 7.1 Passive Versus Active Filters 124 -- 7.2 The Lowpass RC Filter 125.…”

Tabla de Contenidos: “…How do we avoid being fooled by random noise and ensure scientific discoveries are trustworthy? …”

Tabla de Contenidos: “…Montes Remote Sensing of Coral Reefs: Uncertainty in the Detection of Benthic Cover, Depth, and Water Constituents Imposed by Sensor Noise, Reprinted from: Appl. Sci. 2018, 8, 2691, doi:10.3390/app8122691 -- Emmanuel Boss, Christopher R. …”

Tabla de Contenidos: “…Skodda, ON THE HARMONIC-TO-NOISE RATIO AS A CUE FOR AUTOMATIC CLASSIFICATION OF PARKINSON'S DISEASE -- R. …”

Tabla de Contenidos: “…Automating LLM interactions -- Lab 4.3 - Generating Splunk SPL content from news -- Summary -- Part 2: Automating Validations within CI/CD Pipelines -- Chapter 5: Implementing Logical Unit Tests -- Technical requirements -- Validating syntax and linting -- Lab 5.1 - CrowdStrike syntax validation -- Performing metadata and taxonomy checks -- Lab 5.2 - Google Chronicle payload validation -- Performing data input checks -- Lab 5.3 - Palo Alto signature limitation tests -- Lab 5.4 - Suricata simulation testing -- Lab 5.5 - Git pre-commit hook protections -- Summary -- Further reading -- Chapter 6: Creating Integration Tests -- Technical requirements -- Mapping and Using Synthetic Payloads -- Lab 6.1 - Splunk SPL Detection Testing -- Testing In-Line Payloads -- Lab 6.2 - AWS CloudTrail Detection Tests -- Executing Live-Fire Asynchronous Tests -- Lab 6.3 - CrowdStrike Falcon Payload Testing -- Lab 6.4 - Deploying Caldera BAS -- Summary -- Further reading -- Chapter 7: Leveraging AI for Testing -- Technical requirements -- Synthetic testing with LLMs -- Lab 7.1 - Poe Bot synthetic CI/CD unit testing -- Evaluating data security and ROI -- Lab 7.2 - CodeRabbit augmented peer review -- Implementing multi-LLM model validation -- Summary -- Part 3: Monitoring Program Effectiveness -- Chapter 8: Monitoring Detection Health -- Technical requirements -- Identifying telemetry sources -- Measuring use case performance -- Upstream detection performance -- Downstream detection performance -- Lab 8.1 - Google Chronicle detection insights -- Extending dashboard use cases -- Lab 8.2 - Mock SOAR disable excessive firing rule -- Summary -- Further reading -- Chapter 9: Measuring Program Efficiency -- Technical requirements -- Creating program KPIs -- Locating data for metrics -- Signal to Noise Ratio -- MITRE ATT&amp -- CK coverage…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Dedication -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Preparing Our First Raspberry Pi Project -- Getting what you need -- Downloading the IoT Dashboard -- Installing Windows 10 on your Raspberry Pi -- Starting your Raspberry Pi -- Connecting to your Raspberry Pi -- Downloading the Arduino IDE -- Installing Firmata on your Arduino board -- Testing your Arduino board -- Choosing a development environment -- Using the IoT Gateway project and libraries -- Creating your first project -- Adding NuGet packages -- Aggregating capabilities -- Initializing the application -- Communicating with the Arduino -- Testing the app -- Deploying the app -- Summary -- Chapter 2: Creating a Sensor to Measure Ambient Light -- Preparing our project -- Initializing the inventory library -- Initializing the persistence library -- Sampling raw sensor data -- Performing basic error correction -- Canceling noise -- Removing random spikes -- Converting to a physical quantity -- Illustrating measurement results -- Calculating basic statistics -- Defining data persistence -- Storing measured data -- Removing old data -- Summary -- Chapter 3: Creating an Actuator for Controlling Illumination -- Preparing our project -- Defining control parameters -- Understanding relays -- Connecting our lamp -- Connecting an LED -- Controlling output -- Persisting control parameter states -- Logging important control events -- Using Raspberry Pi GPIO pins directly -- Checking whether GPIO is available -- Initializing the GPIO output pin -- Setting the GPIO pin output -- Summary -- Chapter 4: Publishing Information Using MQTT -- Introducing the MQTT protocol -- Understanding the Publish/Subscribe pattern -- Preparing our project -- Selecting an MQTT broker -- Creating a Device ID -- Connecting to the broker…”

Tabla de Contenidos: “…Remove Online Distractions -- Kill the Bandwidth Eaters -- Test Your Audio -- Eliminate Background Noise -- Krisp -- SoliCall Pro -- Change Audio Settings -- Adjust Your Camera -- Prepare Yourself -- Dress for the Meeting -- Posture -- Know Your Attendees -- Get Some Clarity: Quiet Your Mind -- Etiquette -- Check Your Tech -- Arrive Early -- Make Eye Contact -- Cut the Mic -- Use Their Names -- Pay Attention -- When You Are the Presenter -- When You Are an Attendee -- Make Use of Whiteboards -- Bluescape -- Conceptboard -- Draw.Chat -- Hoylu -- Miro -- Engagement and Communication -- Chapter 8 The Watercooler Has Moved-Engagement and Socializing Remotely -- The Impact of Social Isolation -- It's Far More Than Loneliness -- Safety and Security -- Love and Belonging -- Esteem -- The Need for Social Interactions -- Avoiding Social Isolation -- Be on the Lookout for Isolation -- What to Look For -- It's Preventable -- Changes in Appearance -- Changes in Communication Habits -- Changes in Quality of Work -- Cancellations or Lateness -- Overproductivity -- High Levels of Distractability -- Don't Make Poor Assumptions -- Self-Assessment -- Negative Thoughts and Feelings -- Change in Sleep Patterns -- Fatigue -- Disinterest -- Lowered Motivation -- Irritability -- Trouble Maintaining Focus -- Depression -- Combating Isolation -- Structural Changes -- Make Work Accessible -- Visibility and Transparency -- Communication -- Check-In -- Feedback -- Recognition -- Mental Health Support -- Mental Health Toolkits -- Encourage Physical Fitness -- Engagement -- Showing Up and Staying -- Not Out of Mind -- Engagement Aligned with Other Values -- Foster Openness -- Listening Actively and Empathetically -- Social Needs -- Creating Non-Work Spaces -- Virtual Lunches, Coffee Breaks, and Happy Hours -- Make Pre-Meeting Time -- Celebrations…”

Tabla de Contenidos: “…Part III Informing Models with Theory and Data -- Chapter 13 Integrating Computational Modeling and Experiments: Toward a More Unified Theory of Social Influence -- Introduction -- Social Influence Research -- Opinion Network Modeling -- Integrated Empirical and Computational Investigation of Group Polarization -- Group Polarization Theory -- Frame‐Induced Polarization Theory -- Accept‐Shift‐Constrict Model of Opinion Dynamics -- Experiment and Results -- Integrated Approach -- Conclusion -- Acknowledgments -- References -- Chapter 14 Combining Data‐Driven and Theory‐Driven Models for Causality Analysis in Sociocultural Systems -- Introduction -- Understanding Causality -- Ensembles of Causal Models -- Case Studies: Integrating Data‐Driven and Theory‐Driven Ensembles -- Letting the Data Speak: Additive Noise Ensembles -- Choosing Data‐Driven Approaches Using Theory -- Parameterizing Theory‐Driven Models Using Data -- Theory and Data Dialogue -- Conclusions -- References -- Chapter 15 Theory‐Interpretable, Data‐Driven Agent‐Based Modeling -- The Beauty and Challenge of Big Data -- A Proposed Unifying Principle for Big Data and Social Science -- Data‐Driven Agent‐Based Modeling -- Parameter Optimization -- News Consumption -- Urgent Diffusion -- Rule Induction -- Commuting Patterns -- Social Media Activity -- Conclusion and the Vision -- Acknowledgments -- References -- Chapter 16 Bringing the Real World into the Experimental Lab: Technology‐Enabling Transformative Designs -- Understanding, Predicting, and Changing Behavior -- Social Domains of Interest -- Preventing Disease -- Harm Mitigation in Crises -- Terrorism Reduction and Lone Actors -- The SOLVE Approach -- Overview of SOLVE -- Shame Reduction as a Key Intervention -- Intelligent Agents in Games -- Generalizing Approach: Understanding and Changing Behavior Across Domains…”

“…Part I is new aspects of OSP including 7 chapters, OSP revisit, generalized OSP, FPGA designs for OSP and CEM, Kalman filter-based linear unmixing, least squares fully constrained linear mixture analysis, exploitation-based hyperspectral data compression and size estimation of supixel targets, Part II is interference rejection for linear unmixing composed of three chapters, signal-composed interference-annihilated theory, interference-annihilated noise-adjusted theory and information-processed matched filter theory; Part III is nonlinear non-literal techniques for linear unmixing consisting of 3 chapters, convex cone analysis, information theoretic criterion-based project pursuit and nonlinear mixing model analysis; Part IV is spectral coding comprising of three chapters, progressive spectral coding, spectral binary coding and spectral coding for band selection; Part V is applications made up of two chapters, applications to magnetic resonance imaging and landmine detection"--…”

Tabla de Contenidos: “…6.3 Multi-Standard Transceiver Architectures -- 6.4 Evaluating Reconfigurability -- 6.5 ADCs for Software-Defined Radio -- 6.6 DACs for Software-Defined Radio -- 6.7 Conclusions -- II RECEIVER DESIGN -- 7 OFDM Transform-Domain Receivers for Multi-Standard Communications (Sebastian Hoyos) -- 7.1 Introduction -- 7.2 Transform-Domain Receiver Background -- 7.3 Transform-Domain Sampling Receiver -- 7.4 Digital Baseband Design for the TD Receiver -- 7.5 A Comparative Study -- 7.6 Simulations -- 7.7 Gain-Bandwidth Product Requirement for an Op-Amp in a Charge-Sampling Circuit -- 7.8 Sparsity of (GHG)-1 -- 7.9 Applications -- 7.10 Conclusions -- 8 Discrete-Time Processing of RF Signals (RenaldiWinoto and Borivoje Nikolic) -- 8.1 Introduction -- 8.2 Scaling of an MOS Switch -- 8.3 Sampling Mixer -- 8.4 Filter Synthesis -- 8.5 Noise in Switched-Capacitor Filters -- 8.6 Circuit-Design Considerations -- 8.7 Perspective and Outlook -- 9 Oversampled ADC Using VCO-Based Quantizers (MatthewZ. …”

Tabla de Contenidos: “…Chapter 9: Understanding Feature Engineering and Preparing Data for Modeling -- Chapter 10: Mastering Machine Learning Concepts -- Introducing the machine learning workflow -- Problem statement -- Model selection -- Model tuning -- Model predictions -- Getting started with supervised machine learning -- Regression versus classification -- Linear regression - regression -- Logistic regression -- k-nearest neighbors (k-NN) -- Random forest -- Extreme Gradient Boosting (XGBoost) -- Getting started with unsupervised machine learning -- K-means -- Density-based spatial clustering of applications with noise (DBSCAN) -- Other clustering algorithms -- Evaluating clusters -- Summarizing other notable machine learning models -- Understanding the bias-variance trade-off -- Tuning with hyperparameters -- Grid search -- Random search -- Bayesian optimization -- Summary -- Chapter 11: Building Networks with Deep Learning -- Introducing neural networks and deep learning -- Weighing in on weights and biases -- Introduction to weights -- Introduction to biases -- Activating neurons with activation functions -- Common activation functions -- Choosing the right activation function -- Unraveling backpropagation -- Gradient descent -- What is backpropagation? …”

Tabla de Contenidos: “…-- Darwinian evolution -- The genetic algorithms analogy -- The theory behind genetic algorithms -- The schema theorem -- Differences from traditional algorithms -- Population-based -- Genetic representation -- Fitness function -- Probabilistic behavior -- Advantages of genetic algorithms -- Global optimization -- Handling complex problems -- Handling a lack of mathematical representation -- Resilience to noise -- Parallelism -- Continuous learning -- Limitations of genetic algorithms -- Special definitions -- Hyperparameter tuning -- Computationally intensive -- Premature convergence -- No guaranteed solution -- Use cases for genetic algorithms -- Summary -- Further reading -- Chapter 2: Understanding the Key Components of Genetic Algorithms -- The basic flow of a genetic algorithm -- Creating the initial population -- Calculating the fitness -- Applying selection, crossover, and mutation -- Checking the stopping conditions -- Selection methods -- Roulette wheel selection -- Stochastic universal sampling -- Rank-based selection -- Fitness scaling -- Tournament selection -- Crossover methods -- Single-point crossover -- Two-point and k-point crossover -- Uniform crossover -- Crossover for ordered lists -- Mutation methods -- Flip-bit mutation -- Swap mutation -- Inversion mutation -- Scramble mutation -- Real-coded genetic algorithms -- Blend crossover -- Simulated binary crossover -- Real mutation -- Understanding elitism -- Niching and sharing -- Serial niching versus parallel niching -- The art of solving problems using genetic algorithms -- Summary -- Further reading -- Part 2: Solving Problems with Genetic Algorithms…”