Search Results - (((((((ant OR giant) OR semantic) OR when) OR cantor) OR anne) OR shape) OR hints) algorithms.

Introduction to Mathematical Optimization : From Linear Programming to Metaheuristics. by Yang, Xin-She

Table of Contents: “…Tabu Search""; ""8.1 Tabu Search""; ""8.2 Travelling Salesman Problem""; ""8.3 Tabu Search for TSP""; ""9. Ant Colony Optimization""; ""9.1 Behaviour of Ants""; ""9.2 Ant Colony Optimization""; ""9.3 Double Bridge Problem""; ""9.4 Multi-Peak Functions""; ""10. …”
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What Every Teacher Should Know About Effective Teaching Strategies. by Tileston, Donna E. Walker

Table of Contents: “…Procedural MemoryAutomatic Memory -- Emotional Memory -- Chapter 4 -- Procedural Knowledge: Teaching Strategies That Work -- Helping Students Construct Models -- Teaching Procedural Knowledge That Requires Algorithms -- Strategies to Help Construct Models -- Shaping Procedural Knowledge -- Internalizing Procedural Knowledge -- The Metacognitive System -- Chapter 5 -- Graphic Organizers: Strategies for Thinking -- Graphic Organizers -- Chapter 6 -- Using Verbal Strategies in the Classroom -- Brainstorming -- Socratic Questioning -- Quaker Dialogues…”
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Stochastic global optimization : techniques and applications in chemical engineering

Table of Contents: “…Differential evolution : method, developments and chemical engineering applications / Chen Shaoqiang, Gade Pandu Rangaiah and Mekapati Srinivas -- ch. 7. Ant colony optimization : details of algorithms suitable for process engineering / V.K. …”
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Understanding Artificial Intelligence. by Sabouret, Nicolas

Table of Contents: “…Small but Strong: Understanding How Ants Find Their Way -- Multi-Agent Systems -- The Algorithm and the Ant -- Just Like in the Ant Colony -- Calculating a Path With Ants -- The More the Merrier! …”
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Computational mechanics of discontinua by Munjiza, Antonio A.

Table of Contents: “…Front Matter -- Introduction to Mechanics of Discontinua -- Methods of Mechanics of Discontinua -- Disc to Edge Contact Interaction in 2D -- Triangle to Edge Contact Interaction in 2D -- Ball to Surface Contact Interaction in 3D -- Tetrahedron to Points Contact Interaction in 3D -- Tetrahedron to Triangle Contact Interaction in 3D -- Rock Joints -- MR Contact Detection Algorithm for Bodies of Similar Size -- MR Contact Detection Algorithm for Bodies of Different Sizes -- MR Contact Detection Algorithm for Complex Shapes in 2D -- MR Contact Detection Algorithm for Complex Shapes in 3D -- Parallelization.…”
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Artificial neural systems : principle and practice by Lorrentz, Pierre

Table of Contents: “…INTRODUCTIONDENSITY BASED ALGORITHMS: CLUSTERING ALGORITHMS; NATURE-BASED ALGORITHMS; Evolutionary Algorithm and Programming ; Genetic Algorithm; GA Operators; APPLICATIONS OF GENETIC ALGORITHM; NETWORK METHOD: EDGES AND NODES; MULTI-LAYERED PERCEPTRON; REAL-TIME APPLICATIONS OF STATE-OF-THE-ART ANN SYSTEMS; DEFINITION OF ARTIFICIAL NEURAL NETWORKS (ANN); Intelligence; An Artificial Neural Network (ANN) system; PERFORMANCE MEASURES; Receiver's Operating Characteristics (ROC); Hypothesis Testing; Chi-squared (Goodness-of-fit) Test; CONFLICT OF INTEREST; ACKNOWLEDGEMENTS; REFERENCES…”
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Generalized Network Design Problems : Modeling and Optimization. by Pop, Petrică C.

Table of Contents: “…3.2 An efficient transformation of the GTSP into the TSP3.3 An exact algorithm for the Generalized Traveling Salesman Problem; 3.4 Integer programming formulations of the GTSP; 3.4.1 Formulations based on the properties of Hamiltonian tours; 3.4.2 Flow based formulations; 3.4.3 A local-global formulation; 3.5 Solving the Generalized Traveling Salesman Problem; 3.5.1 Reinforcing ant colony system for solving the GTSP; 3.5.2 Computational results; 3.5.3 A hybrid heuristic approach for solving the GTSP; 3.5.4 Computational results; 3.6 The drilling problem; 3.6.1 Stigmergy and autonomous robots.…”
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Evolutionary optimization

Table of Contents: “…Cover -- Contents -- Preface -- Contributing Authors -- Part I Introduction -- 1 Conventional Optimization Techniques -- 1 Classifying Optimization Models -- 2 Linear Programming -- 3 Goal Programming -- 4 Integer Programming -- 5 Nonlinear Programming -- 6 Simulation -- 7 Further Reading -- 2 Evolutionary Computation -- 1 What Is Evolutionary Computation -- 2 A Brief Overview of Evolutionary Computation -- 3 Evolutionary Algorithm and Generate-and-Test Search Algorithm -- 4 Search Operators -- 5 Summary -- Part II Single Objective Optimization -- 3 Evolutionary Algorithms and Constrained Optimization -- 1 Introduction -- 2 General considerations -- 3 Numerical optimization -- 4 Final Remarks -- 4 Constrained Evolutionary Optimization -- 1 Introduction -- 2 The Penalty Function Method -- 3 Stochastic Ranking -- 4 Global Competitive Ranking -- 5 How Penalty Methods Work -- 6 Experimental Study -- 7 Conclusion -- Appendix: Test Function Suite -- Part III Multi-Objective Optimization -- 5 Evolutionary Multiobjective Optimization -- 1 Introduction -- 2 Definitions -- 3 Historical Roots -- 4 A Quick Survey of EMOO Approaches -- 5 Current Research -- 6 Future Research Paths -- 7 Summary -- 6 MEA for Engineering Shape Design -- 1 Introduction -- 2 Multi-Objective Optimization and Pareto-Optimality -- 3 Elitist Non-dominated Sorting GA (NSGA-II) -- 4 Hybrid Approach -- 5 Optimal Shape Design -- 6 Simulation Results -- 7 Conclusion -- 7 Assessment Methodologies for MEAs -- 1 Introduction -- 2 Assessment Methodologies -- 3 Discussion -- 4 Comparing Two Algorithms: An Example -- 5 Conclusions and Future Research Paths -- Part IV Hybrid Algorithms -- 8 Hybrid Genetic Algorithms -- 1 Introduction -- 2 Hybridizing GAs with Local Improvement Procedures -- 3 Adaptive Memory GA's -- 4 Summary -- 9 Combining choices of heuristics -- 1 Introduction -- 2 GAs and parameterised algorithms -- 3 Job Shop Scheduling -- 4 Scheduling chicken catching -- 5 Timetabling -- 6 Discussion and future directions -- 10 Nonlinear Constrained Optimization -- 1 Introduction -- 2 Previous Work -- 3 A General Framework to look for SPdn -- 4 Experimental Results -- 5 Conclusions -- Part V Parameter Selection in EAs -- 11 Parameter Selection -- 1 Introduction -- 2 Parameter tuning vs. parameter control -- 3 An example -- 4 Classification of Control Techniques -- 5 Various forms of control -- 6 Discussion -- Part VI Application of EAs to Practical Problems -- 12 Design of Production Facilities -- 1 Introduction -- 2 Design for Material Flow When the Number of I/O Points is Unconstrained -- 3 Design for Material Flow for a Single I/O Point -- 4 Considering Intradepartmental Flow -- 5 Material Handling System Design -- 6 Concluding Remarks -- 13 Virtual Population and Acceleration Techniques -- 1 Introduction -- 2 Concept of Virtual Population -- 3 Solution Acceleration Techniques -- 4 Accelerated GA and Acceleration Sche.…”
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Ivor Horton's beginning Visual C++ 2012 by Horton, Ivor

Table of Contents: “…When to Overload Functions --…”
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Navigation signal processing for GNSS software receivers by Pany, Thomas

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Artificial Intelligence in Semiconductor Industry - Materials to Applications. by Wee, Hui-Ming

Table of Contents: “…Cover -- Guest editorial -- Efficient VLSI architecture for FIR filter design using modified differential evolution ant colony optimization algorithm -- Optimized DA-reconfigurable FIR filters for software defined radio channelizer applications -- Grid -- connected operation and performance of hybrid DG having PV and PEMFC -- High speed data encryption technique with optimized memory based RSA algorithm for communications -- A 10-bit 200 MS/s pipelined ADCwith parallel sampling and switched op-amp sharing technique…”
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Machine learning : a Bayesian and optimization perspective by Theodoridis, Sergios, 1951-

Table of Contents: “…Probability and stochastic processes -- Learning in parametric modeling: basic concepts and directions -- Mean-square error linear estimation -- Stochastic gradient descent: the LMS algorithm -- The least-squares family -- Classification: a tour of the classics -- Parameter learning: a convex analytic path -- Sparsity-aware learning: concepts and theoretical foundations -- Sparcity-aware learning: algorithms and applications -- Learning in reproducing Kernel Hilbert spaces -- Bayesian learning: inference and the EM alogrithm -- Bayesian learning: approximate inference and nonparametric models -- Monte Carlo methods -- Probabilistic graphical models: Part I -- Probabilistic graphical models: Part II -- Particle filtering -- Neural networks and deep learning -- Dimensionality reduction -- Appendix A LInear algebra -- Appendix B Probability theory and statistics -- Appendix C Hints on constrained optimization.…”
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Recent advances in hybrid metaheuristics for dataclustering

Table of Contents: “…Cover -- Title Page -- Copyright -- Contents -- List of Contributors -- Series Preface -- Preface -- Chapter 1 Metaheuristic Algorithms in Fuzzy Clustering -- 1.1 Introduction -- 1.2 Fuzzy Clustering -- 1.2.1 Fuzzy c-means (FCM) clustering -- 1.3 Algorithm -- 1.3.1 Selection of Cluster Centers -- 1.4 Genetic Algorithm -- 1.5 Particle Swarm Optimization -- 1.6 Ant Colony Optimization -- 1.7 Artificial Bee Colony Algorithm -- 1.8 Local Search-Based Metaheuristic Clustering Algorithms -- 1.9 Population-Based Metaheuristic Clustering Algorithms -- 1.9.1 GA-Based Fuzzy Clustering…”
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Language and logos : studies in theoretical and computational linguistics

Table of Contents: “…CONTENTS -- Preface -- Part I Syntax -- A Spurious Genitive Puzzle in Polish -- Semantic Type Effects on Crossing Movement in German -- Me and Chomsky -- On the Typology of Verb Second -- Movement from Verb-Second Clauses Revisited -- Spurious Ambiguities and the Parentheticals Debate -- Part II Semantics -- On Squeamishness of the Royal Kind -- Information Structure of schon -- When- Clauses, Factive Verbs and Correlates -- The Proof Theory of Partial Variables -- Brentano's Apple -- How to Interpret Expletive Negation under bevor in German…”
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Visual Data Mining : The VisMiner Approach. by Anderson, Russell K.

Table of Contents: “…Regression Analysis -- The Regression Model -- Correlation and Causation -- Algorithms for Regression Analysis -- Assessing Regression Model Performance -- Model Validity -- Looking Beyond R2 -- Polynomial Regression -- Artificial Neural Networks for Regression Analysis -- Dataset Preparation -- Tutorial -- A Regression Model for Home Appraisal -- Modeling with the Right Set of Observations -- Exercise 6.1 -- ANN Modeling -- The Advantage of ANN Regression -- Top-Down Attribute Selection -- Issues in Model Interpretation -- Model Validation -- Model Application -- Summary…”
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New autonomous systems by Cardon, Alain, 1946-, Itmi, Mhamed

Table of Contents: “…Intro -- Table of Contents -- Title -- Copyright -- Introduction -- List of Algorithms -- 1 Systems and their Design -- 1.1. Modeling systems -- 1.2. …”
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Enterprise AI for Dummies. by Jarvinen, Zachary

Table of Contents: “…External partnerships -- The importance of executive buy-in -- Weighing Your Options: Build versus Buy -- When you should do it yourself -- When you should partner with a provider -- Hosting in the Cloud versus On Premises -- What the cloud providers say -- What the hardware vendors say -- The truth in the middle -- Part 2 Exploring Vertical Market Applications -- Chapter 5 Healthcare/HMOs: Streamlining Operations -- Surfing the Data Tsunami -- Breaking the Iron Triangle with Data -- Matching Algorithms to Benefits -- Examining the Use Cases -- Delivering lab documents electronically…”
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Solutions in lidar profiling of the atmosphere by Kovalev, Vladimir A.

Table of Contents: “…1.6.1 Algorithm and Solution Uncertainty1.6.2 Numerical Simulations and Experimental Data; 1.7 Examination of the Remaining Offset in the Backscatter Signal by~Analyzing the Shape of the Integrated Signal; 1.8 Issues in the Examination of the Lidar Overlap Function; 1.8.1 Influence of Distortions in the Lidar Signal when Determining the~Overlap Function; 1.8.2 Issues of Lidar Signal Inversion within the Incomplete Overlap Area; Chapter 2 Essentials and Issues in Separating the Backscatter and Transmission Terms in The Lidar Equation.…”
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Frontiers in Massive Data Analysis. by Council, National Research

Table of Contents: “…FrontMatter -- Acknowledgments -- Contents -- Summary -- 1 Introduction -- 2 Massive Data in Science, Technology, Commerce, National Defense, Telecommunications, and Other Endeavors -- 3 Scaling the Infrastructure for Data Management -- 4 Temporal Data and Real-Time Algorithms -- 5 Large-Scale Data Representations -- 6 Resources, Trade-offs, and Limitations -- 7 Building Models from Massive Data -- 8 Sampling and Massive Data -- 9 Human Interaction with Data -- 10 The Seven Computational Giants of Massive Data Analysis -- 11 Conclusions -- Appendixes…”
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Advances in Computational Intelligence : Theory & Applications by Wang, Fei-Yue

Table of Contents: “…Lin and F.-Y. Wang -- 8. Ant colony algorithms: the state-of-the-art / J. …”
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