UID:
almahu_9949983417602882
Umfang:
1 online resource (680 pages)
Ausgabe:
First edition.
ISBN:
9780443161483
,
0443161488
Serie:
Uncertainty, Computational Techniques, and Decision Intelligence Series
Inhalt:
This book is part of the Computational Techniques, and Decision Intelligence series and focuses on advanced computational methods and decision-making processes using artificial intelligence. It covers a range of topics including neural networks, artificial intelligence algorithms, metaheuristic algorithms, optimization problems, and machine learning techniques. The book aims to provide insights into the application of these technologies in fields such as software testing, sustainable supply chain management, and data analysis. Edited by Tofigh Allahviranloo and Witold Pedrycz, it offers a comprehensive overview of both theoretical foundations and practical implementations. The intended audience includes researchers, practitioners, and students in engineering, computer science, and related disciplines.
Anmerkung:
Intro -- Decision-Making Models -- Copyright -- Contents -- Contributors -- Preface -- Section 1: Decision-making: New developments -- Chapter 1: Neural networks -- 1.1. Introduction and motivation -- 1.2. Neural networks overview -- 1.3. Exploring advanced neural network concepts -- 1.4. Neural networks and decision-making -- References -- Chapter 2: Artificial intelligent algorithms, motivation, and terminology -- 2.1. Introduction to artificial intelligence -- 2.2. Types of AI algorithms -- 2.2.1. Supervised learning -- 2.2.2. Unsupervised learning -- 2.2.3. Semisupervised learning -- 2.2.4. Reinforcement learning -- 2.2.5. Ensemble learning -- 2.2.6. Deep learning -- 2.2.7. Neural networks -- 2.2.8. Convolutional neural networks (CNNs) -- 2.2.9. Recurrent neural networks (RNNs) -- 2.2.10. Generative adversarial networks (GANs) -- 2.2.11. Heuristic algorithms -- 2.2.12. Metaheuristic algorithms -- 2.3. Types of problems solved using artificial intelligence algorithms -- 2.4. Evolution of AI algorithms -- References -- Chapter 3: Decision process: A stakeholder-oriented video conferencing software selection in sustainable distance education -- 3.1. Introduction -- 3.2. Literature review -- 3.3. Background -- 3.3.1. Rational decision-making model -- 3.3.2. Bounded rationality decision-making model -- 3.3.3. Intuitive decision-making model -- 3.3.4. Creative decision-making model -- 3.4. Method -- 3.4.1. Preliminaries of picture fuzzy sets (PFSs) -- 3.4.2. MACTOR method -- 3.4.3. Proposed PF-MACTOR method -- 3.5. Analysis and findings -- 3.5.1. Stakeholders of SDE -- 3.5.2. Objectives of SDE -- 3.5.3. Application of PF-MACTOR method and findings -- 3.6. Discussion -- 3.7. Conclusions -- References -- Chapter 4: Learning theory -- 4.1. Introduction to learning theory -- 4.2. Ethical considerations in learning models.
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4.3. Learning theory and decision-making -- 4.4. Future directions in learning theory -- References -- Section 2: Metaheuristic algorithms -- Chapter 5: A comprehensive survey: Nature-inspired algorithms -- 5.1. Nature-inspired algorithms: What are they? -- 5.2. Motivation -- 5.3. Optimization -- 5.4. No-free-lunch -- 5.5. Nature-inspired metaheuristics -- References -- Chapter 6: A comprehensive survey: Physics-based algorithms -- 6.1. Introduction -- 6.2. Physics-based algorithms -- 6.2.1. Gravitational search algorithm -- 6.2.2. Atom search optimization -- 6.2.3. Sonar-inspired optimization -- 6.2.4. Vortex search algorithm -- 6.2.5. Lightning search algorithm -- 6.2.6. RIME: A physics-based optimization -- 6.2.7. Henry gas solubility optimization -- 6.2.8. Multiverse optimizer -- 6.3. Simulation results -- References -- Chapter 7: A comprehensive survey: Evolutionary-based algorithms -- 7.1. Introduction -- 7.2. Evolutionary strategy -- 7.3. Genetic algorithm -- 7.4. Genetic programming -- 7.5. Differential evolution -- 7.6. Discussion -- References -- Chapter 8: A comprehensive survey: Swarm-based algorithms -- 8.1. Introduction -- 8.2. Ant colony optimization -- 8.3. Cat swarm optimization -- 8.4. Elephant herding optimization -- 8.5. Gray wolf optimization -- 8.6. Harris hawks optimization -- 8.7. Marine predators algorithm -- 8.8. Particle swarm optimization -- 8.9. Sand cat swarm optimization -- 8.10. Case study: Kinematics PUMA 560 -- References -- Chapter 9: Single and multi-objective metaheuristic algorithms and their applications in software maintenance -- 9.1. Introduction and motivation -- 9.2. Literature review -- 9.3. Heuristic-based software module clustering -- 9.3.1. Definition of the problem -- 9.3.2. Algorithm structure -- 9.3.3. Objective function -- 9.4. Experiments and results -- 9.4.1. Experiments platform -- 9.4.2. Results.
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9.5. Conclusion -- References -- Chapter 10: Constraint-based heuristic algorithms for software test generation -- 10.1. Introduction and motivation -- 10.2. Literature review -- 10.3. Heuristic-based test data generation -- 10.3.1. Generating test data -- 10.3.2. Fitness function -- 10.4. Results and discussion -- 10.4.1. Implementation -- 10.4.2. Benchmarks -- 10.4.3. Evaluation -- 10.5. Conclusion -- References -- Chapter 11: Discretized optimization algorithms for finding the bug-prone locations of a program source code -- 11.1. Introduction and motivation -- 11.2. Literature review -- 11.3. Identifying the bug-prone paths of the programs -- 11.4. Experiments and results -- 11.5. Conclusion -- References -- Section 3: Optimization problems -- Chapter 12: Mathematical programming -- 12.1. Introduction -- 12.2. Types of mathematical programming -- 12.3. Basic concepts -- 12.4. Simplex algorithm -- 12.5. Nonlinear programming -- 12.5.1. Unconstrained problems -- 12.6. Constrained problems with equality constraints -- 12.7. Lagrange multiplier method -- 12.8. Unconstrained problem with inequality constraints -- 12.9. Double search -- 12.10. Interval bisection method -- 12.11. Conclusion -- Acknowledgment -- References -- Chapter 13: Discrete and combinatorial optimization -- 13.1. Introduction -- 13.1.1. Various optimization problems are divided into the following two categories -- 13.2. Examining search and optimization methods -- 13.2.1. Enumerative methods -- 13.2.2. Calculation methods (mathematical search or-based method calculus) -- 13.2.3. Innovative and meta-innovative methods (random search) -- 13.2.4. Combinational optimization problems -- 13.2.5. The method of solving combined optimization problems -- 13.2.5.1. Relaxation -- 13.2.5.2. Analysis -- 13.2.5.3. Column generation method -- 13.2.5.4. Constructive search.
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13.2.5.5. Improving search -- 13.2.6. Neighborhood search method -- 13.2.7. Metaheuristic methods derived from nature -- 13.2.8. Traveling salesman problem (TSP) -- 13.3. Integer programming -- 13.3.1. Solution methods -- 13.3.1.1. Cutting plane method -- 13.3.1.2. Mixed algorithm -- 13.4. Branch-and-bound method -- 13.5. Additive algorithm for pure binary problem -- 13.5.1. Branch and bound zero-one tree -- 13.6. The Transportation problem -- 13.6.1. Fogel's approximation method -- 13.7. Find the optimum solution of transportation problem -- 13.8. Conclusion -- Acknowledgment -- References -- Chapter 14: Data optimization and analysis -- 14.1. Introduction -- 14.2. Data envelopment analysis -- 14.3. Network data envelopment analysis -- 14.4. Progress and regress -- 14.5. Ranking -- 14.6. Data analysis and support vector machines -- 14.6.1. Separable state and data clustering -- 14.6.2. Nonseparable state -- 14.6.3. Nonlinear SVM -- 14.6.4. Separable SVMs in multiple categories -- 14.6.5. SVM applications -- 14.7. Conclusion -- Acknowledgment -- References -- Chapter 15: Applied optimization problems -- 15.1. Introduction -- 15.2. Linear Programming -- 15.3. Integer programing -- 15.4. Nonlinear programming -- 15.5. Network programing -- 15.6. Inventory -- 15.7. Calculus of variations -- 15.8. Risk measurement -- 15.8.1. Standard risk measures, value-at-risk (VaR) -- 15.9. Mean-variance analysis -- 15.9.1. Diversification effect -- 15.10. Multiperiod binomial model -- 15.11. Queuing theory optimization -- 15.12. Supply chain concept and its applications -- 15.12.1. Applications of data envelopment analysis (DEA) in supply chain -- 15.13. Multiobjective optimization is an optimization -- 15.13.1. Applications of multiobjective optimization -- 15.13.2. Applications of optimization in reliability models.
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15.13.3. Classic reliability optimization models -- 15.13.4. Presenting a series-parallel redundancy allocation problem (RAP) -- 15.13.5. Reliability optimization of a k-out-of-n series-parallel system -- 15.14. Conclusion -- Acknowledgment -- References -- Chapter 16: Engineering optimization -- 16.1. Introduction -- 16.2. Types of optimization problems -- 16.2.1. ``Continuous´´ and ``discrete´´ optimization problems -- 16.2.2. ``Constrained´´ and ``unconstrained´´ optimization problems -- 16.2.3. ``Deterministic´´ and ``stochastic´´ optimization problems -- 16.2.4. Nonobjective, single-objective, and multiobjective optimization problems -- 16.2.5. Heuristic and meta-heuristic methods (random search) -- 16.3. Engineering optimization -- 16.3.1. Types of selected engineering optimization methods -- 16.3.2. Deterministic and probabilistic optimization algorithms -- 16.3.3. Direct and indirect optimization algorithms -- 16.3.4. Heuristic and metaheuristic optimization algorithm -- 16.3.4.1. Genetic algorithm -- 16.3.4.2. Simulated annealing -- 16.3.4.3. Tabu search -- 16.3.4.4. Particle swarm optimization algorithm -- 16.3.5. Comparison and selection of the appropriate optimization method -- 16.3.6. Advantages of the genetic algorithm compared to other optimization methods in engineering -- 16.4. Conclusion -- References -- Section 4: Machine learning -- Chapter 17: Deep learning -- 17.1. Introduction and motivation -- 17.2. Background -- 17.3. Literature review -- 17.4. Minatar -- 17.5. Results and discussions -- 17.6. Conclusion -- References -- Chapter 18: (Artificial) neural networks -- 18.1. Introduction and motivation -- 18.2. Literature review -- 18.3. Artificial neural networks -- 18.4. Dataset -- 18.5. Experiments and results -- 18.6. Conclusion -- References -- Chapter 19: Reinforcement learning algorithms -- 19.1. Introduction and motivation.
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19.2. Literature review.
Weitere Ausg.:
ISBN 9780443161476
Weitere Ausg.:
ISBN 044316147X
Sprache:
Englisch
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