Optimization and decision making Tshilidzi Marwala, Collins Achepsah Leke, University of Johannesburg, South Africa.

Contents
Chapter 1: Introduction
1.1 Introduction
1.2 A Brief Description of This Book
1.3 Conclusion
References
Chapter 2: Classical Optimization
2.1 Introduction
2.2 Optimization
2.3 Nelder–Mead Simplex Method
2.4 Quasi-Newton Broyden–Fletcher–Goldfarb–Shanno (BFGS) Algorithm
2.5 Conjugate Gradient (CG) Method
2.6 Parameter Estimation of a Simple Beam
2.7 Parameter Estimation of an Unsymmetrical H-shaped Structure
2.8 Conclusion
References
Chapter 3: Genetic Algorithm
3.1 Genetic Algorithm
3.1.1 Initialization
3.1.2 Crossover
3.1.3 Mutation
3.1.4 Selection
3.1.5 Termination
3.2 Remanufacturing
3.3 Unsymmetrical H-shaped Structure
3.4 Conclusion
References
Chapter 4: Particle Swarm Optimization
4.1 Introduction
4.2 Particle Swarm Optimization
4.3 Genetic Algorithm Versus PSO
4.4 Operating System Scheduler Tuning
4.5 Unsymmetrical H-shaped Structure
4.6 Missing Data Estimation
4.7 Conclusion
References
Chapter 5: Simulated Annealing
5.1 Introduction
5.2 Simulated Annealing (SA)
5.2.1 Simulated annealing parameters
5.2.2 Transition probabilities
5.2.3 Monte Carlo method
5.2.4 Markov Chain Monte Carlo (MCMC)
5.2.5 Acceptance probability function: Metropolis algorithm
5.2.6 Cooling schedule
5.3 Particle Swarm Optimization Versus SA
5.4 Solving Sudoku
5.5 Unsymmetrical H-shaped Structure
5.6 Conclusion
References
Chapter 6: Response Surface Method
6.1 Introduction
6.2 Response Surface Method (RSM)
6.3 Neural Networks
6.3.1 Multi-layer perceptron (MLP)
6.3.2 Training the multi-layer perceptron
6.3.3 Backpropagation method
6.3.4 Scaled conjugate gradient method
6.4 Evolutionary Optimization
6.5 Estimating Parameters of a Beam
6.6 Estimating Parameters of an Unsymmetrical H-shaped Structure
6.7 Conclusion
References
Chapter 7: Ant Colony Optimization
7.1 Introduction
7.2 Ant Colony Optimization
7.3 Missing Data Estimation Using ACO and Neural Networks
7.4 Condition Monitoring of Bushings
7.4.1 Dissolved gas analysis (DGA) experiment
7.4.2 Data preprocessing
7.5 Conclusion
References
Chapter 8: Bat and Firefly Algorithms
8.1 Introduction
8.2 Bat Algorithm
8.3 Firefly Algorithm
8.4 Deep Learning
8.5 Restricted Boltzmann Machine (RBM)
8.6 Contrastive Divergence (CD)
8.7 Missing Data Estimation Using BA, FA and Neural Networks
8.8 Conclusion
References
Chapter 9: Artificial Immune System
9.1 Introduction
9.2 Artificial Immune System
9.2.1 Innate immunity
9.2.2 Acquired immunity
9.2.3 Self-tolerant trait
9.2.4 Immune system memory
9.3 Artificial Immune System Basic Concepts
9.3.1 Initialization/Encoding
9.3.2 Similarity or affinity measure
9.3.3 Somatic hypermutation
9.4 The Concept of the Negative Selection Algorithm
9.4.1 Implementation of algorithm
9.4.2 Training
9.4.3 Testing
9.5 Missing Data Estimation Using AIS and Neural Networks
9.6 Conclusion
References
Chapter 10: Invasive Weed Optimization and Cuckoo Search Algorithms
10.1 Introduction
10.2 Invasive Weed Optimization
10.3 Cuckoo Search Algorithm
10.4 Deep Autoassociative Neural Network
10.5 Missing Data Estimation Using IWO, CS and Neural Networks
10.6 Conclusion
References
Chapter 11: Decision Trees and Random Forests
11.1 Introduction
11.2 Decision Trees
11.3 Random Forests
11.3.1 Common variables for random forest
11.4 Missing Data Estimation Using Decision Trees and AANN-GA
11.5 Missing Data Estimation Using Random Forests
11.5.1 Missing data estimation using random forests hybrids
11.6 Conclusion
References
Chapter 12: Hybrid Methods
12.1 Introduction
12.2 Finite Element Model Updating
12.3 Nelder–Mead Simplex Method
12.4 Structural Mechanics
12.5 Hybrid PSO and the Nelder–Mead Simplex
12.6 Finite Element Updating
12.7 Conclusion
References
Chapter 13: Economic Modeling
13.1 Introduction
13.2 Economic Concepts
13.3 Pitfalls on Economic Data Modeling
13.4 Data Handling
13.5 Neural Networks
13.6 Support Vector Machine
13.7 Rough Sets
13.8 Autoassociative Networks
13.9 Incremental Learning
13.10 Conclusion
References
Chapter 14: Condition Monitoring
14.1 Introduction
14.2 Condition Monitoring Framework
14.3 Stages of Condition Monitoring
14.4 Data Representation for Condition Monitoring
14.4.1 Time domain
14.4.2 Modal domain
14.4.3 Frequency domain
14.4.4 Time–frequency
14.5 Correlation-Based Condition Monitoring
14.6 Finite Element-Based Condition Monitoring
14.7 Artificial Intelligence Methods
14.7.1 Multi-layer perceptrons
14.7.2 Hidden Markov models (HMMs)
14.7.3 Fuzzy logic
14.7.4 Rough sets
14.7.5 Support vector machines (SVMs)
14.8 Conclusion
References
Chapter 15: Rational Decision-Making
15.1 Introduction
15.2 Can Rationality be Measured?
15.2.1 Information
15.2.2 Model: Biological and physical brain
15.2.3 What about the uncertainty principle?
15.2.4 Optimization
15.2.5 Classification of rationality
15.2.6 Rationality quantification
15.3 Can AI Machines Be Rational?
15.3.1 What did Herbert Simon say?
15.3.2 What happens when we replace humans with machines?
15.3.3 Digital and quantum computing
15.3.4 All models are wrong
15.3.5 Can machines be rational?
15.4 Are Markets Rational?
15.5 Unbounded Rational Decision-Making
15.6 Bounded Rational Decision-Making
15.7 Flexibly Bounded Rational Decision-Making
15.8 Condition Monitoring
15.9 Modeling HIV
15.10 Conclusion
References
Chapter 16: Concluding Remarks
16.1 Summary of the Book
References
Index

"Building on Handbook of Machine Learning - Volume 1: Foundation of Artificial Intelligence, this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like the first volume, it provides a starting point for machine learning enthusiasts as a comprehensive guide on classical optimization methods. It also provides an in-depth overview on how artificial intelligence can be used to define, disprove or validate economic modeling and decision making concepts"--

Includes bibliographical references and index.