فهرست مطالب

• Part 1. Deterministic search algorithms
• Chapter 1: Introduction to search and optimization
• 1.1 Why care about search and optimization?
• 1.2 Going from toy problems to the real world
• 1.3 Basic ingredients of optimization problems
• 1.3.1 Decision variables
• 1.3.2 Objective functions
• 1.3.3 Constraints
• 1.4 Well-structured problems vs. ill-structured problems
• 1.4.1 Well-structured problems
• 1.4.2 Ill-structured problems
• 1.4.3 WSP, but ISP in practice
• 1.5 Search algorithms and the search dilemma
• Summary
• Chapter 2: A deeper look at search and optimization
• 2.1 Classifying optimization problems
• 2.1.1 Number and type of decision variables
• 2.1.2 Landscape and number of objective functions
• 2.1.3 Constraints
• 2.1.4 Linearity of objective functions and constraints
• 2.1.5 Expected quality and permissible time for the solution
• 2.2 Classifying search and optimization algorithms
• 2.3 Heuristics and metaheuristics
• 2.4 Nature-inspired algorithms
• Summary
• Chapter 3: Blind search algorithms
• 3.1 Introduction to graphs
• 3.2 Graph search
• 3.3 Graph traversal algorithms
• 3.3.1 Breadth-first search
• 3.3.2 Depth-first search
• 3.4 Shortest path algorithms
• 3.4.1 Dijkstra’s search
• 3.4.2 Uniform-cost search (UCS)
• 3.4.3 Bidirectional Dijkstra's search
• 3.5 Applying blind search to the routing problem
• Summary
• Chapter 4: Informed search algorithms
• 4.1 Introducing informed search
• 4.2 Minimum spanning tree algorithms
• 4.3 Shortest path algorithms
• 4.3.1 Hill climbing algorithm
• 4.3.2 Beam search algorithm
• 4.3.3 A* search algorithm
• 4.3.4 Hierarchical approaches
• 4.4 Applying informed search to a routing problem
• 4.4.1 Hill climbing for routing
• 4.4.2 Beam search for routing
• 4.4.3 A* for routing
• 4.4.4 Contraction hierarchies for routing
• Summary
• Part 2. Trajectory-based algorithms
• Chapter 5: Simulated annealing
• 5.1 Introducing trajectory-based optimization
• 5.2 The simulated annealing algorithm
• 5.2.1 Physical annealing
• 5.2.2 SA pseudocode
• 5.2.3 Acceptance probability
• 5.2.4 The annealing process
• 5.2.5 Adaptation in SA
• 5.3 Function optimization
• 5.4 Solving Sudoku
• 5.5 Solving TSP
• 5.6 Solving a delivery semi-truck routing problem
• Summary
• Chapter 6: Tabu search
• 6.1 Local search
• 6.2 Tabu search algorithm
• 6.2.1 Memory structure
• 6.2.2 Aspiration criteria
• 6.2.3 Adaptation in TS
• 6.3 Solving constraint satisfaction problems
• 6.4 Solving continuous problems
• 6.5 Solving TSP and routing problems
• 6.6 Assembly line balancing problem
• Summary
• Part 3. Evolutionary computing algorithms
• Chapter 7: Genetic algorithms
• 7.1 Population-based metaheuristic algorithms
• 7.2 Introducing evolutionary computation
• 7.2.1 A brief recap of biology fundamentals
• 7.2.2 The theory of evolution
• 7.2.3 Evolutionary computation
• 7.3 Genetic algorithm building blocks
• 7.3.1 Fitness function
• 7.3.2 Representation schemes
• 7.3.3 Selection operators
• 7.3.4 Reproduction operators
• 7.3.5 Survivor selection
• 7.4 Implementing genetic algorithms in Python
• Summary
• Chapter 8: Genetic algorithm variants
• 8.1 Gray-coded GA
• 8.2 Real-valued GA
• 8.2.1 Crossover methods
• 8.2.2 Mutation methods
• 8.3 Permutation-based GA
• 8.3.1 Crossover methods
• 8.3.2 Mutation methods
• 8.4 Multi-objective optimization
• 8.5 Adaptive GA
• 8.6 Solving the traveling salesman problem
• 8.7 PID tuning problem
• 8.8 Political districting problem
• Summary
• Part 4. Swarm intelligence algorithms
• Chapter 9: Particle swarm optimization
• 9.1 Introducing swarm intelligence
• 9.2 Continuous PSO
• 9.2.1 Motion equations
• 9.2.2 Fitness update
• 9.2.3 Initialization
• 9.2.4 Neighborhoods
• 9.3 Binary PSO
• 9.4 Permutation-based PSO
• 9.5 Adaptive PSO
• 9.5.1 Inertia weight
• 9.5.2 Cognitive and social components
• 9.6 Solving the traveling salesman problem
• 9.7 Neural network training using PSO
• Summary
• Chapter 10: Other swarm intelligence algorithms to explore
• 10.1 Nature’s tiny problem-solvers
• 10.2 ACO metaheuristics
• 10.3 ACO variants
• 10.3.1 Simple ACO
• 10.3.2 Ant system
• 10.3.3 Ant colony system
• 10.3.4 Max–min ant system
• 10.3.5 Solving open TSP with ACO
• 10.4 From hive to optimization
• 10.5 Exploring the artificial bee colony algorithm
• Summary
• Part 5. Machine learning-based methods
• Chapter 11: Supervised and unsupervised learning
• 11.1 A day in the life of AI-empowered daily routines
• 11.2 Demystifying machine learning
• 11.3 Machine learning with graphs
• 11.3.1 Graph embedding
• 11.3.2 Attention mechanisms
• 11.3.3 Pointer networks
• 11.4 Self-organizing maps
• 11.5 Machine learning for optimization problems
• 11.6 Solving function optimization using supervised machine learning
• 11.7 Solving TSP using supervised graph machine learning
• 11.8 Solving TSP using unsupervised machine learning
• 11.9 Finding a convex hull
• Summary
• Chapter 12: Reinforcement learning
• 12.1 Demystifying reinforcement learning
• 12.1.1 Markov decision process (MDP)
• 12.1.2 From MDP to reinforcement learning
• 12.1.3 Model-based vs. model-free RL
• 12.1.4 Actor-critic methods
• 12.1.5 Proximal policy optimization
• 12.1.6 Multi-armed bandit (MAB)
• 12.2 Optimization with reinforcement learning
• 12.3 Balancing CartPole using A2C and PPO
• 12.4 Autonomous coordination in mobile networks using PPO
• 12.5 Solving the truck selection problem using contextual bandits
• 12.6 Journey’s end: A final reflection
• Summary
• Appendix A. Search and optimization libraries in Python
• A.1 Setting up the Python environment
• A.1.1 Using a Python distribution
• A.1.2 Installing Jupyter Notebook and JupyterLab
• A.1.3 Cloning the book’s repository
• A.2 Mathematical programming solvers
• A.2.1 SciPy
• A.2.2 PuLP
• A.2.3 Other mathematical programming solvers
• A.3 Graph and mapping libraries
• A.3.1 NetworkX
• A.3.2 OSMnx
• A.3.3 GeoPandas
• A.3.4 contextily
• A.3.5 Folium
• A.3.6 Other libraries and tools
• A.4 Metaheuristics optimization libraries
• A.4.1 PySwarms
• A.4.2 Scikit-opt
• A.4.3 NetworkX
• A.4.4 Distributed evolutionary algorithms in Python (DEAP)
• A.4.5 OR-Tools
• A.4.6 Other libraries
• A.5 Machine learning libraries
• A.5.1 node2vec
• A.5.2 DeepWalk
• A.5.3 PyG
• A.5.4 OpenAI Gym
• A.5.5 Flow
• A.5.6 Other libraries
• A.6 Projects
• Appendix B. Benchmarks and datasets
• B.1 Optimization test functions
• B.2 Combinatorial optimization benchmark datasets
• B.3 Geospatial datasets
• B.4 Machine learning datasets
• B.5 Data folder
• Appendix C. Exercises and solutions
• C.1 Chapter 2: A deeper look at search and optimization
• C.1.1 Exercises
• C.1.2 Solutions
• C.2 Chapter 3: Blind search algorithms
• C.2.1 Exercises
• C.2.2 Solutions
• C.3 Chapter 4: Informed search algorithms
• C.3.1 Exercises
• C.3.2 Solutions
• C.4 Chapter 5: Simulated annealing
• C.4.1 Exercises
• C.4.2 Solutions
• C.5 Chapter 6: Tabu search
• C.5.1 Exercises
• C.5.2 Solutions
• C.6 Chapter 7: Genetic algorithm
• C.6.1 Exercises
• C.6.2 Solutions
• C.7 Chapter 8: Genetic algorithm variants
• C.7.1 Exercises
• C.7.2 Solutions
• C.8 Chapter 9: Particle swarm optimization
• C.8.1 Exercises
• C.8.2 Solutions
• C.9 Chapter 10: Other swarm intelligence algorithms to explore
• C.9.1 Exercises
• C.9.2 Solutions
• C.10 Chapter 11: Supervised and unsupervised learning
• C.10.1 Exercises
• C.10.2 Solutions
• C.11 Chapter 12: Reinforcement learning
• C.11.1 Exercises
• C.11.2 Solutions
• References
• Index