Textbook in PDF format

Solve design, planning, and control problems using modern machine learning and AI techniques.
In Optimization Algorithms: AI techniques for design, planning, and control problems you will learn
Machine learning methods for search and optimization problems
The core concepts of search and optimization
Deterministic and stochastic optimization techniques
Graph search algorithms
Nature-inspired search and optimization algorithms
Efficient trade-offs between search space exploration and exploitation
State-of-the-art Python libraries for search and optimization
Optimization problems are everywhere in daily life. What’s the fastest route from one place to another? How do you calculate the optimal price for a product? How should you plant crops, allocate resources, and schedule surgeries? Optimization Algorithms introduces the AI algorithms that can solve these complex and poorly-structured problems. Inside you’ll find a wide range of optimization methods, from deterministic and stochastic derivative-free optimization to nature-inspired search algorithms and machine learning methods. Don’t worry—there’s no complex mathematical notation. You’ll learn through in-depth case studies that cut through academic complexity to demonstrate how each algorithm works in the real world.
about the technology
Search and optimization algorithms are powerful tools that can help practitioners find optimal or near-optimal solutions to a wide range of design, planning and control problems. When you open a route planning app, call for a rideshare, or schedule a hospital appointment, an AI algorithm works behind the scenes to make sure you get an optimized result. This guide reveals the classical and modern algorithms behind these services.
Optimization Algorithms
Introduction_to_Search_and_Optimization
A Deeper_Look_at_Search_and_Optimization
Blind_Search_Algorithms
Informed_Search_Algorithms
Simulated_Annealing
Tabu_Search
Genetic_Algorithm
Genetic_Algorithm_Variants
Particle_Swarm_Optimization
Other_Swarm_Intelligence_Algorithms_to_Explore
Supervised_and_Unsupervised_Learning
Reinforcement_Learning
Appendix_A._Search_and_Optimization_Libraries_in_Python
Appendix_B._Benchmarks_and_Datasets
Appendix_C._Exercises_and_Solutions