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Synopsis
Solve design, planning, and control problems using modern AI techniques.
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.
In Optimization Algorithms: AI techniques for design, planning, and control problems you will learn:
• The core concepts of search and optimization
• Deterministic and stochastic optimization techniques
• Graph search algorithms
• Trajectory-based optimization algorithms
• Evolutionary computing algorithms
• Swarm intelligence algorithms
• Machine learning methods for search and optimization problems
• Efficient trade-offs between search space exploration and exploitation
• State-of-the-art Python libraries for search and optimization
Inside this comprehensive guide, you’ll find a wide range of optimization methods, from deterministic search algorithms to stochastic derivative-free metaheuristic 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. Plus, get hands-on experience with practical exercises to optimize and scale the performance of each algorithm.
Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications.
About the technology
Every time you call for a rideshare, order food delivery, book a flight, or schedule a hospital appointment, an algorithm works behind the scenes to find the optimal result. Blending modern AI methods with classical search and optimization techniques can deliver incredible results, especially for the messy problems you encounter in the real world. This book shows you how.
About the book
Optimization Algorithms explains in clear language how optimization algorithms work and what you can do with them. This engaging book goes beyond toy examples, presenting detailed scenarios that use actual industry data and cutting-edge AI techniques. You will learn how to apply modern optimization algorithms to real-world problems like pricing products, matching supply with demand, balancing assembly lines, tuning parameters, coordinating mobile networks, and cracking smart mobility challenges.
What's inside
• Graph search algorithms
• Metaheuristic algorithms
• Machine learning methods
• State-of-the-art Python libraries for optimization
• Efficient trade-offs between search space exploration and exploitation
About the reader
Requires intermediate Python and machine learning skills.
About the author
Dr. Alaa Khamis is an AI and smart mobility technical leader at General Motors and a lecturer at the University of Toronto.
The technical editor on this book was Frances Buontempo.
Table of Contents
PART 1
1 Introduction to search and optimization
2 A deeper look at search and optimization
3 Blind search algorithms
4 Informed search algorithms
PART 2
5 Simulated annealing
6 Tabu search
PART 3
7 Genetic algorithms
8 Genetic algorithm variants
PART 4
9 Particle swarm optimization
10 Other swarm intelligence algorithms to explore
PART 5
11 Supervised and unsupervised learning
12 Reinforcement learning
included with the eBook only:
Appendix A
Appendix B
Appendix C
"synopsis" may belong to another edition of this title.
About the Author
Dr. Alaa Khamis is an AI and smart mobility technical leader at General Motors and a sessional lecturer at the University of Toronto. He is also an adjunct professor at Ontario Tech University and Nile University, affiliate member of the Center of Pattern Analysis and Machine Intelligence (CPAMI) at the University of Waterloo, and a former professor of artificial intelligence and robotics.
From the Back Cover
From the Back Cover:
Optimization Algorithms: AI techniques for design, planning, and control problems explores the AI algorithms that determine the most efficient routes, optimal designs, and solve other logistical issues. Dive into the exciting world of classical problems like the Travelling Salesman Problem and the Knapsack Problem, as well as cutting-edge modern implementations like graph search methods, metaheuristics and machine learning. Discover how to use these algorithms in real-world situations, with in-depth case studies on assembly line balancing, fitness planning, rideshare dispatching, routing and more. Plus, get hands-on experience with practical exercises to optimize and scale the performance of each algorithm.
About the reader:
For AI practitioners familiar with the Python language.
"About this title" may belong to another edition of this title.
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Hardback. Condition: New. 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 problemsThe core concepts of search and optimizationDeterministic and stochastic optimization techniquesGraph search algorithmsNature-inspired search and optimization algorithmsEfficient trade-offs between search space exploration and exploitationState-of-the-art Python libraries for search and optimization 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. Seller Inventory # LU-9781633438835
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Hardcover. Condition: new. Hardcover. 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 problemsThe core concepts of search and optimizationDeterministic and stochastic optimization techniquesGraph search algorithmsNature-inspired search and optimization algorithmsEfficient trade-offs between search space exploration and exploitationState-of-the-art Python libraries for search and optimization 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. Solve design, planning, and control problems using modern machine learning and AI techniques. For AI practitioners familiar with the Python language. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781633438835
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