Optimization Algorithms: AI techniques for design, planning, and control problems (True/Retail Copy)

English | 2024 | ISBN: 163343883X | 536 pages | True/Retail EPUB | 47.9 MB

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.

InOptimization Algorithms: AI techniques for design, planning, and control problems you will learn

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.

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 bookOptimization 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

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 wasFrances 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 Appendix A Appendix B Appendix C

---

Download from free file storage

 Download from nitroflare.com  Download from uploadgig.com

---

我要反馈

---

本站不对文件进行储存，仅提供文件链接，请自行下载，本站不对文件内容负责，请自行判断文件是否安全，如发现文件有侵权行为，请联系管理员删除。