Includes bibliographical references (pages 492-496) and index.

Part 1. Deterministic search algorithms. Introduction to search and optimization -- A deeper look at search and optimization -- Blind search algorithms -- Informed search algorithms -- Part 2. Trajectory-based algorithms. Simulated annealing -- Tabu search -- Part 3. Evolutionary computing algorithms. Genetic algorithms -- Genetic algorithm variants -- Part 4. Swarm intelligence algorithms. Particle swarm optimization -- Other swarm intelligence algorithms to explore -- Part 5. Machine learning-based methods. Supervised and unsupervised learning -- Reinforcement learning.

Every time you call for a rideshre, order food delivery, book a flight, or schedule a hospital appointement, 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. 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.

School of engineering, Technology &Sciences Physical Science Reference Stacks

law