Optimization Algorithms: Ai Techniques For Design, Planning, And Control Problems,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 problems The core concepts of search and optimization Deterministic and stochastic optimization techniques Graph search algorithms Natureinspired search and optimization algorithms Efficient tradeoffs between search space exploration and exploitation Stateoftheart Python libraries for search and optimizationOptimization problems are everywhere in daily life. Whats 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 poorlystructured problems. Inside youll find a wide range of optimization methods, from deterministic and stochastic derivativefree optimization to natureinspired search algorithms and machine learning methods. Dont worrytheres no complex mathematical notation. Youll learn through indepth case studies that cut through academic complexity to demonstrate how each algorithm works in the real world.About the technologySearch and optimization algorithms are powerful tools that can help practitioners find optimal or nearoptimal 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.About the bookOptimization 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 cuttingedge modern implementations like graph search methods, metaheuristics and machine learning. Discover how to use these algorithms in realworld situations, with indepth case studies on assembly line balancing, fitness planning, rideshare dispatching, routing and more. Plus, get handson experience with practical exercises to optimize and scale the performance of each algorithm.About the readerFor AI practitioners familiar with the Python language.About the authorDr. 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.