Optimization Algorithms

Solve design, planning, and control problems using modern machine learning and AI techniques. For AI practitioners familiar with the Python language.

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.

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

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.