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Optimization Algorithms (MEAP v11)
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Название: Optimization Algorithms: AI techniques for design, planning, and control problems (MEAP v11)
Автор: Alaa Khamis
Издательство: Manning Publications
Год: 2023
Страниц: 741
Язык: английский
Формат: pdf
Размер: 38.6 MB

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

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. Inside you’ll find a wide range of optimization methods, from deterministic and stochastic derivative-free optimization to nature-inspired search 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.

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.

Without assuming any prior knowledge of search and optimization and with an intermediate knowledge of data structures and Python, this book has been written to take most anyone from never solving search and optimization problems to being a well-rounded search and optimization practitioner able to select, implement and adapt the right solver for the right problem. For managers or professionals involved in the high-level technological decisions at their workplace, these skills can be critical in understanding software-based approaches, their opportunities, and limitations when discussing process improvement. In contrast, IT professionals will find these skills more directly applicable when considering options for developing or selecting new software suites and technologies for in-house use. The following subsection describes the methodology we will follow throughout this book.

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