Description
Introduction to Evolutionary Algorithms is a comprehensive and structured guide to the field of evolutionary computing and nature-inspired optimization algorithms. Xinjie Yu and co-authors have attempted to explain the complex concepts of intelligent optimization in a simple and understandable way for engineers and students of various disciplines. The book begins with a review of the basic principles of biological evolution and then shows how these natural mechanisms can be translated into programming languages and mathematical codes to solve complex engineering problems.
Throughout the chapters, the author examines in detail a variety of evolutionary algorithms, including genetic algorithms, evolutionary programming, evolutionary strategies, and particle swarm optimization. The book’s main focus is on practicality, so that after understanding the theory of each method, readers will become familiar with how to implement it in practice and adjust various parameters to achieve the best response. As a standard textbook, this work provides a strong foundation for entering the world of intelligent optimization and collective artificial intelligence.
Book Features
- Step-by-step and fluent teaching of evolutionary optimization concepts without the need for a very complex mathematical background.
- Extensive coverage of a variety of optimization methods including genetic algorithms (GA) and ant colony optimization.
- Providing practical, real-world examples from the engineering world to better understand how algorithms work in practice.
- Investigating convergence analysis methods and evaluating the efficiency of various algorithms in dealing with difficult problems.
- An educational structure suitable for teaching at master’s and doctoral levels in engineering and computer science.
Book specifications
- Publisher: Springer
- Instructor/Author: Xinjie Yu
- Number of pages: 427
- Number of chapters: 10
- Format: PDF
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