Genetic Algorithm: Theory, Applications, and Software Implementation: Theory, Applications, and Software Implementation

Author: Wang Xiaoping
Publisher:
Publish Date: 2002-01-01
Features: This book is suitable for researchers in the fields of computational intelligence, automatic control, image processing and pattern recognition, systems engineering, optimization design, and high-performance computing. It can also serve as a reference for graduate students and senior undergraduate students. Genetic algorithms are highly parallel, random, and adaptive search algorithms developed by drawing inspiration from the natural selection and evolution mechanisms of the biological world. Due to their robustness, they are particularly well-suited for handling complex and nonlinear problems that traditional search algorithms struggle to solve. Evolutionary algorithms centered around genetic algorithms have become a hot topic in computational intelligence research, attracting attention from many disciplines. This book comprehensively and systematically introduces the fundamental theories of genetic algorithms, with a focus on their classic applications and recent developments both domestically and internationally. The book is divided into 11 chapters. Chapter 1 provides an overview of the origin and development of genetic algorithms, their basic principles, fundamental operations, and application scenarios. Chapter 2 introduces the basic genetic algorithm. Chapter 3 discusses the mathematical foundation of genetic algorithms. Chapter 4 analyzes various improved methods of genetic algorithms. Chapter 5 gives a preliminary introduction to the theoretical framework of evolutionary computation. Chapter 6 covers the application of genetic algorithms in numerical optimization problems. Chapter 7 introduces the application of genetic algorithms in combinatorial optimization problems. Chapter 8 discusses the application of genetic algorithms in machine learning. Chapter 9 explores the application of genetic algorithms in intelligent control. Chapter 10 addresses issues related to the study of artificial life in conjunction with genetic algorithms. Chapter 11 introduces the application of genetic algorithms in image processing and pattern recognition.