Basic Theory and Applications of Genetic Algorithms

Author: Li Minqiang
Publisher:
Publish Date: 2002-01-01
Features: This book can be used as a graduate textbook for information technology and management science majors, and is also available for reference by relevant scientific research personnel and engineering technicians. The book aims to systematically introduce the theory, application, and development of genetic algorithms, covering a total of 9 chapters. First, the book discusses the origin, history, and main research directions of genetic algorithms, introducing the basic principles of genetic algorithms. Next, it explores the general convergence theory of genetic algorithms, the Markov chain model and convergence analysis of genetic algorithms, and the stochastic functional analysis of genetic algorithms. It also introduces the pattern theory of genetic algorithms, particularly the pattern deception theory of genetic algorithms, as well as experimental analysis of deception problems. Additionally, it provides a detailed discussion on the analysis and design of micro-genetic strategies—genetic operators—as well as parameter setting and adaptive micro-genetic strategy design within micro-genetic strategies. The macro-genetic strategy—genetic algorithm structure analysis and design—is also discussed. Subsequently, the book introduces the application of genetic algorithms in knowledge acquisition, particularly genetic algorithm methods for concept learning and feature extraction. It discusses the principles, methods, and convergence analysis of genetic programming, as well as its applications in typical problems. Finally, the book introduces the development of genetic algorithms—principles and methods of evolutionary computing—providing a general framework and convergence analysis of evolutionary algorithms, discussing the significance of the NFL theorem, and the application of floating-point real-coded genetic algorithms in solving constrained optimization problems. An appendix provides a set of typical performance test functions.