The book "Introduction to Genetic Algorithms" by S.N. Sivanandam and S.N. Deepa provides a comprehensive overview of genetic algorithms (GAs) and their applications. The authors, both from PSG College of Technology in India, aim to introduce the basic concepts of GAs to a wide audience, including programmers, researchers, and management experts. The book covers the historical development of evolutionary computing, the features of evolutionary algorithms, and various operators and techniques used in GAs. It also discusses advanced topics such as genetic programming, optimization problems, and implementations using MATLAB, C, and C++. The book is structured into 11 chapters, each focusing on different aspects of GAs, from theoretical foundations to practical applications. It includes numerous examples, solved problems, and case studies to illustrate the concepts and techniques discussed. The authors highlight the advantages of GAs, such as conceptual simplicity, broad applicability, and robustness to dynamic changes, making the book a valuable resource for both students and professionals in the field of optimization.The book "Introduction to Genetic Algorithms" by S.N. Sivanandam and S.N. Deepa provides a comprehensive overview of genetic algorithms (GAs) and their applications. The authors, both from PSG College of Technology in India, aim to introduce the basic concepts of GAs to a wide audience, including programmers, researchers, and management experts. The book covers the historical development of evolutionary computing, the features of evolutionary algorithms, and various operators and techniques used in GAs. It also discusses advanced topics such as genetic programming, optimization problems, and implementations using MATLAB, C, and C++. The book is structured into 11 chapters, each focusing on different aspects of GAs, from theoretical foundations to practical applications. It includes numerous examples, solved problems, and case studies to illustrate the concepts and techniques discussed. The authors highlight the advantages of GAs, such as conceptual simplicity, broad applicability, and robustness to dynamic changes, making the book a valuable resource for both students and professionals in the field of optimization.