This paper reviews the development, current state, and future directions of genetic algorithms (GAs). It discusses the key elements of GAs, including chromosome representation, fitness selection, and biological-inspired operators such as selection, mutation, and crossover. The paper also covers various types of genetic operators, their pros and cons, and different variants of GAs. It discusses the applications of GAs in various fields, including multimedia, and highlights the challenges and future research directions in the area of genetic operators, fitness functions, and hybrid algorithms. The paper also presents a structured review of the research methodology used to analyze the literature on GAs, including the selection of relevant research papers based on specific criteria. The review emphasizes the importance of GAs in solving complex real-world problems and their potential for future development in various domains. The paper concludes with a discussion on the main contributions of the research, including the elaboration of the general framework of GAs and hybrid GAs, the discussion of various types of genetic operators, and the discussion of the applicability of GAs in multimedia fields. The paper also highlights the challenges and future research directions in the area of genetic algorithms, including the need for better control parameters, improved search capabilities, and the integration of GAs with other optimization methods. The review provides a comprehensive overview of the current state of genetic algorithms and their potential for future development.This paper reviews the development, current state, and future directions of genetic algorithms (GAs). It discusses the key elements of GAs, including chromosome representation, fitness selection, and biological-inspired operators such as selection, mutation, and crossover. The paper also covers various types of genetic operators, their pros and cons, and different variants of GAs. It discusses the applications of GAs in various fields, including multimedia, and highlights the challenges and future research directions in the area of genetic operators, fitness functions, and hybrid algorithms. The paper also presents a structured review of the research methodology used to analyze the literature on GAs, including the selection of relevant research papers based on specific criteria. The review emphasizes the importance of GAs in solving complex real-world problems and their potential for future development in various domains. The paper concludes with a discussion on the main contributions of the research, including the elaboration of the general framework of GAs and hybrid GAs, the discussion of various types of genetic operators, and the discussion of the applicability of GAs in multimedia fields. The paper also highlights the challenges and future research directions in the area of genetic algorithms, including the need for better control parameters, improved search capabilities, and the integration of GAs with other optimization methods. The review provides a comprehensive overview of the current state of genetic algorithms and their potential for future development.