This paper provides a comprehensive review of recent advances in genetic algorithms (GAs), focusing on their theoretical foundations, implementation, and applications. The review aims to offer a broad vision for new researchers by discussing well-known algorithms, their pros and cons, and the usage of genetic operators. The paper covers various research domains involving GAs and discusses future research directions in genetic operators, fitness functions, and hybrid algorithms. The main contributions of the paper include elaborating the general framework of GAs and hybrid GAs, discussing different types of genetic operators, presenting variants of GAs with their advantages and disadvantages, and exploring the applicability of GAs in multimedia fields. The paper is structured into several sections, including methodology, background, variants of GAs, applications, and future research directions. The methodology section outlines the research process, while the background section delves into the classical GA and its genetic operators. The variants of GAs section covers real and binary coded GAs, multiobjective GAs, parallel GAs, chaotic GAs, and hybrid GAs. The applications section discusses the successful applications of GAs in operation management and multimedia fields. The paper concludes with a discussion on the challenges and future research directions in the area of GAs.This paper provides a comprehensive review of recent advances in genetic algorithms (GAs), focusing on their theoretical foundations, implementation, and applications. The review aims to offer a broad vision for new researchers by discussing well-known algorithms, their pros and cons, and the usage of genetic operators. The paper covers various research domains involving GAs and discusses future research directions in genetic operators, fitness functions, and hybrid algorithms. The main contributions of the paper include elaborating the general framework of GAs and hybrid GAs, discussing different types of genetic operators, presenting variants of GAs with their advantages and disadvantages, and exploring the applicability of GAs in multimedia fields. The paper is structured into several sections, including methodology, background, variants of GAs, applications, and future research directions. The methodology section outlines the research process, while the background section delves into the classical GA and its genetic operators. The variants of GAs section covers real and binary coded GAs, multiobjective GAs, parallel GAs, chaotic GAs, and hybrid GAs. The applications section discusses the successful applications of GAs in operation management and multimedia fields. The paper concludes with a discussion on the challenges and future research directions in the area of GAs.