Journal of Artificial Intelligence, Machine Learning and Soft Computing

Evolutionary Computing (EC) is a subfield of artificial intelligence inspired by natural evolutionary processes. Among the various techniques under EC, Genetic Algorithms (GAs) have emerged as a robust optimization and search paradigm. This review provides an in-depth examination of evolutionary computing, with a focus on genetic algorithms, their theoretical foundations, algorithmic structures, and diverse applications across engineering, computer science, and industry. The paper also discusses hybrid approaches, recent advancements, limitations, and future research directions. By synthesizing findings from multiple studies, this review highlights the versatility, adaptability, and performance of genetic algorithms in solving complex, real-world problems.

KEYWORDS: Evolutionary Computing, Genetic Algorithms, Optimization, Selection, Crossover, Mutation, Heuristic Search, Hybrid Algorithms