<![CDATA[The Genetic Algorithm (GA) is a technique that uses the selection principle of genetics to optimize the search tool for challenging issues. It is used for research and development as well as machine learning in addition to optimization, the purpose of this literature review is to determine the current state of research on the use and applications of genetic algorithms (GAs) for optimization across a range of sectors. Natural selection and biological evolution serve as the foundation for genetic algorithms (GAs), which replicate solutions through crossover, mutation, and selection. The review accentuates the diversity and universality of GAs in solving numerous complex problems such as path finding, image analytics and data referral systems. It examines the effectiveness of GAs in solving optimization problems as compared to other methods and focuses on GAs efficient properties in searching large and chaotic solution spaces. The results indicate that GAs can be considered as a strong result-oriented tool to further improve the machine learning and artificial intelligence operability.]]>
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