HR Analytics for Talent Acquisition: Leveraging Genetic Algorithms and Neural Networks

In a world where things are heading toward a new order of human resource management, the use of sophisticated analytics seems to be a critical strategy that can be used to accelerate talent selection. The research focuses on the employment of genetic algorithms as well as neural networks in human resource (HR) analytics to heighten recruitment efficiency as well as make it more precise or have higher accuracy. Based on a combined quantitative and qualitative strategy, the research develops and verifies a model that involves leveraging AI algorithms, helping to match job candidates to the right positions, and predicting their work-related success indicators. As the resulting study shows, genetic algorithms can cut down the time-to-match and enhance the matching accuracy by organizing the matching process by using optimization methods. Bearing in mind this, neural networks as one of the attractions offer predictive insights into candidate performance which gives a possibility of better and more informed hiring choices. Organizations that adopt this dual approach to talent acquisition will not only tend to attract the best talents but as well achieve their strategic goals more efficiently. This study adds to the overall body of knowledge on human resource analytics by giving examples of the contributions of solving the challenge of talent acquisition through genetic algorithms and neural networks. It emphasizes what analytics with the advanced state provide HR systems with, and which, therefore, is their future. It is through more data-driven, efficient, and effective recruitments that the goal can be attained. The study also elucidates the operational significance for HR professionals, mentions the current study manifestations, and provides suggestions on the way forward for the implementation of HR analytics and technology.