Neuroevolution of Augmenting Topologies for Talent Acquisition Optimization in Large Enterprises

Abstract

The paper delves into the benefits in implementation of NeuroEvolution of Augmenting Topologies (NEAT) for effective talent management and its contribution to the recruitment process. Using a standard mechanism capable of representing deep neural networks of any topology, NEAT will be used for prediction and optimization of strategies for recruitment. Furthermore, it is difficult to recruit fresh employees in case of large scale organizations. The significance of recruitment is that it improves the recruitment process of an organisation which will lead to increased productivity of the human resource. Additionally, aligning job classification with appropriate recruitment is very important as it rarely affects the recruiting of predicted personal. The research will focus on recruitment optimization to help big scale firms in achieving the best level of selection planning by neuro-evolutionary artificial intelligence system.  It tries to put in place a top hiring forecast for the growth of the future.