Neural networks have become the state-of-the-art algorithm for solving many complex problems in today’s world. However, one of the major challenges of using neural networks is to find the best architecture (i.e. number of hidden layers, number of neurons, etc.), which relies heavily on human experts. Neuroevolution is a machine learning technique where neural network architecture is developed through the use of evolutionary algorithms. It allows us to add/remove a node, update weighted connections, etc., thus creating a robust method for network development. Here the aim is to minimize dependency on human experts by evolving the network topologies in an automated manner.