Exploring genetic algorithm to optimize hyper parameter for training of artificial neural network

Genetic algorithms (GA) are one of the most popular choices for solving optimization problems under uncertainties, imprecision, partial truth inexactness in which the solution space is large. It is inspired by population genetics and natural selection. The goal is to optimize an objective function to its global maxima or minima under a set of imposed constraints. The usefulness of genetic algorithms lies in the fact that it can reduce the state-imposed complexity of such an optimization problem to many fold and thereby improving the search time to find the optimized solution in the solution space. Artificial neural networks and its many variants like CNN, RNN, R-CNN etc. has been instrumental in solving various supervised learning problems. However, it is generally accepted that parameterized training of a neural network is a difficult and time-consuming task due to its inherent hit and trial approach. In this paper we tend to use a genetic algorithm technique to optimize an Artificial Neural Network (ANN). The intended outcome is to optimize the learning rate, optimizer and weights of an ANN so minimize the cost function. We will run this experiment in an artificial neural network to create a prediction model to predict the cooling and heating load in a building.