Comparison of Genetic and Hill Climbing Algorithms to Improve an Artificial Neural Networks Model for Water Consumption Prediction

No unique method has been so far specified for determining the number of neurons in hidden layers of Multi-Layer Perceptron (MLP) neural networks used for prediction. The present research is intended to optimize the number of neurons using two meta-heuristic procedures namely genetic and hill climbing algorithms. The data used in the present research for prediction are consumption data of water subscribers in Fasa City of Fars Province (Iran) between the years 2010 to 2013. Ultimately, using the respective data set, the data of the subsequent year 2014 can be predicted. In the present research it was observed that the mean square errors of per data (MSEPD) for the abovementioned algorithms are less than 0.2, indicating a high performance in the neural networks’ prediction. Correlation coefficients using genetic and hill climbing algorithms were respectively equal to 0.891 and 0.759. Thus, GA was able to leave a better effect on optimization of neural network