Evolutionary Optimization on Artificial Neural Networks for Predicting the User's Future Semantic Location

Location prediction has gained enormously in importance in the recent years. For this reason, there exists a great variety of research work carried out at both the academia and the industry. At the same time, there is an increasing trend towards utilizing additional semantic information aiming at building more accurate algorithms. Existing location prediction approaches rely mostly on data-driven models, such as Hidden Markov Chains, Bayes Networks and Artificial Neural Networks (ANN), with the latter achieving usually the best results. Most ANN-based solutions apply Grid Parameter Search and Stochastic Gradient Descent for training their models, that is, for identifying the optimal structure and weights of the network. In this work, motivated by the promising results of genetic algorithms in optimizing neural networks in temporal sequence learning areas, such as the gene and the stock price index prediction, we propose and evaluate their use in optimizing our ANN-based semantic location prediction model. It can be shown that evolutionary algorithms can lead to a significant improvement with respect to its predictive performance, as well as to the time needed for the model's optimization.