Tackling Overfitting in Evolutionary-Driven Financial Model Induction

This chapter explores the issue of overfitting in grammar-based Genetic Programming. Tools such as Genetic Programming are well suited to problems in finance where we seek to learn or induce a model from the data. Models that overfit the data upon which they are trained prevent model generalisation, which is an important goal of learning algorithms. Early stopping is a technique that is frequently used to counteract overfitting, but this technique often fails to identify the optimal point at which to stop training. In this chapter, we implement four classes of stopping criteria, which attempt to stop training when the generalisation of the evolved model is maximised. In this way, we hope to increase the generalisation of trading rules in buy/sell prediction problems. We show promising results using, in particular, one novel class of criteria, which measured the correlation between the training and validation fitness at each generation. These criteria determined whether or not to stop training depending on the measurement of this correlation they had a high probability of being the best among a suite of potential criteria to be used during a run. This meant that they often found the lowest validation set error for the entire run, and did so faster than other criteria.