Using restricted loops in genetic programming for image classification

Loops are rarely used in genetic programming due to issues such as detecting infinite loops and invalid programs. In this paper we present a restricted form of loops that is specifically designed to be evolved in image classifiers. Particularly, we evolve classifiers that use these loops to perform calculations on image regions chosen by the loops. We have compared this method to another classification method that only uses individual pixels in its calculations. These two methods are tested on two synthesised and one non-synthesised greyscale image classification problems of varying difficulty. The most difficult problem requires determining heads or tails of 320 x 320 pixel images of a US one cent coin at any angle. On this problem, the accuracy of the loops approach was 97.80% in contrast to the no-loop method accuracy of 79.46%. Use of loops also reduces overfitting of training data. We also found that loop methods overfit less when only a few training examples are available.