This paper examines genetic programming as a machine learning technique in the context of object detection. Object detection is performed on image features and on gray-scale images themselves, with different goals. The generality of the solutions discovered, over the training set and over a wider range of images, is tested in both cases. Using genetic programming as a means of testing the utility of algorithms is also explored. Two programs generated using different features are hierarchically combined, improving the results to 1.4% false negatives on an untrained image, while saving processing.