The Effectiveness of Cost Based Subtree Caching Mechanisms in Typed Genetic Programming for Image Segmentation

Genetic programming has long been known as a computationally expensive optimisation technique. When evolving imaging operations, the processing time increases dramatically. This work describes a system using a caching mechanism which reduces the number of evaluations needed by up to 66 percent, counteracting the effects of increasing tree size. This results in a decrease in elapsed time of up to 52 percent. A cost threshold is introduced which can guarantee a speed increase. This caching technique allows genetic programming to be feasibly applied to problems in computer vision and image processing. The tradeoffs involved in caching are analysed, and the use of the technique on a previously time consuming medical segmentation problem is shown.